Data-Driven Fault Diagnosis for Electric Drives: A Review

Abstract

The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.

Machine Learning-Based Fault Detection and Diagnosis of Faulty Power Connections of Induction Machines

“…For example, there are many examples that use vibration or current measurements to diagnose stator and rotor failures [30][31][32][33][34][35]. Article [36] presents an extensive review of the application of data-driven methods for electric drives.…”

“…Furthermore, increasing the cost of a drive by adding FDD functionalities is not justified nowadays, especially in view of the rise of communication and cloud-based technologies. As mentioned in [36], FDD strategy trends show that data-driven methodologies based on ML or DL have emerged as a valid solution for electric drives. As an example, several publications show the application of ML or DL for the detection of faults in stator, rotor and bearings [32,[50][51][52].…”

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

“…Furthermore, Rauber, et al [ 4 ], proposed a methodology based on feature extraction and dimensionality reduction with principal component analysis applied to bearing fault diagnosis. Another relevant studies that address the basic guidelines of DDCM approaches on machine learning are [ 5 , 6 ]. Although this intelligent fault diagnostics research reports a significant advance in predictive maintenance, there are still some limitations.…”

CNN-LSTM-Based Prognostics of Bidirectional Converters for Electric Vehicles’ Machine

“…There are several reasons, from the high capital cost and central importance electrical machines play in the process, to the heavy economic losses that unexpected faults can generate because of needed machine or parts replacements, loss of production due to its unavailability, or property damage to property and people injuries [5]. The imperious requirement of manufacturing more competitive equipment, together with the progress of digital technologies, has facilitated the acquisition of operational data, which can be processed by means of machine learning (ML) methods to extract valuable information to apply data-driven diagnosis and maintenance approaches [6].…”