Enhancing Software Reliability in Industrial Mechatronics through Anomaly Detection Models.
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| Title: | Enhancing Software Reliability in Industrial Mechatronics through Anomaly Detection Models. |
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| Authors: | Singh, Baljeet1 baljeet.16938@lpu.co.in |
| Source: | International Journal of Performability Engineering. Aug2025, Vol. 21 Issue 8, p429-437. 9p. |
| Subjects: | Software reliability, Anomaly detection (Computer security), Industrial controls manufacturing, Machine learning, Condition-based maintenance, Failure analysis, Mechatronics, Telemetry |
| Abstract: | Mechatronic systems, including robotic arms and CNC machines, can experience operational failures, increased downtime, and financial losses due to software flaws. Through the analysis of sensor data, system logs, and operational metrics, this study suggests a hybrid machine learning (ML) framework for anomaly identification and fault prediction in mechatronic systems. Three main modules make up the framework: (1) a feature extraction module that uses time-series and statistical analysis to extract important indicators; (2) an anomaly detection module that uses Autoencoders and Isolation Forest to find anomalous patterns; and (3) a fault prediction module that uses a Random Forest and Multi-Layer Perceptron (MLP) ensemble for accurate fault classification. Real-world industrial datasets and benchmark datasets, including the NASA Bearing Dataset and PHM Data Challenge datasets, are used to assess the suggested approach. According to experimental results, the Fault Prediction Module achieves 96.3% accuracy with an AUC of 0.98, while the Anomaly Detection Module achieves 94.5% accuracy. The framework improves predictive maintenance techniques, lowers false alarms, and effectively detects anomalies caused by software. This study demonstrates how ML-driven fault detection can enhance industrial mechatronic systems' dependability, minimize downtime, and optimize maintenance schedules. For additional improvement, future research will concentrate on edge computing integration, real-time deployment, and adaptive learning models. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 188335743 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing Software Reliability in Industrial Mechatronics through Anomaly Detection Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Singh%2C+Baljeet%22">Singh, Baljeet</searchLink><relatesTo>1</relatesTo><i> baljeet.16938@lpu.co.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Performability+Engineering%22">International Journal of Performability Engineering</searchLink>. Aug2025, Vol. 21 Issue 8, p429-437. 9p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Software+reliability%22">Software reliability</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Industrial+controls+manufacturing%22">Industrial controls manufacturing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Condition-based+maintenance%22">Condition-based maintenance</searchLink><br /><searchLink fieldCode="DE" term="%22Failure+analysis%22">Failure analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Mechatronics%22">Mechatronics</searchLink><br /><searchLink fieldCode="DE" term="%22Telemetry%22">Telemetry</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Mechatronic systems, including robotic arms and CNC machines, can experience operational failures, increased downtime, and financial losses due to software flaws. Through the analysis of sensor data, system logs, and operational metrics, this study suggests a hybrid machine learning (ML) framework for anomaly identification and fault prediction in mechatronic systems. Three main modules make up the framework: (1) a feature extraction module that uses time-series and statistical analysis to extract important indicators; (2) an anomaly detection module that uses Autoencoders and Isolation Forest to find anomalous patterns; and (3) a fault prediction module that uses a Random Forest and Multi-Layer Perceptron (MLP) ensemble for accurate fault classification. Real-world industrial datasets and benchmark datasets, including the NASA Bearing Dataset and PHM Data Challenge datasets, are used to assess the suggested approach. According to experimental results, the Fault Prediction Module achieves 96.3% accuracy with an AUC of 0.98, while the Anomaly Detection Module achieves 94.5% accuracy. The framework improves predictive maintenance techniques, lowers false alarms, and effectively detects anomalies caused by software. This study demonstrates how ML-driven fault detection can enhance industrial mechatronic systems' dependability, minimize downtime, and optimize maintenance schedules. For additional improvement, future research will concentrate on edge computing integration, real-time deployment, and adaptive learning models. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Performability Engineering is the property of Totem Publisher, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.23940/ijpe.25.08.p3.429437 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 429 Subjects: – SubjectFull: Software reliability Type: general – SubjectFull: Anomaly detection (Computer security) Type: general – SubjectFull: Industrial controls manufacturing Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Condition-based maintenance Type: general – SubjectFull: Failure analysis Type: general – SubjectFull: Mechatronics Type: general – SubjectFull: Telemetry Type: general Titles: – TitleFull: Enhancing Software Reliability in Industrial Mechatronics through Anomaly Detection Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Singh, Baljeet IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09731318 Numbering: – Type: volume Value: 21 – Type: issue Value: 8 Titles: – TitleFull: International Journal of Performability Engineering Type: main |
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