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Data Acquiring System for Gas Turbine Engine's Dynamic Performance; Build and Validate

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Title: Data Acquiring System for Gas Turbine Engine's Dynamic Performance; Build and Validate
Language: English
Authors: Mostafa M. Samy, Mohamed A. Metwally, Mahmoud Ashry, Wael M. Elmayyah (ORCID 0000-0003-3302-0342)
Source: Measurement: Interdisciplinary Research and Perspectives. 2025 23(1):39-55.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 17
Publication Date: 2025
Document Type: Journal Articles
Reports - Evaluative
Descriptors: Engines, Power Technology, Data Collection, Data Interpretation, Evaluation Methods, Test Construction, Test Validity, Measurement Equipment, Measurement Objectives, Information Systems, Data Processing, Computer Assisted Testing
DOI: 10.1080/15366367.2023.2283682
ISSN: 1536-6367
1536-6359
Abstract: Gas Turbine Engines (GTE) have the highest power-to-weight ratio among Internal Combustion Engines (ICE). Its modularity and ability to utilize various types of fuel make it highly recommended in power plants, naval transportation, and, of course, the most equipped in aviation. The lack of GTEs' real data is increasing a recognized need for collecting informative real data sets from GTEs for modeling, monitoring, and fault diagnosis and isolation (FDI). In the present article, a robust versatile data acquisition measuring system has been built to collect real-time data from an axial turboshaft GTE. The system facilitates connectivity and integrity by means of two wiring harnesses connecting the data acquisition card (DAQ), the engine's control unit, and the operator's control console. Mobility is guaranteed using a wireless connection between DAQ and a customized programmable LabVIEW interface application. Many experiments have been conducted in different operating conditions to collect the engine and controller's data in different regimes. The measured signals have been filtered using software-implemented filters and transformed into the right measurement quantities. The experiment data have been analyzed for different regimes, and the milestone documented readings were compared with the measured ones to validate the accuracy and precision of the measuring system. The system is valid for collecting informative data sets that are usable for modeling the engine's dynamic performance and fault detection.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1467045
Database: ERIC
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  Value: <anid>AN0183055949;p6i01jan.25;2025Feb18.03:58;v2.2.500</anid> <title id="AN0183055949-1">Data Acquiring System for Gas Turbine Engine's Dynamic Performance; Build and Validate </title> <p>Gas Turbine Engines (GTE) have the highest power-to-weight ratio among Internal Combustion Engines (ICE). Its modularity and ability to utilize various types of fuel make it highly recommended in power plants, naval transportation, and, of course, the most equipped in aviation. The lack of GTEs' real data is increasing a recognized need for collecting informative real data sets from GTEs for modeling, monitoring, and fault diagnosis and isolation (FDI). In the present article, a robust versatile data acquisition measuring system has been built to collect real-time data from an axial turboshaft GTE. The system facilitates connectivity and integrity by means of two wiring harnesses connecting the data acquisition card (DAQ), the engine's control unit, and the operator's control console. Mobility is guaranteed using a wireless connection between DAQ and a customized programmable LabVIEW interface application. Many experiments have been conducted in different operating conditions to collect the engine and controller's data in different regimes. The measured signals have been filtered using software-implemented filters and transformed into the right measurement quantities. The experiment data have been analyzed for different regimes, and the milestone documented readings were compared with the measured ones to validate the accuracy and precision of the measuring system. The system is valid for collecting informative data sets that are usable for modeling the engine's dynamic performance and fault detection.</p> <p>Keywords: Gas turbine engines; data acquisition system; FADEC</p> <hd id="AN0183055949-2">Introduction</hd> <p>The usage of GTEs is the most common in aviation on both commercial and military aircraft also, they are widely used in transportation and electric power generation (Li et al., [<reflink idref="bib13" id="ref1">13</reflink>]). Progress in GTE control systems and the introduction of new modeling and diagnostic techniques are sensitive topics that stimulate many researchers to keep investigating in order to advance GTE operation and efficiency (Mohamed & Khalil, [<reflink idref="bib15" id="ref2">15</reflink>]). However, neither goal can be fulfilled without a sufficiently accurate model, which in turn has a recognized need for collecting informative dynamic data sets from the engine being modeled. The latest progress in GTEs' modeling, control and diagnostic techniques will be reviewed in this section to highlight the recognized need for collecting real-time data in order to advance GTE operation and efficiency. Then, a search of the literature will take place to reveal that despite the importance of GTE real-time data, there remains a paucity of studies that consider the methodology of building acquisition systems, collecting, preprocessing, and preparing informative data sets for GTEs.</p> <p>Mohammadi et al. ([<reflink idref="bib17" id="ref3">17</reflink>]) proposed the main milestones in GTE control strategies over the past eight decades. They concluded the necessity of developing new control algorithms for the next generation of GTEs that permit optimized performance while dealing with the environmental considerations and limitations set by governments and organizations. Lutambo et al. ([<reflink idref="bib14" id="ref4">14</reflink>]) stated the influence of significant development of the engine's control strategies on increasing the efficiency and performance of GTEs. Both reviews highlighted the importance of accurate engine models along with the availability of the engine's real-time data to achieve progress in developing GTE control systems. Zhang et al. ([<reflink idref="bib29" id="ref5">29</reflink>]) presented a novel model compensation-based model predictive control (MPC) structure. The proposed approach had better reference tracking and disturbance rejection ability as a result of modifying the dynamics of the plant by the extended state observer (ESO) with the real-time input-output. Hanachi et al. ([<reflink idref="bib5" id="ref6">5</reflink>]) summarized the advantages and limitations of diagnostic and prognostic techniques commonly used for GTEs. With regards to the different strengths and weaknesses of the reviewed techniques, they suggested the consideration of advancing real-time data collection from GTE operating parameters in terms of the number of measured parameters and the volume. Li and Zhao ([<reflink idref="bib12" id="ref7">12</reflink>]) addressed simultaneous FDI for an aircraft engine by proposing two data-driven approaches based on multi-label learning. Although their experimental results diagnosed the simultaneous fault requiring a low computation burden, they confirmed that the diagnosis accuracy of the proposed methods can be further improved by adding more simultaneous fault real-time data into the training dataset. Previous studies on GTE control and FDI, show that high-fidelity models reinforced with real-time data are the main key in developing GTE performance.