Data-Driven Fault Diagnosis for Electric Drives: A Review
David Gonzalez-Jimenez1, Jon Del-Olmo1, Javier Poza1
1Faculty of Engineering, Mondragon Unibertsitatea, 20500 Arrasate-Mondragón, Gipuzkoa, Spain.
Sensors (Basel, Switzerland)
|July 2, 2021
Summary
This review explores data-driven strategies for electric drive fault detection and diagnosis (FDD), highlighting Machine Learning techniques that are advancing Condition Monitoring beyond traditional methods.
Area of Science:
- Engineering
- Computer Science
- Industrial Automation
Background:
- Industry 4.0 digital technologies are transforming industrial sectors.
- Massive operational data acquisition and processing are key trends.
- Data-driven approaches are surpassing traditional Condition Monitoring methods.
Purpose of the Study:
- To review data-driven active supervision strategies for electric drives.
- To focus on fault detection and diagnosis (FDD) methods.
- To identify research gaps and opportunities in this field.
Main Methods:
- Overview of primary FDD methodologies.
- Explanation of Machine Learning workflow implementation for data-driven strategies.
- Comprehensive review of relevant scientific literature.
Main Results:
- Data-driven strategies are increasingly prevalent in electric drive FDD.
- Machine Learning and Deep Learning are central to modern Condition Monitoring.
- Traditional model- and signal-based methods are being superseded.
Conclusions:
- The review consolidates current data-driven FDD strategies for electric drives.
- Guidelines for implementing Machine Learning workflows are provided.
- Key research gaps and future opportunities in electric drive FDD are identified.
Keywords:
condition monitoringdata-drivenelectric driveelectric tractionfault detectionfault diagnosismachine learningMore Related Videos
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