Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals

Hwang, Don-Ha; Youn, Young-Woo; Sun, Jong-Ho; Choi, Kyeong-Ho; Lee, Jong-Ho; Kim, Yong‐Hwa

doi:10.5370/jeet.2015.10.4.1558

Cited by 34 publications

(20 citation statements)

References 22 publications

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“…The problem of classification was already discussed in literature [29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45]. Neural networks were described in many scientific articles [37][38][39][40][41][42].…”

Section: Nearest Neighbour Classifiermentioning

confidence: 99%

“…Neural networks were described in many scientific articles [37][38][39][40][41][42]. However the author decided to use the Nearest Neighbour classifier [2,36,43,44] and Support Vector Machine [45], because they had high efficiency of recognition of similar problems. The Nearest Neighbour classifier was very good to classify a high dimensional feature vector.…”

Section: Nearest Neighbour Classifiermentioning

confidence: 99%

“…The advantages of SVM was: it used support vectors in the decision function (memory efficient), SVM used different Kernel functions for the decision function, classifier has high efficiency in high dimensional spaces. More about SVM was available in the literature [45].…”

Section: Support Vector Machinementioning

confidence: 99%

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Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED

Głowacz

2016

Archives of Electrical Engineering

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An early fault diagnostic method of Direct Current motors was presented in this article. The proposed method used acoustic signals of a motor. A method of feature extraction called MSAF-RATIO30-EXPANDED (method of selection of amplitudes of frequencies -ratio 30% of maximum of amplitude -expanded) was presented and implemented. An analysis of proposed method was carried out for early fault states of a real DC motor. Four following states of the DC motor were measured and analyzed: the healthy DC motor, DC motor with 3 shorted rotor coils, DC motor with 6 shorted rotor coils, DC motor with a broken coil. Measured states were caused by natural degradation of the DC motor. The obtained results of analysis were good. The presented early fault diagnostic method can be used for protection of DC motors.

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Section: Nearest Neighbour Classifiermentioning

confidence: 99%

Section: Nearest Neighbour Classifiermentioning

confidence: 99%

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Fault diagnostics of DC motor using acoustic signals and MSAF-RATIO30-EXPANDED

Głowacz

2016

Archives of Electrical Engineering

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“…The HS-TBFO technique is applied to produce an Optimal Test by identifying the maximum number of faults covered with minimum test cases. Let us consider the vector array "VA" of "n" elements with progressive phase excitation [18] be formulated as given below.…”

Section: Bacterial Foraging Optimization Techniquementioning

confidence: 99%

“…Though, the original voltage magnitude of the faulty phases is decreased quickly, but the results are comparatively low in EMTPRV model. With the purpose of detecting bearing faults, a two-stage diagnosis method for fault and fault-severity detections based on the FFT and SVMs is designed in [18]. But, Peak-to-average ratio does not perform optimally.…”

Section: Introductionmentioning

confidence: 99%

An Effective Detection of Inrush and Internal Faults in Power Transformers Using Bacterial Foraging Optimization Technique

Gopila¹,

Gnanambal²

2016

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Power transformers in transmission network are utilized for increasing or decreasing the voltage level. Power Transformers fail to connect directly to the consumers that result in the less load fluctuations. Power transformer operation under any abnormal condition decreases the lifetime of the transformer. Power Transformer protection from inrush and internal fault is critical issue in power system because the obstacle lies in the precise and swift distinction between them. Due to the limitation of heterogeneous resources, occurrence of fault poses severe problem. Providing an efficient mechanism to differentiate between faults (i.e. inrush and internal) is the key for efficient information flow. In this paper, the task of detecting inrush and internal fault in power transformers is formulated as an optimization problem which is solved by using Hyperbolic S-Transform Bacterial Foraging Optimization (HS-TBFO) technique. The Gaussian Frequencybased Hyperbolic S-Transform detects the faults at much earlier stage and therefore minimizes the computation cost by applying Cosine Hyperbolic S-Transform. Next, the Bacterial Foraging Optimization (BFO) technique has been proposed and has demonstrated the capability of identifying the maximum number of faults covered with minimum test cases and therefore improving the fault detection efficiency in a wise manner. The HS-TBFO technique is evaluated and validated in various simulation test cases to detect inrush and internal fault in a significant manner. This HS-TBFO technique is investigated based on three phase power transformer embedded in a power system fed from both ends. Results have confirmed that the HS-TBFO technique is capable of categorizing the inrush and internal faults by identifying maximum number of faults with minimum computation cost as compared to the state-of-the-art works.

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