Bearing fault identification of three-phase induction motors bases on two current sensor strategy

Abstract

Three-phase induction motors are the most commonly used devices for electromechanical energy conversion. This study proposes an alternative approach for identifying bearing faults in induction motors, using two current sensors and a pattern classifier, based on artificial neural networks. To validate the methodology, results are given from experiments carried out on a test bench where the motors operate with different types of bearing faults, under varying conditions of load torque and voltage unbalance. This paper also provides the comparative performance of neural network and random forest classifiers. This study also presents an analysis of the current signals in the time domain, applied to different neural structures.

Acoustic-Based Fault Diagnosis of Commutator Motor

“…, where FS-A1, FS-A2, FS-A3, FS-A4-denoted 4 frequency spectra of state A, FS-B1, FS-B2, FS-B3, FS-B4-denoted 4 frequency spectra of state B, FS-C1, FS-C2, FS-C3, FS-C4-denoted 4 frequency spectra of state C, FS-D1, FS-D2, FS-D3, FS-D4-denoted 4 frequency spectra of state D, FS-E1, FS-E2, FS-E3, FS-E4-denoted 4 frequency spectra of state E. Next, 40 differences between frequency spectra are computed: The MSAF-15-MULTIEXPANDED-8-GROUPS found 28 essential frequency components: 48,50,79,81,97,101,128,157,159,1469,1471,1672,1926,1927,1934,1935,1939,1942,1953,1957,1958,1961,1978,2038,2039,2042 Found essential frequency components were classified by the NN classifier [35,36], NM classifier, SOM [37], BNN [38][39][40][41][42][43][44]. There was possibility to use another classifier such as naive Bayes, support vector machine [45][46][47], linear discriminant analysis [48], fuzzy classifiers [49,50], and fuzzy c-means clustering [51].…”

“…x -cmbrc (1) where = [x36, x37, x59, x60, x72, x75, x95, x117, x118, x1092, x1094, x1243, x1432, x1433, x1438, x1439, x1442, x1444, x1452, x1455, x1456, x1458, x1471, x1515, x1516, x1518, x1531, x1894] and training feature vector cmbrc = [cmbrc36, cmbrc37, cmbrc59, cmbrc60, cmbrc72, cmbrc75, cmbrc95, Found essential frequency components were classified by the NN classifier [35,36], NM classifier, SOM [37], BNN [38][39][40][41][42][43][44]. There was possibility to use another classifier such as naive Bayes, support vector machine [45][46][47], linear discriminant analysis [48], fuzzy classifiers [49,50], and fuzzy c-means clustering [51].…”

“…It has been used for many applications, such as speaker recognition, image recognition, signal recognition, control, prediction, computer games, robots, etc. The applied backpropagation algorithm has been described in the literature [38][39][40][41][42][43][44]. The author implemented a backpropagation neural network (Figure 25).…”

“…The values of output neurons were 10000-healthy CM, 01000-CM with shorted stator coils, 00100-CM with broken rotor coil, 00010-CM with broken tooth on sprocket, and 00001-CM with damaged gear train. More information about BNN can be found in following papers [38][39][40][41][42][43][44].…”

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

“…84–87 Three main types of analysis are used in that studies including, electrical based analysis, oil and chemical based analysis, and finally, mechanical analysis. 88 In more details, twelve analysis based on electrical, mechanical, and chemical are found in the literature, including, vibration, 89–92 noise, 93,94 radio-frequency (RF), 95–98 infrared, 99–104 current and voltage, 105,106 electromagnetic field, 107–109 oil, 110–113 pressure, 114–118 ultrasound, 119–121 temperature, 122–126 and sound and acoustic emission (AE) analysis. Table 1 highlight main analysis used in CM and FD for IM.…”