Advancements in Induction Motor Fault Diagnosis and Condition Monitoring: A Comprehensive Review

Area of Science:

• Electrical Engineering
• Industrial Automation
• Machine Condition Monitoring

Background:

• Induction motors (IMs) are critical industrial components prone to faults affecting efficiency and safety.
• Timely fault detection is essential for preventing operational downtime and serious malfunctions.
• Existing fault diagnosis methods face challenges in handling complex data characteristics.

Purpose of the Study:

• To provide a comprehensive analysis of current IM fault detection techniques.
• To identify deficiencies and obstacles in the existing body of knowledge.
• To explore the effectiveness of various fault classification approaches in addressing data-driven challenges.

Main Methods:

• Systematic review of IM fault diagnosis literature.
• Categorization of approaches based on the IM diagnosis process.
• Analysis of fault classification techniques against data-driven challenges (e.g., high-dimensionality, class imbalance, nonlinearity, noise, overfitting).

Main Results:

• Identified gaps and limitations in current IM fault detection research.
• Highlighted the increasing trend towards data-driven fault diagnosis strategies.
• Demonstrated the growing importance and application of deep learning in IM fault diagnosis.

Conclusions:

• Data-driven approaches, particularly deep learning, are crucial for overcoming complex fault diagnosis challenges.
• Advancements in these areas significantly impact the field, enabling intelligent, real-time condition monitoring.
• Further research is needed to fully leverage intelligent systems for enhanced motor reliability.