artículo
An Industrial Internet Application for Real-Time Fault Diagnosis in Industrial Motors
Fecha
2020Registro en:
10.1109/TASE.2019.2913628
1558-3783
1545-5955
WOS:000507640900022
Autor
Langarica Chavira, Saúl Alberto
Ruffelmacher, Christian
Nuñez, Felipe
Institución
Resumen
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%.