</p> <p>Hashmi et al. ([<reflink idref="bib6" id="ref8">6</reflink>]) presented a comprehensive literature overview considering various modeling techniques for GTEs' transient behavior. They highlighted the crucial role of the accurate and robust transient modeling of GTEs in ensuring a reliable and safe engine operation. Their review concluded the urgent need for real-time data to guarantee high-accuracy computational modeling for diagnostics, fault detection and prognostics, and predictive condition monitoring which, in turn, have a paramount importance nowadays. In common, dynamic modeling has three main approaches addressed by Coraddu et al. ([<reflink idref="bib2" id="ref9">2</reflink>]): physics-based models which explain natural phenomena through well-defined mathematics (Zhang et al., [<reflink idref="bib30" id="ref10">30</reflink>]), by contrast, data-driven models which are too close to the terms artificial intelligence (AI) and machine learning (ML) as presented in the recent literature (Li & Zhao, [<reflink idref="bib12" id="ref11">12</reflink>]; Nasiri et al., [<reflink idref="bib20" id="ref12">20</reflink>]; Salilew et al., [<reflink idref="bib25" id="ref13">25</reflink>]). The third approach combines physics-based and data-driven modeling methods into a hybrid method presented lately in the literature (Xu et al., [<reflink idref="bib28" id="ref14">28</reflink>]). Xu et al. ([<reflink idref="bib27" id="ref15">27</reflink>]) stated that the effectiveness of the proposed hybrid approach was evaluated and the verification results reveal the superiority of this methodology. The same approach was presented by Akbari et al. ([<reflink idref="bib1" id="ref16">1</reflink>]) for model-based sensor fault detection and their validation results showed precise GTE sensor fault detection and estimation. These promising results might be explained by the fact that the model was developed using real-time field data.</p> <p>As mentioned above, all three modeling approaches have a recognized need for collecting informative dynamic data sets from the engine being modeled. For physics-based models, the data is necessary to identify unmeasurable parameters and model validation. On the other hand, real data is the core of the data-driven models despite the technique used to build them. In addition, it offers a promising opportunity for novel research using the potential of Theory-guided data science (TGDS) (Karpatne et al., [<reflink idref="bib9" id="ref17">9</reflink>]). Mohamed and Za'ter ([<reflink idref="bib16" id="ref18">16</reflink>]) compared three modeling approaches for GT power generation systems involving physical-based, state-space, and artificial neural network (ANN) methods. They proved that ANN is the most accurate methodology and concluded that there are many more opportunities to improve it. However, more sets of data are needed to conduct different ANN structures such as RNN. Ibrahem et al. ([<reflink idref="bib7" id="ref19">7</reflink>]) modeled an aero-derivative GTE using a non-linear autoregressive network with exogenous inputs (NARX). To represent each of the GTE output parameters they used multiple-input single-output (MISO) NARX models with different configurations and the same input parameters. Using operational closed-loop data collected from Siemens (SGT-A65) GTE was the reason for the improvement in the accuracy and robustness of their modeling approach.</p> <p>In addition, building a sufficiently accurate model of GTEs and their subsystems plays a key role when using hardware in the loop simulation (HIL) testing technology in advancing control techniques, condition monitoring and diagnostic systems of GTEs (Kulikov et al., [<reflink idref="bib10" id="ref20">10</reflink>]). Montazeri-Gh et al. ([<reflink idref="bib19" id="ref21">19</reflink>]) simulate the dynamics of a turbojet engine using a HIL platform to rapid control prototyping of an electronic control unit. Kumarin et al. ([<reflink idref="bib11" id="ref22">11</reflink>]) designed a HIL simulation for testing a GTE control system based on a neural network model built using JETCAT-P60 testing data. Montazeri-Gh et al. ([<reflink idref="bib18" id="ref23">18</reflink>]) presented a HIL simulation for testing a two-shaft GTE's electronic control unit. Ibrahem et al. ([<reflink idref="bib8" id="ref24">8</reflink>]) performed a HIL simulation to demonstrate the performance of a MISO NARX model developed for an aero-derivative GTE based on operational data collected from Siemens (SGT-A65) and the physical engine controller is used to control that model. Salehi and Montazeri-GH ([<reflink idref="bib24" id="ref25">24</reflink>]) designed a test bench that facilitates the collection of real data to perform a HIL-based verification of a designed fuel control unit for GTE using the electrohydraulic load sensing method.</p> <p>In recognition of the importance of experimental data for GTE modeling and simulation, more attention has recently focused on providing a testbed to enable real-time performance capture of GTE subsystems. Samy et al. ([<reflink idref="bib26" id="ref26">26</reflink>]) presented a test bench with an integrated acquisition system for the inlet guide vanes' electrohydraulic position control system used in GTE. They profited enormously from the acquired data to promote their physics-based model simulation results. Fadel et al. ([<reflink idref="bib4" id="ref27">4</reflink>]) used a similar test bench to build a data-driven model for a fuel metering system of GTE. Furthermore, Elmayyah and Samy ([<reflink idref="bib3" id="ref28">3</reflink>]) used real-time data collected from a test bench for a turboshaft GTE fuel management unit to develop a reduced mathematical model for the engine's inlet guide vanes to decrease the computational cost by 45%. Salehi and Montazeri-Gh ([<reflink idref="bib23" id="ref29">23</reflink>]) modeled a turboshaft GTE's fuel control unit based on the NARX neural network using the data acquired from a test bench including the engine's hydraulic system.