Rolling bearing fault detection of electric motor using time domain and frequency domain features extraction and ANFIS

Abstract

Demands for various products, higher qualities, reduction of costs and competitiveness, have resulted in the use of intelligent fault detection systems. Bearing fault diagnosis as a major component of the electric motors has had an essential role in the operation of production units' reliability. In addition, vibration analysis is one of the most powerful tools in diagnostics. Advances in signal processing technology and electrical equipment have developed a machinery condition monitoring for defect detection. This study has used the extracted features of vibration signals and the adaptive neuro-fuzzy interface system (ANFIS) network proposing a structure for fault detection and diagnosis of rolling bearings. Time-domain and frequency-domain statistical characteristics have been extracted fault information from vibration signals. Besides, the test data sets are presented to the ANFIS network. Simulation results indicated that the performance of the ANFIS network is acceptable. The results reveal that this method has more accuracy and better classification performance in comparison with other methods proposed in the literature.

A New Hybrid Diagnosis of Bearing Faults Based on Time-Frequency Images and Sparse Representation

“…The mathematical model of ELM, which has L nodes in the hidden layer and an activation function g(x), is as follows. In (12), H represents the output of the hidden layer, and the output matrix of the classifier is given by T. The two matrices are given in (13) and 14…”

“…These oscillations appear as a pattern in vibration signals. Time and frequency analysis of vibration signals were performed to determine ball, inner, and outer ring faults [10][11][12][13][14]. Multiple domain features were obtained from vibration signals and the faults occurred on rotating machinery were detected [15].…”

“…Although the method offers good results in inner and outer race faults, it obtains low performance in ball faults. The features obtained in TF domain were given to an Adaptive Neuro-fuzzy Inference System (ANFIS) system, and bearing faults were diagnosed [12]. In recent years, the development of deep learning methods has facilitated the applications of the fault diagnosis.…”

Optimised approach of feature selection based on genetic and binary state transition algorithm in the classification of bearing fault in BLDC motor

“…The fault diagnosis model usually has three important parts: (i) feature extraction, (ii) feature selection (FS), and (iii) feature classification. This model makes use of many characteristic features extracted from measurement signals in time and frequency domains [10–13]. Based on the second approach, a bearings fault diagnosis model in BLDC motors has been investigated in this work.…”

Failure Diagnosis of Demagnetization in Interior Permanent Magnet Synchronous Motors Using Vibration Characteristics

“…There have been several studies on failure diagnosis in electric motors. For example, diagnosis of bearing faults has been reported [3][4][5][6]; diagnosis of unbalanced rotors or broken rotor bars in induction motors has been reported [7][8][9][10][11][12][13]; and diagnosis for DC commutator motors has been reported [14,15]. For PM motors, Rotor Position Error, Winding Short-Circuit Fault, and Rotor Eccentricity, among others, have been reported [16][17][18][19][20].…”