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.
Highlights
Research on Condition Monitoring (CM) and maintenance of electric drives has been a field of activity for decades
Model- and signal-based techniques have been used. The former method is based on an analytical redundancy generated by the mathematical model that replicates the operational behaviour of the system under investigation
The latter is based on the analysis of different signals acquired from the real system to identify specific characteristics that indicate anomalies in the equipment
Summary
Research on Condition Monitoring (CM) and maintenance of electric drives has been a field of activity for decades. Electric drives control software including algorithms, strategies, or routines aimed at actively monitoring their operation by supervising possible system faults. For this purpose, traditionally, model- and signal-based techniques have been used. Model- and signal-based techniques have been used The former method is based on an analytical redundancy generated by the mathematical model that replicates the operational behaviour of the system under investigation. The latter is based on the analysis of different signals acquired from the real system to identify specific characteristics that indicate anomalies in the equipment.
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