Machinery Fault Diagnosis in Electric Motors Through Mechanical Vibration Monitoring Using Fiber Bragg Grating-Based Accelerometers

This article presents machinery fault diagnosis in electric motors through mechanical vibration monitoring. Using a commercial machinery fault simulator, two fiber Bragg grating (FBG) accelerometers are evaluated under one healthy condition and nine different fault conditions, including rotors with broken bars, faulted bearings, misalignment, and imbalance. The mechanical vibration spectrum for each fault condition is assessed by analyzing the Bragg wavelength shift modulated by the acceleration. Offline analysis is carried out to identify the mechanical vibration pattern characteristics. In this preliminary analysis, the high-order frequency characteristics of faulted-bearing conditions and the slip frequency characteristics of broken rotor bar conditions were well-identified by both accelerometers. Following this analysis, this article uses two concurrent approaches to indicate the feasibility of classifying and/or clustering the data from supervised or unsupervised approaches. To that extent, the sensor system is first analyzed using a supervised approach from the
$k$
-nearest neighbors (kNN) approach for fault condition classification, where the proposed supervised algorithm resulted in an accuracy of 100% for the fault condition estimation. After the classification analysis, data clustering is also proposed. In the unsupervised approach, the highest score for seven clusters was observed. Besides in this work, nine fault conditions were studied, there are two fault conditions where the positions of the accelerometers were interchanged for a sensor’s acceleration sensitivity evaluation purpose (maintaining the same fault condition). Finally, the results obtained in this work have shown the feasibility of the use of FBG-based accelerometers in different fault conditions under supervised and unsupervised approaches.