</p> <p>From the existing literature, the main challenge regarding GTE modeling is the lack of accurate and informative real data that describes the engine performance for all operating conditions. Some previous studies were concerned with the methodology of collecting specific GTE subsystems' real data. Despite the growing body of literature that recognizes the vital role of GTE real data (Nayeri et al., [<reflink idref="bib21" id="ref30">21</reflink>]), there remains a paucity of attention has been paid to the methodology of building acquisition systems, collecting, preprocessing, and preparing informative data sets for GTEs in the published research.</p> <p>Motivated by the aforementioned issues, this paper presents a detailed and comprehensive approach to building an acquisition system that provides accurate and informative real-time data of a double-spool recuperated axial turbo-shaft GTE for the engine's entire operation envelope. In this work, vast experimental data have been collected from a real test setup for an axial turboshaft GTE. The experimental data have been collected using a robust versatile data acquisition measuring system that provides mobility and facilitates the connection with the engine control unit to allow the acquisition of all available data from the engine and controller in different operating regimes. A customized LabVIEW interface application has been developed to wirelessly communicate with the data acquisition card DAQ. The capability of the system to precisely measure the engine's dynamic performance and accurately interpret it has been verified. In addition, the present work proposes a systematic method to build a data acquisition system for identifying GTE performance.</p> <p>The paper will provide a system description for the GTE under study. It then goes on to explain the measuring system both hardware and software parts. Then the experimental work conducted will be discussed followed by the data analysis and verification. The validation of the system will be performed for three different operating modes (starting sequence, acceleration/deceleration schedule for BOV and VIGV, and relight process). Finally, the conclusion and future work will take place.</p> <hd id="AN0183055949-3">System description</hd> <p>The system under investigation is a double-spool recuperated axial turbo-shaft GTE powering a ground vehicle with 1500 HP. A full authority digital engine control unit (FADEC) is controlling the engine's performance to fulfill the system's desired performance. Figure 1 shows a layout for the engine with the low-pressure (LP) and the high-pressure (HP) spools, each consisting of a multistage compressor and a one-stage turbine interconnected with a shaft. The power is extracted through a double-stage power turbine connected to a gearbox to convert the high RPM into high torque. The engine also incorporates a heat exchanger that utilizes the heat in the exhaust gases to preheat the compressor discharge air before it enters the combustor, noise suppression is an additional benefit of it. The engine, which is the plant under control, has three manipulating inputs illustrated in the figure in white boxes and black font. And also has three outputs in dark boxes written in white font.</p> <p>Graph: Figure 1. Engine layout.</p> <p>The three manipulating inputs that control the engine's behavior are as follows:</p> <p></p> <ulist> <item> The Variable Inlet Guide Vanes (VIGV) are located before the first stage of the compressor and their inclination angle directs the angle of attack of the inlet air to the LP compressor rotor for achieving a stall-free operation. It's linked to a Bleed-Off Valve (BOV) situated on top of the HP compressor to alleviate possible stall conditions in the inlet stages of the engine. These two devices are moved together using a single actuator derived from an electrohydraulic valve. Accordingly, from the controller's point of view, the two devices will be referred to here as (VIGV).</item> <p></p> <item> Weight of Fuel (WF) is the amount of fuel flow delivered to the combustor through an electrohydraulic fuel metering valve.</item> <p></p> <item> Power Turbine Stators (PTS) are stator vanes located in front of the 1<sups>st</sups> stage power turbine and derived by an electrohydraulic valve. PTS angle controls engine temperature and speed by controlling airflow through the engine.</item> </ulist> <p>While the three outputs being controlled are:</p> <p></p> <ulist> <item> High-pressure speed (NH) indicates the HP spool's angular speed and correspondingly how much pressure the sucked air gains.</item> <p></p> <item> Power Turbine speed (NPT) is the rotation speed of the engine's output shaft after the gearbox and it's the requested output from the plant.</item> <p></p> <item> Power Turbine Inlet Temperature (PTIT) is the temperature of the exhaust gases before entering the power turbine stators and it has to be kept within certain limits.</item> </ulist> <p>The latter description defines the engine as a multi-input multi-output dynamic system. But only one among the outputs, which is NPT, represents the engine's output power while the two other outputs have to be kept within certain limits to accomplish the safest and most economic operation. In accordance, the engine control algorithm has to schedule the VIGV/BOV and the PTS angles to keep the NH speed and PTIT in the required regime for the entire operation's envelope. While dynamically controlling the amount of fuel, WF, to achieve the required power that maintains a specific output speed, NPT, in certain load conditions.</p> <p>The latter explanation of the engine control algorithm is demonstrated in Figure 2. The reference signal for the output power requested by the operator is the power lever angle (PLA). The received (PLA) signal is transformed to the required power turbine speed, compared to the actual engine speed (NPT) then the error is fed to the engine control logic to determine the required fuel flow rate. On the other hand, the required VIGV/BOV angle is scheduled based on the NH speed and compressor inlet air temperature (T1) temperature, and the required PTS angle is scheduled based on the NH speed and the PTIT. In addition, three sub-controller algorithms are implemented to compare the request with the actual amount of the three manipulating signals (WF, VIGV, PTS) and accordingly determine the control signals for the solenoids that drive the electrohydraulic valves.</p> <p>Graph: Figure 2. Engine control algorithm layout.</p> <p>To capture the whole engine control scenario all related signals have to be recognized, measured, and interpreted to their physical quantities. Figure 3 illustrates the 27 related signals that flow through the engine's control unit classified into 5 groups as follows:</p> <p>Graph: Figure 3. Signals flow through the engine's control unit.