An Industrial Internet Application for Real-Time Fault Diagnosis in Industrial Motors

• Published in: IEEE transactions on automation science and engineering Vol. 17; no. 1; pp. 284 - 295
• Main Authors: Langarica, Saul, Ruffelmacher, Christian, Nunez, Felipe
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Asset management; Cyber physical systems; Evaluation; False alarms; Fault detection; Fault diagnosis; Identification; industrial automation; Industrial Internet of Things; Industry standards; Maintenance engineering; Motors; Parameter estimation; Predictive maintenance; Principal component analysis; Principal components analysis; Productivity; Real time; Real-time systems; Signal processing algorithms; Source code; Unbalance; Vibration analysis; Vibration measurement
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2019.2913628

Being able to detect, identify, and diagnose a fault is a key feature of industrial supervision systems, which enables advance asset management, in particular, predictive maintenance, which greatly increases efficiency and productivity. In this paper, an Industrial Internet app for real-time fault detection and diagnosis is implemented and tested in a pilot scale industrial motor. Real-time fault detection and identification is based on dynamic incremental principal component analysis (DIPCA) and reconstruction-based contribution (RBC). When the analysis indicates that one of the vibration measurements is responsible for the fault, a convolutional neural network (CNN) is used to identify the unbalance or bearing fault type. The application was evaluated in its three functionalities: fault detection, fault identification, and fault identification of vibration-related faults, yielding a fault detection rate over 99%, a false alarm rate below 5%, and an identification accuracy over 90%.