Application of intelligent tools to detect and classify broken rotor bars in three‐phase induction motors fed by an inverter

Godoy, Wagner Fontes; Silva, Ivan Nunes da; Goedtel, Alessandro; Palácios, Rodrigo Henrique Cunha; Lopes, Tiago Drummond

doi:10.1049/iet-epa.2015.0469

Cited by 60 publications

(39 citation statements)

References 43 publications

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“…, 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].…”

Section: Components (Fs-a1 Fs-b1 Fs-c1 Fs-d1 Fs-e1) (Fs-a2 Fs-bmentioning

confidence: 99%

Acoustic-Based Fault Diagnosis of Commutator Motor

Głowacz

2018

Electronics

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In the paper, the author presents acoustic-based fault diagnosis of a commutator motor (CM). Five states of the commutator motor were considered: healthy commutator motor, commutator motor with broken rotor coil, commutator motor with shorted stator coils, commutator motor with broken tooth on sprocket, commutator motor with damaged gear train. A method of feature extraction MSAF-15-MULTIEXPANDED-8-GROUPS (Method of Selection of Amplitudes of Frequency Multiexpanded 8 Groups) was described and implemented. Classification methods, such as nearest neighbour (NN), nearest mean (NM), self-organizing map (SOM), backpropagation neural network (BNN) were used for acoustic analysis of the commutator motor. The paper provides results of acoustic analysis of the commutator motor. The results had a good recognition rate. The results of acoustic analysis were in the range of 88.4–94.6%. The NM classifier and the MSAF-15-MULTIEXPANDED-8-GROUPS provided TERCM = 94.6%.

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Section: Components (Fs-a1 Fs-b1 Fs-c1 Fs-d1 Fs-e1) (Fs-a2 Fs-bmentioning

confidence: 99%

Acoustic-Based Fault Diagnosis of Commutator Motor

Głowacz

2018

Electronics

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“…They have been extensively used to monitor broken bars [6]− [11], eccentricity [12]− [14], and bearingrelated faults [7]− [10], [15]− [21]. Similarly, the use of support vector machines to diagnose motors faults has been widely reported in literature: for broken bars [6], [22]− [25], bearings [19], [26]− [33], and eccentricity [29].…”

Section: Index Terms-diagnosticmentioning

confidence: 99%

“…K-nearest neighbors has been applied to diagnose broken bars [6] and bearing faults [34]. Farajzadeh-Zanjani et al [35] use a supervised fuzzy-neighborhood density-based clustering to diagnose bearing faults.…”

Section: Index Terms-diagnosticmentioning

confidence: 99%

Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

García-Escudero

Duque-Pérez

Fernandez-Temprano

et al. 2017

IEEE Trans. on Ind. Applicat.

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Abstract-A considerable amount of papers has been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper, a new method is proposed, which is based on robust statistical techniques applied in quality control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques, which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked: a broken bar, a faulty bearing, and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults.

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“…Furthermore, the fault diagnosis method based on the support vector machine (SVM) is employed in [37,38] for motor faults. Moreover, classifiers based on C4.5, K-nearest neighbors (k-NNs), and multilayer perceptron (MLP) are discussed in [39,40] to recognize faults.…”

Section: Introductionmentioning

confidence: 99%

Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

et al. 2019

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Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs.

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Application of intelligent tools to detect and classify broken rotor bars in three‐phase induction motors fed by an inverter

Cited by 60 publications

References 43 publications

Acoustic-Based Fault Diagnosis of Commutator Motor

Acoustic-Based Fault Diagnosis of Commutator Motor

Robust Detection of Incipient Faults in VSI-Fed Induction Motors Using Quality Control Charts

Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

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