</p> <p></p> <ulist> <item> The operator's commands to the control unit are 1 analog and 7 discrete signals as follows:</item> <p></p> <item> Power Lever Angle (PLA) which is the reference signal for the engine's requested power.</item> <p></p> <item> Five discrete signals perform particular functions: purge, start, stop, fault reset, and the automatic low oil pressure shutdown signal.</item> <p></p> <item> Two discrete signals define operating modes: a LOW/HIGH idle speed selection and load engagement switches.</item> <p></p> <item> The requests for the manipulating signals calculated by the engine control logic are 3 analog signals (VIGV<subs>request</subs>, WF<subs>request</subs>, PTS<subs>request</subs>)</item> <p></p> <item> The control signals from the control unit to the engine are 3 analog and 4 discrete signals as follows:</item> <p></p> <item> Three controlling signals for the proportional solenoids that drive the electro-hydraulic valves (VIGV<subs>solenoid</subs>, WF<subs>solenoid</subs>, PTS<subs>solenoid</subs>).</item> <p></p> <item> Four discrete switching signals (ON, OFF) for the engine's starter relay, exciter, fuel cut-off solenoid, and fuel back-up solenoid.</item> <p></p> <item> The feedback signals from the engine to the control unit are 7 analog signals as follows:</item> <p></p> <item> Compressor inlet air temperature (T1).</item> <p></p> <item> Three feedback signals for the controlled parameters (NH, NPT, PTIT).</item> <p></p> <item> Three feedback signals detect the actual quantity of the manipulating parameters (VIGV<subs>actual</subs>, WF<subs>actual</subs>, PTS<subs>actual</subs>)</item> <p></p> <item> Two analog signals are positive and negative reference voltages for the control unit's signal conditioning circuits.</item> </ulist> <p>This section has concluded the 16 analog and the 11 discrete signals that need to be measured. It is now necessary to determine the range of each signal and the circuitry to measure it from the engine's control network. The next section will describe the measuring system's circuitry, signal conditioning, hardware, and software needed to capture and log the real-time data.</p> <hd id="AN0183055949-4">Measuring system</hd> <p>This section ties together the requirements of the measuring system with the acquisition system's selection, design, and building. All the control unit's analog inputs have suitable built-in signal conditioning circuits. Thus, a representative DC voltage signal is available at the control unit diagnostic connector for each analog signal. The data acquisition system is required to measure the representative DC voltage of the signals. Among the 27 signals described in the latter section, a total of 20 signals are provided at the control unit diagnostic connector. The remaining seven signals are provided in the operator's console at three different diagnostic connectors. Accordingly, the control unit's standard diagnostic cable and a specially-made matting connector have been used to transmit the control unit signals (20 signals) to the acquisition card. In addition, a special-purpose wiring harness has been made to collect the remaining seven signals and connect them to the data acquisition card.</p> <p>The measured phenomena and its sensor's type, quantity, output signal, and conditioned signal full range along with the corresponding measurand range are presented in Table 1. It is apparent from this table that the representative DC voltage, of the analog signals, ranges from −10 Vdc to 10 Vdc. On the other hand, all the discrete signals are fed by the vehicle's main power bus, ranging from 20 Vdc, when in a low battery condition, to 29 Vdc while the engine is running. In order to rescale the discrete signals in the same voltage range as the analog ones, a signal conditioning circuit is required. Accordingly, a resistor voltage divider conditioning circuit, with one third voltage ratio, integrated with the acquisition system to rescale the discrete signals between 6.7 and 9.7 Vdc.</p> <p>Table 1. Analog and discrete signals of the measuring system.</p> <p> <ephtml> <table><thead><tr><td>Measured phenomena</td><td>Sensor type</td><td>Sensor Qty</td><td>Sensor O/P signal</td><td>Conditioned signal range <italic>V</italic><sub><italic>o</italic></sub> (DCV)</td><td>Measurand full range</td><td><italic>DR</italic><sub><italic>x</italic></sub></td><td><italic>S</italic></td></tr></thead><tbody><tr><td>NH</td><td>Speed pickup</td><td>2 redundant</td><td>Frequency</td><td>[0 10]</td><td>0–100(% Max)</td><td>1000</td><td>0.003(% Max)</td></tr><tr><td>NPT</td><td>Speed pickup</td><td>2 redundant</td><td>Frequency</td><td>[0 5]</td><td>0–100(%Max)</td><td>1000</td><td>0.006(% Max)</td></tr><tr><td>PLA</td><td>RVDT</td><td>1</td><td>Differential AC voltage</td><td>[0.5 7]</td><td>0–70 ̊</td><td>700</td><td>0.003 ̊</td></tr><tr><td>VIGV<sub>actual</sub></td><td>RVDT</td><td>1</td><td>[−10 0]</td><td>0–100(% Open)</td><td>100</td><td>0.003(%Open)</td></tr><tr><td>PTS<sub>actual</sub></td><td>RVDT</td><td>1</td><td>[−7–1]</td><td>100</td><td>0.005(%Open)</td></tr><tr><td>WF<sub>actual</sub></td><td>LVDT</td><td>1</td><td>[0.4 8]</td><td>40–800 PPH</td><td>760</td><td>0.03 PPH</td></tr><tr><td>VIGV<sub>request</sub>,</td><td>Generated by FADEC</td><td>–</td><td>DCV</td><td>[0 10]</td><td>0–100% Open)</td><td>100</td><td>0.003(%Open)</td></tr><tr><td>PTS<sub>request</sub></td><td>–</td><td>[−6 0]</td><td>100</td><td>0.005(%Open)</td></tr><tr><td>WF<sub>request</sub>,</td><td>–</td><td>[−8–0.4]</td><td>40–800 PPH</td><td>760</td><td>0.03 PPH</td></tr><tr><td>VIGV<sub>solenoid</sub></td><td>Generated by FADEC</td><td>–</td><td>DCV</td><td>[0 6]</td><td>–</td><td>––</td><td /></tr><tr><td>PTS<sub>solenoid</sub></td><td>–</td><td>[0 6]</td><td>–</td><td>––</td><td /></tr><tr><td>WF<sub>solenoid</sub></td><td>–</td><td>[1.5 4]</td><td>–</td><td>––</td><td /></tr><tr><td>T1</td><td>RTD</td><td>1</td><td>DCV</td><td>[1.5 6]</td><td>−32 52 ̊ C</td><td>84</td><td>0.006 ̊ C</td></tr><tr><td>PTIT</td><td>Thermocouple (type K)</td><td>12 averaging</td><td>Microvolt DC</td><td>[−6 4.5]</td><td>280 860 ̊ C</td><td>580</td><td>0.017 ̊ C</td></tr><tr><td>Pos. Ref</td><td>Supplied by vehicle</td><td>–</td><td>DCV</td><td>10</td><td>–</td><td>––</td><td /></tr><tr><td>Neg. Ref</td><td>–</td><td>−10</td><td>–</td><td>––</td><td /></tr><tr><td>Discrete signals</td><td>Switches</td><td>11 signals</td><td>On Battery</td><td>[18 24]</td><td>[ON OFF]</td><td>––</td><td /></tr><tr><td>On Generator</td><td>29</td><td>––</td><td /></tr></tbody></table> </ephtml> </p> <hd id="AN0183055949-5">Hardware</hd> <p>According to the data presented in Table 1, a 32-channel analog voltage input data acquisition card (NI-9205) with a ± 10Vdc input range was used