A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor

Table 1

Comparison between various CM analysis techniques for bearings of IM.

The technique	Advantages	Drawbacks	Fault
Temperature and infrared analysis	(i) Basic method; (ii) Noninvasive	(i) Expensive sensor is required; (ii) It cannot be used as early FDD	(i) Mechanical and electrical faults
Vibration and noise analysis	(i) Reliable and standard method; (ii) It can be used as early FDD	(i) Sensitive to the noise; (ii) Expensive sensor is required; (iii) Intrusive	(i) Mechanical faults
Chemical and oil analysis	(i) Fault estimation and location capabilities; (ii) High performance for bearing FDD	(i) Expensive; (ii) Applicable for big size machines	(i) Mechanical faults
Sound and acoustic emission analysis	(i) It could be used as reliable and remote CM; (ii) It is easily implemented; (iii) Fault estimation and location capabilities; (iv) Signal to noise ratio is high; (v) It deals with high frequency range	(i) Sensitive to the noise; (ii) Expensive sensor is required; (iii) Intrusive	(i) Mechanical faults
Current, voltage, and electromagnetic field analysis	(i) Inexpensive; (ii) Nonintrusive	(i) Sensitive to the noise; (ii) It cannot be used as early FDD	(i) Mechanical and electrical faults
Ultrasound analysis	(i) Effective in low speed bearings; (ii) It deals with low and middle frequency ranges; (iii) High signal to noise ratio	(i) Expensive sensor is required; (ii) Intrusive	(i) Mechanical and electrical faults