Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems
Signal Processing for Fault Detection and Diagnosis in Electric Machines and Systems
1 Parametric signal processing approach
1.1 Fault effects on intrinsic parameters of electromechanical systems
1.1.1 Main failures and occurrence frequency
Electric Motor Fault Causes
1.1.2 Origins and consequences
Electric Motor Fault Causes
1.1.3 Condition-based maintenance
Fault Detection Frameworks
1.1.4 Motor current signature analysis
1.2 Fault features extraction techniques
1.2.1 Introduction
1.2.2 Stator current model under fault conditions
1.2.3 Non-parametric spectral estimation techniques
1.2.4 Subspace spectral estimation techniques
1.2.5 ML-based approach
1.3 Fault detection and diagnosis
1.3.1 Artificial intelligence techniques briefly
1.3.2 Detection theory-based approach
1.3.3 Simulation results
1.4 Some experimental results
1.4.1 Experimental set-up description
1.4.2 Eccentricity fault detection
1.4.3 Bearing fault detection
1.4.4 Broken rotor bars fault detection
2 The signal demodulation techniques
2.1 Introduction
2.2 Brief status on demodulation techniques as a fault detector
2.2.1 Mono-component and multicomponent signals
2.2.2 Demodulation techniques
2.3 Synchronous demodulation
2.4 Hilbert transform
2.5 Teager–Kaiser energy operator
2.6 Concordia transform
2.7 Fault detector
2.7.1 Fault detector based on HT and TKEO demodulation
2.7.2 Fault detector after CT demodulation
2.7.3 Synthetic signals
2.8 EMD method
2.9 Ensemble EMD principle
2.10 EEMD-based notch filter
2.10.1 Statistical distance measurement
2.10.2 Dominant-mode cancellation
2.10.3 Fault detector based on EEMD demodulation
2.10.4 Synthetic signals
3 Kullback–Leibler divergence for incipient fault diagnosis
4 Higher-order spectra
5 Fault detection and diagnosis based on principal component analysis