for measuring the whole 27 signals. The National Instrument (NI) C Series voltage input module provides a maximum sampling rate of 250 kS/s and a 16 bits resolution for the analog-to-digital converter (ADC). Also, a wireless connection is needed between the data acquisition system and the host computer to achieve mobility. Thus, an NI 1‑Slot 802.11 Wi‑Fi Compact-DAQ chassis (cDAQ‑9191) was used for remoting the measurement system. The chassis controls the timing, synchronization, and data transfer between the NI-9205 C Series voltage input module and the external host Laptop. The acquisition chassis is supported with a set of three batteries and a charging circuit to provide the necessary power-up for the system during field measurement experiments. The chassis, the input module, the batteries with the charging circuit, and the voltage divider conditioning circuit are interconnected together in one sealed enclosure. The enclosure was equipped with two interface connectors, one for the diagnostic cable and the other for the special-purpose harness, along with an external power switch to turn the acquisition system on & off.</p> <p>Figure 4 illustrates the acquisition system's actual enclosure layout and Figure 5 shows the acquisition system's components as follows:</p> <p>Graph: Figure 4. Data acquisition system enclosure.</p> <p>Graph: Figure 5. Data acquisition system.</p> <p></p> <ulist> <item> Compact-DAQ chassis (cDAQ‑9191).</item> <p></p> <item> NI-9205 C Series voltage input module.</item> <p></p> <item> Battery set and charging circuit.</item> <p></p> <item> The voltage divider conditioning circuit.</item> <p></p> <item> Interface connectors.</item> <p></p> <item> Control unit diagnostic cable.</item> <p></p> <item> The special purpose wiring harness.</item> <p></p> <item> Power switch.</item> <p></p> <item> The engine's control unit.</item> <p></p> <item> Acquisition system enclosure.</item> <p></p> <item> The operator's console.</item> </ulist> <p>The basic specifications for the measuring system can be described according to (Pallàs-Areny & Piedrafita, [<reflink idref="bib22" id="ref31">22</reflink>]) by the quantization interval and the overall sensitivity for each signal. The 16-bit ADC provides 2<sups>16</sups> distinct digital outputs within a quantization interval Q of approximately 0.3 mV according to Equation 1. The intended dynamic range of certain measurement <emph>DR</emph><subs><emph>x</emph></subs> can be described by Equation 2 and has to be equal to or less than the ADC dynamic range <emph>DR</emph><subs><emph>ADC</emph></subs>. Table 1 shows <emph>DR</emph><subs><emph>x</emph></subs> for each measured phenomenon while <emph>DR</emph><subs><emph>ADC</emph></subs> is calculated by Equation 3. Accordingly, the sensitivity of each signal <emph>S</emph><subs><emph>x</emph></subs> of the measurement system can be obtained using Equation 4, and its value for each signal is illustrated in the last column of Table 1.</p> <p>(<reflink idref="bib1" id="ref32">1</reflink>)</p> <p>Graph</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="italic">Q</mi><mo>=</mo><mrow><mfrac><mrow><mrow><msub><mi mathvariant="italic">V</mi><mrow><mi mathvariant="italic">imax</mi></mrow></msub></mrow><mo>−</mo><mrow><msub><mi>V</mi><mrow><mi mathvariant="italic">imin</mi></mrow></msub></mrow></mrow><mrow><mrow><msup><mn>2</mn><mi>N</mi></msup></mrow></mrow></mfrac></mrow><mo>=</mo><mrow><mfrac><mrow><mn>10</mn><mo>−</mo><mfenced open="(" close=")"><mrow><mo>−</mo><mn>10</mn></mrow></mfenced></mrow><mrow><mrow><msup><mn>2</mn><mrow><mn>16</mn></mrow></msup></mrow></mrow></mfrac></mrow><mo>=</mo><mn>305</mn><mi mathvariant="italic">μV</mi></math> </ephtml> </p> <p>(<reflink idref="bib2" id="ref33">2</reflink>)</p> <p>Graph</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="italic">D</mi><mrow><msub><mi>R</mi><mi>x</mi></msub></mrow><mo>=</mo><mrow><mfrac><mrow><mi mathvariant="italic">Measurand</mi><mtext /><mi mathvariant="italic">Span</mi></mrow><mrow><mi mathvariant="italic">Intended</mi><mtext /><mi mathvariant="italic">resolution</mi></mrow></mfrac></mrow><mo>=</mo><mrow><mfrac><mrow><mrow><msub><mi mathvariant="italic">x</mi><mrow><mi mathvariant="italic">max</mi></mrow></msub></mrow><mo>−</mo><mrow><msub><mi>x</mi><mrow><mi mathvariant="italic">min</mi></mrow></msub></mrow></mrow><mrow><mi mathvariant="normal">Δx</mi></mrow></mfrac></mrow></math> </ephtml> </p> <p>(<reflink idref="bib3" id="ref34">3</reflink>)</p> <p>Graph</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mi mathvariant="italic">D</mi><mrow><msub><mi>R</mi><mrow><mi mathvariant="italic">ADC</mi></mrow></msub></mrow><mo>=</mo><mrow><mfrac><mrow><mo stretchy="false">(</mo><mrow><msup><mn>2</mn><mi>N</mi></msup></mrow><mo>−</mo><mn>1</mn><mo stretchy="false">)</mo><mo>∗</mo><mi mathvariant="italic">Q</mi></mrow><mi mathvariant="italic">Q</mi></mfrac></mrow><mo>=</mo><mn>65</mn><mo>,</mo><mn>535</mn></math> </ephtml> </p> <p>(<reflink idref="bib4" id="ref35">4</reflink>)</p> <p>Graph</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>S</mi><mi>x</mi></msub></mrow><mo>=</mo><mrow><mfrac><mrow><mi mathvariant="italic">Measurand</mi><mtext /><mi mathvariant="italic">Span</mi></mrow><mrow><mi mathvariant="normal">Δ</mi><mi mathvariant="italic">V</mi></mrow></mfrac></mrow><mo>∗</mo><mi mathvariant="italic">Q</mi><mo>=</mo><mrow><mfrac><mrow><mrow><msub><mi mathvariant="italic">x</mi><mrow><mi mathvariant="italic">max</mi></mrow></msub></mrow><mo>−</mo><mrow><msub><mi>x</mi><mrow><mi mathvariant="italic">min</mi></mrow></msub></mrow></mrow><mrow><mrow><msub><mi>V</mi><mrow><mi mathvariant="italic">omax</mi></mrow></msub></mrow><mo>−</mo><mrow><msub><mi>V</mi><mrow><mi mathvariant="italic">omin</mi></mrow></msub></mrow></mrow></mfrac></mrow><mo>∗</mo><mi mathvariant="italic">Q</mi></math> </ephtml> </p> <hd id="AN0183055949-6">Software</hd> <p>The Graphical User Interface (GUI) needed to connect the acquisition system to the host computer was developed using the NI-LabVIEW environment. A customized programmable LabVIEW interface application has been developed to wirelessly communicate with the data acquisition hardware with the capabilities illustrated in Figure 6 as follows:</p> <p>Graph: Figure 6. Date acquisition GUI.</p> <p></p> <ulist> <item> Set the sampling rate for acquiring data and the time for experimenting.</item> <p></p> <item> Receiving the real-time measured data and filtering the analog signals using software-implemented filters.</item> <p></p> <item> Save them along with the experiment's basic information (date and time, ambient temperature, serial numbers for engine & controller, experiment scenario).</item> <p></p> <item> Transform the measured signals to the right measurement quantities and render the measured, filtered, and actual signals with the ability to choose particular signals to display.