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Fusing LabVIEW and Machine Learning: A Project‐Based Approach for Teaching Industrial Condition Monitoring.

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Bibliographic Details
Title:	Fusing LabVIEW and Machine Learning: A Project‐Based Approach for Teaching Industrial Condition Monitoring.
Authors:	Xu, Xin¹ (AUTHOR), Li, Hongli¹ (AUTHOR), Pan, Chengliang¹ (AUTHOR) clpan@hfut.edu.cn, Gao, Ruhao² (AUTHOR), Lin, Xiaotian² (AUTHOR), Zhang, Tengda¹ (AUTHOR), Wang, Biao¹ (AUTHOR), Shu, Shuangbao¹ (AUTHOR), Cheng, Juan¹ (AUTHOR), Xia, Haojie¹ (AUTHOR) hjxia@hfut.edu.cn
Source:	Computer Applications in Engineering Education. Mar2026, Vol. 34 Issue 2, p1-13. 13p.
Subjects:	LabVIEW (Computer software), Machine learning, Monitoring of machinery, National Instruments Corp., Industry 4.0, Engineering education, Mechanical vibration research, Project method in teaching, Convolutional neural networks
Abstract:	The rapid evolution of Industry 4.0 necessitates that engineering education equips students with skills that bridge traditional instrumentation and modern data‐driven analytics. This paper addresses this need by presenting a comprehensive project‐based learning module that fuses LabVIEW‐based virtual instrumentation with machine learning (ML) for teaching industrial condition monitoring. Implemented in a senior‐level undergraduate course, the module tasks student teams with diagnosing the health of an industrial fan. Using the NI ELVIS educational platform instrumented with an accelerometer, students acquire real‐time vibration data. A key innovation is the seamless integration of LabVIEW, used for data acquisition and visualization, with a Python‐based Convolutional Neural Network (CNN) model, which classifies the fan's condition (normal, minor, or severe malfunction) and rotational speed. The technical implementation achieved high classification accuracy (exceeding 95% on test data) and low inference latency (approximately 0.1 s), demonstrating the feasibility of real‐time ML deployment. Pedagogically, the project provided an authentic, interdisciplinary learning experience, enhancing student understanding of vibration analysis, sensor integration, and the practical application of deep learning. The module successfully demonstrates a scalable framework for incorporating AI into engineering laboratories, effectively preparing students for roles that require synthesizing physical system knowledge with intelligent algorithm deployment. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	The rapid evolution of Industry 4.0 necessitates that engineering education equips students with skills that bridge traditional instrumentation and modern data‐driven analytics. This paper addresses this need by presenting a comprehensive project‐based learning module that fuses LabVIEW‐based virtual instrumentation with machine learning (ML) for teaching industrial condition monitoring. Implemented in a senior‐level undergraduate course, the module tasks student teams with diagnosing the health of an industrial fan. Using the NI ELVIS educational platform instrumented with an accelerometer, students acquire real‐time vibration data. A key innovation is the seamless integration of LabVIEW, used for data acquisition and visualization, with a Python‐based Convolutional Neural Network (CNN) model, which classifies the fan's condition (normal, minor, or severe malfunction) and rotational speed. The technical implementation achieved high classification accuracy (exceeding 95% on test data) and low inference latency (approximately 0.1 s), demonstrating the feasibility of real‐time ML deployment. Pedagogically, the project provided an authentic, interdisciplinary learning experience, enhancing student understanding of vibration analysis, sensor integration, and the practical application of deep learning. The module successfully demonstrates a scalable framework for incorporating AI into engineering laboratories, effectively preparing students for roles that require synthesizing physical system knowledge with intelligent algorithm deployment. [ABSTRACT FROM AUTHOR]
ISSN:	10613773
DOI:	10.1002/cae.70160