</item> <p></p> <item> Export the data, or a specific segment of it, in several formats (TDMS, EXCEL) to facilitate the utilization of data by a variety of software applications (such as MATLAB®) for analyzing, pre-processing, and modeling the system.</item> </ulist> <p>In view of the thorough description of the acquisition system in this section, it can be seen that the system is capable of capturing the complete parameters of the engine control system. The next section will describe the experiments conducted to verify these capabilities, validate the data interpretation, and provide informative data sets for further analysis of the engine behavior.</p> <hd id="AN0183055949-7">Experimental work</hd> <p>This section will discuss the experiments conducted on the GTE under study and the measurements taken by the acquisition system. The experiments covered a wide range of operating modes including starting, low and high idle speed, acceleration and deceleration in no-load conditions, operating the engine in different load conditions, and finally cooling and shutting down the engine. Each operating mode had been measured at least three times. The experiments were conducted in various ambient conditions thus different inlet air temperature and accordingly dense has a great effect on the GTE control process.</p> <p>A total of 17 Experiments have been conducted during 10 successful runs for the engine that allowed logging engine's and control unit's data for 266 minutes (about 4.5 hours). The experiments have been conducted over a year to emphasize engine operation in different ambient conditions. As illustrated in Figure 7 the experiments started in June 2021 and extended to March 2022 covering an inlet temperature range starting from 14 to 44.5 ̊ C (57 to 112 ̊ F).</p> <p>Graph: Figure 7. Experiments running time and inlet temperature ranges.</p> <p>Samples of the measurements will be demonstrated in the following section representing different engine modes from the entire operating envelope. The measurements were compared with the prescribed manufacturer data to verify that the acquisition system is valid for acquiring real engine performance.</p> <hd id="AN0183055949-8">Analyzing measured data and verification</hd> <p>To verify the capability of the in-hand acquisition system to deliver confident and informative data sets representing real engine performance, the illustrated measurements have to cover a wide range of the entire engine operation. Accordingly, the measurements of three different operating modes will be analyzed herein.</p> <hd id="AN0183055949-9">Starting sequence</hd> <p>According to the engine's data, a brief explanation of the starting sequence is as follows. The starting sequence begins with the engagement of the starter and the excitation of the spark plug. The starter accelerates the high-pressure compressor until the control unit senses 5% NH speed it initializes a 50 PPH fuel flow request and ramps it up with a 5 PPH/s slope to reach 90 PPH and stays there. When the NH speed reaches 30% the control unit ramps the fuel flow request again with the previous rate to 165 PPH. During the acceleration process, the control unit monitors the NH until it reaches 50% to disengage the starter and ignition and declare the engine started. The final step of the starting process is accomplished, when the control unit senses NPT speed exceeds 27%, by quitting the starting fuel flow schedule and responding to the engine's steady-state controller fuel request.</p> <p>Starting process as shown in Figure 8 is a graph illustrating the first 85 seconds of the engine run process that includes the entire starting cycle until the engine reaches the steady state idle speed. The graph presents the measured real data and has two vertical axes to represent different units' measurements. The left one represents the normalized value for the Starter engagement (dashed black line), ignition excitation (solid red line), NH (dash-dot magenta line), and NPT (dash-dot black line) as a percentage of the maximum value. While the right vertical axis is for WFR (solid blue line) in pounds/hour (PPH).</p> <p>Graph: Figure 8. Starting sequence.</p> <p>To validate the right interpretation of the measured signals during the starting process, a comparison between the milestones data points mentioned previously and the interpreted measurement data will take a place as follow:</p> <p></p> <ulist> <item> At (1.5 s) the NH exceeded the 5% limit; the measured WFR was 50.5 PPH and ramped up to 90 ± 0.6 PPH with a 5 PPH/s slope.</item> <p></p> <item> At (13 s) the NH exceeded 30%; the measured WFR was ramped again to reach 165 ± 0.6 PPH.</item> <p></p> <item> At (26.4 s) the NH exceeded 50%; the controller disengaged the starter and stop the ignition.</item> <p></p> <item> At (52.2 s) the NPT exceeded 27%; the controller changed the fuel request according to the engine's steady-state requirements.</item> </ulist> <hd id="AN0183055949-10">Acceleration/deceleration schedule for BOV & VIGV</hd> <p>Because the GTE under investigation incorporates a multistage compressor and operates at part-speed conditions from almost (50–100) % NH speed, the engine is incorporated with BOV & VIGV systems. Both systems prevent the air stall on the airfoil surfaces of the compressor's rotating blades to guarantee surge-free operation. Engine surge is a momentary airflow stoppage caused by the high-pressure reservoir of air in the combustor that tends to discharge forward through the compressor. In order to prevent surge occurrence, the BOV & VIGV position is scheduled according to NH value.</p> <p>At low compressor speeds, the BOV is kept open to unload the compressor. The valve will proportionately close as the compressor speed increases. Since BOV is mechanically linked to VIGV and pre-adjusted together, the VIGV starts to move to the open position when the BOV is completely closed. The NH values upon which the VIGV &BOV systems are scheduled are T1-biased. in other words, the opening and closing values of BOV & VIGV change according to T1. The prescribed engine data landmarks that will be checked herein to validate the system are at T1 = 15 ̊C. The BOV should begin to open at 70% NH and by 85% NH it will be completely closed and the VIGV starts to open. In addition, the opening and closing schedules for both systems have to slightly vary between acceleration and deceleration.</p> <p>The BOV & VIGV schedules for acceleration and deceleration are shown in Figure 9. The graph presents a relation between the measured NH data on the horizontal axis and two vertical axes. The left one represents the normalized position for the BOV & VIGV as a percentage of the maximum open position. Four graphs are plotted against this axis; the BOV acceleration (solid red line), the BOV deceleration (dashed red line), the VIGV acceleration (solid blue line), and the VIGV deceleration (dashed blue line). While the right vertical axis is for the measured T1 temperature (dotted black line).</p> <p>Graph: Figure 9. VIGV & BOV schedule during acceleration and deceleration.</p> <p>The graphs show an identical correlation between the prescribed landmarks and the interpreted measured signals at a measured intake temperature of 15 ̊C. In addition, an offset of (2–4) % between the acceleration and deceleration schedules for both BOV & VIGV has been recognized.</p> <hd id="AN0183055949-11">Engine relight process</hd> <p>The engine operation during fast transient can be severely restricted by the combustor's flame out. To assist in flame-out protection, a relight logic is incorporated in the controller to prevent the loss of combustor flame. The controller logic monitors the rate of change of compressor speed (NH') and enables the ignition for 10 seconds any time a rapid deceleration excess of preset limits (−5%) is detected. To demonstrate the occurrence of the relight process, the rapid transition between low and high idle speed is shown in Figure 10. The figure shows four synchronous cascaded subplots representing the real-time data for five measured signals during the idle speed transition. The transition has been conducted during the experiment between (140–215) s, represented on the horizontal axis of all subplots. The upper subplot illustrates the percentage change of NH' (blue line), the second one represents the state (Low/High) of the idle speed selector switch (magenta line), while the controller's ignition signal to the igniter (red line) is illustrated in the third subplot, and finally, both NH (black line) & NPT (brown line) speeds plotted in the lower subplot as a percentage of maximum speed.</p> <p>Graph: Figure 10. Relight process.</p> <p>The interpreted measurement data shows that at 142.1 s the idle speed's selector switch is set to (HIGH) informing the controller to raise NPT from 29% to 45%. Accordingly, the controller increases the fuel flow and allows the engine to accelerate while monitoring NH' during the transient response. The controller detected a rapid deceleration (NH'= -7.88%) that exceeded the preset limit at 145.8 s, thus it set the igniter signal to (ON). After exactly 10 seconds the igniter signal has been reset again to (OFF) at 155.8 s. The idle speed's selector switch is reset to (LOW) at 196.2 s allowing the controller to lower NPT to 29% while monitoring an in-limit deceleration rate.</p> <hd id="AN0183055949-12">Conclusion</hd> <p>The present research aimed to introduce a robust, reliable, and mobile solution for collecting the GTE dynamic performance real-time data. The research presented a battery-powered 16-bit data acquisition system with up to 250 kS/s acquisition rate. The introduced system achieved mobility by wirelessly connecting to a customized programmable LabVIEW interface application. The developed application is capable of collecting, preprocessing, retrieving the measured signals, and interpreting them to their physical quantities. Vast experiments have been conducted to emphasize covering the engine's entire operating envelope. The experiments were conducted over nearly one year in different ambient conditions. The capabilities of the system and the interpretation of the signals have been verified by comparing a wide range of the engine's measurements with the prescribed manufacturer data. The validation of the system has been performed for three different operating modes (starting sequence, scheduling BOV&VIGV and relight process) that demonstrate the steady-state, transient and fault correction conditions. The results analysis of this comparison, conducted in section ‎5, show the capability of the developed acquisition system to precisely measure the engine's dynamic performance and accurately interpret it. Accordingly, the acquisition system proposed herein is a reliable source for GTE's informative data sets. It is recommended that further research efforts based on the collected data sets be undertaken in the following areas:</p> <p></p> <ulist> <item> Build a high-fidelity model for the GTE using data-driven modeling techniques.</item> <p></p> <item> Deploying different control algorithms on the GTE using HIL simulation.</item> <p></p> <item> Applying FDI techniques to the engine's real data.</item> </ulist> <hd id="AN0183055949-13">ACRONYMS</hd> <p></p> <p> <ephtml> <table><tbody><tr><td>ANN</td><td>Artificial Neural Network</td></tr><tr><td>ADC</td><td>Analog to Digital Converter</td></tr><tr><td>AI</td><td>Artificial Inelegance</td></tr><tr><td>BOV</td><td>Bleed-Off Valve</td></tr><tr><td>DAQ</td><td>Data Acquisition Card</td></tr><tr><td>DC</td><td>Direct Current</td></tr><tr><td>FADEC</td><td>Full Authority Digital Engine Control</td></tr><tr><td>FDI</td><td>Fault Diagnostic and Isolation</td></tr><tr><td>GTE</td><td>Gas Turbine Engine</td></tr><tr><td>GUI</td><td>Graphical User Interface</td></tr><tr><td>HP</td><td>High Pressure</td></tr><tr><td>HIL</td><td>Hardware In Loop simulation</td></tr><tr><td>ICE</td><td>Internal Combustion Engine</td></tr><tr><td>LP</td><td>Low Pressure</td></tr><tr><td>MISO</td><td>Multiple Input Single Output</td></tr><tr><td>ML</td><td>Machine Learning</td></tr><tr><td>MPC</td><td>Model Predictive Control</td></tr><tr><td>NARX</td><td>Non-linear Autoregressive Network with Exogenous Inputs</td></tr><tr><td>PLA</td><td>Power Lever Angle</td></tr><tr><td>PPH</td><td>Pound Per Hour</td></tr><tr><td>PTIT</td><td>Power Turbine Inlet Temperature</td></tr><tr><td>PTS</td><td>Power Turbine Stator</td></tr><tr><td>RNN</td><td>Recurrent Neural Network</td></tr><tr><td>TDMS</td><td>Technical Data Management Streaming</td></tr><tr><td>TGDS</td><td>Theory-Guided Data Science</td></tr><tr><td>VIGV</td><td>Variable Inlet Guide Vanes</td></tr><tr><td>WF</td><td>Weight of Fuel</td></tr><tr><td>WFR</td><td>Weight of Fuel Request</td></tr></tbody></table> </ephtml> </p> <hd id="AN0183055949-14">NOMENCLATURE</hd> <p></p> <ulist> <item> DR<subs>ADC</subs></item> <p></p> <item> Dynamic range of the ADC[-]</item> <p></p> <item> DR<subs>x</subs></item> <p></p> <item> Dynamic range of the x measurement[-]</item> <p></p> </ulist> <p>• N</p> <p></p> <ulist> <item> The ADC number of bits[bit]</item> <p></p> </ulist> <p>• NH</p> <p></p> <ulist> <item> High-pressure spool's angular speed[% max. RPM]</item> <p></p> </ulist> <p>• NPT</p> <p></p> <ulist> <item> Power turbine angular speed[% max. RPM]</item> <p></p> </ulist> <p>• Q</p> <p></p> <ulist> <item> Resolution of DAQ voltage measurement[mV]</item> <p></p> <item> S<subs>x</subs></item> <p></p> <item> Sensitivity of x signal measurement[x signal unit]</item> <p></p> </ulist> <p>• T1</p> <p></p> <ulist> <item> Compressor inlet air Temperature[°C]</item> <p></p> <item> V<subs>imax</subs></item> <p></p> <item> Maximum input voltage for DAQ[V]</item> <p></p> <item> V<subs>imin</subs></item> <p></p> <item> Minimum input voltage for DAQ[V]</item> <p></p> <item> V<subs>omax</subs></item> <p></p> <item> Maximum measured voltage for x signal[V]</item> <p></p> <item> V<subs>omin</subs></item> <p></p> <item> Minimum measured voltage for x signal[V]</item> <p></p> <item> x<subs>max</subs></item> <p></p> <item> Maximum value for x signal[x signal unit]</item> <p></p> <item> x<subs>min</subs></item> <p></p> <item> Minimum value for x signal[x signal unit]</item> </ulist> <hd id="AN0183055949-15">Abbreviations</hd> <p></p> <p> <ephtml> <table><tbody><tr><td>Pos. 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  Group: Ti
  Data: Data Acquiring System for Gas Turbine Engine's Dynamic Performance; Build and Validate
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  Label: Language
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  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Mostafa+M%2E+Samy%22">Mostafa M. Samy</searchLink><br /><searchLink fieldCode="AR" term="%22Mohamed+A%2E+Metwally%22">Mohamed A. Metwally</searchLink><br /><searchLink fieldCode="AR" term="%22Mahmoud+Ashry%22">Mahmoud Ashry</searchLink><br /><searchLink fieldCode="AR" term="%22Wael+M%2E+Elmayyah%22">Wael M. Elmayyah</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3302-0342">0000-0003-3302-0342</externalLink>)
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  Label: Source
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  Data: <searchLink fieldCode="SO" term="%22Measurement%3A+Interdisciplinary+Research+and+Perspectives%22"><i>Measurement: Interdisciplinary Research and Perspectives</i></searchLink>. 2025 23(1):39-55.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Label: Peer Reviewed
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 17
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Evaluative
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Engines%22">Engines</searchLink><br /><searchLink fieldCode="DE" term="%22Power+Technology%22">Power Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Collection%22">Data Collection</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Interpretation%22">Data Interpretation</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+Methods%22">Evaluation Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Construction%22">Test Construction</searchLink><br /><searchLink fieldCode="DE" term="%22Test+Validity%22">Test Validity</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+Equipment%22">Measurement Equipment</searchLink><br /><searchLink fieldCode="DE" term="%22Measurement+Objectives%22">Measurement Objectives</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Systems%22">Information Systems</searchLink><br /><searchLink fieldCode="DE" term="%22Data+Processing%22">Data Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Testing%22">Computer Assisted Testing</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/15366367.2023.2283682
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1536-6367<br />1536-6359
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Gas Turbine Engines (GTE) have the highest power-to-weight ratio among Internal Combustion Engines (ICE). Its modularity and ability to utilize various types of fuel make it highly recommended in power plants, naval transportation, and, of course, the most equipped in aviation. The lack of GTEs' real data is increasing a recognized need for collecting informative real data sets from GTEs for modeling, monitoring, and fault diagnosis and isolation (FDI). In the present article, a robust versatile data acquisition measuring system has been built to collect real-time data from an axial turboshaft GTE. The system facilitates connectivity and integrity by means of two wiring harnesses connecting the data acquisition card (DAQ), the engine's control unit, and the operator's control console. Mobility is guaranteed using a wireless connection between DAQ and a customized programmable LabVIEW interface application. Many experiments have been conducted in different operating conditions to collect the engine and controller's data in different regimes. The measured signals have been filtered using software-implemented filters and transformed into the right measurement quantities. The experiment data have been analyzed for different regimes, and the milestone documented readings were compared with the measured ones to validate the accuracy and precision of the measuring system. The system is valid for collecting informative data sets that are usable for modeling the engine's dynamic performance and fault detection.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1467045
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1467045
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    Identifiers:
      – Type: doi
        Value: 10.1080/15366367.2023.2283682
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 17
        StartPage: 39
    Subjects:
      – SubjectFull: Engines
        Type: general
      – SubjectFull: Power Technology
        Type: general
      – SubjectFull: Data Collection
        Type: general
      – SubjectFull: Data Interpretation
        Type: general
      – SubjectFull: Evaluation Methods
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      – SubjectFull: Test Construction
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      – SubjectFull: Test Validity
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      – SubjectFull: Measurement Equipment
        Type: general
      – SubjectFull: Measurement Objectives
        Type: general
      – SubjectFull: Information Systems
        Type: general
      – SubjectFull: Data Processing
        Type: general
      – SubjectFull: Computer Assisted Testing
        Type: general
    Titles:
      – TitleFull: Data Acquiring System for Gas Turbine Engine's Dynamic Performance; Build and Validate
        Type: main
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            NameFull: Mostafa M. Samy
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            NameFull: Mohamed A. Metwally
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            NameFull: Mahmoud Ashry
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            NameFull: Wael M. Elmayyah
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              Y: 2025
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              Value: 1536-6367
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              Value: 1536-6359
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              Value: 23
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            – TitleFull: Measurement: Interdisciplinary Research and Perspectives
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