A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance

Hwang, Chul-Hee; Kang, Myeongsu; Kim, Jong‐Myon

doi:10.9708/jksci.2011.16.12.187

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…생산 공정 자동화와 장비의 대용량 고출력화로 유도 전동기 결함에 의해 발생되는 생산성 저하와 유지보수 비용, 장비 교체 등의 경제 적 손실을 최소화하기 위해 유도 전동기의 관리 및 유지보수 의 중요성이 증대되고 있다. [1][2][3] 유도 전동기의 결함 중 가장 많은 비중을 차지하는 것은 베어링 관련 결함으로, 해당 결함을 사전에 예지하고 적절한 시기에 정비하지 못할 경우 다른 결함도 야기할 수 있는 문제 점을 가지고 있다. 따라서 최근에는 베어링 결함을 조기에 예 지하기 위한 연구가 활발히 진행되고 있다.…”

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Bearing Multi-Faults Detection of an Induction Motor using Acoustic Emission Signals and Texture Analysis

Jang¹,

Kim²

2014

Journal of the Korea Society of Computer and Information

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This paper proposes a fault detection method utilizing converted images of acoustic emission signals and texture analysis for identifying bearing's multi-faults which frequently occur in an induction motor. The proposed method analyzes three texture features from the converted images of multi-faults: multi-faults image's entropy, homogeneity, and energy. These extracted features are then used as inputs of a fuzzy-ARTMAP to identify each multi-fault including outer-inner, inner-roller, and outer-roller. The experimental results using ten times trials indicate that the proposed method achieves 100% accuracy in the fault classification.▸Keywords : Multi-fault detection, induction motor, acoustic emission signals, gray-level co-occurrence matrix, texture analysis, fuzzy ARTMAP•제1저자 : 장원철•교신저자 : 김종면

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Bearing Multi-Faults Detection of an Induction Motor using Acoustic Emission Signals and Texture Analysis

Jang¹,

Kim²

2014

Journal of the Korea Society of Computer and Information

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An Effective Feature Extraction Method for Fault Diagnosis of Induction Motors

Nguyen¹,

Kim²

2013

Journal of the Korea Society of Computer and Information

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This paper proposes an effective technique that is used to automatically extract feature vectors from vibration signals for fault classification systems. Conventional mel-frequency cepstral coefficients (MFCCs) are sensitive to noise of vibration signals, degrading classification accuracy. To solve this problem, this paper proposes spectral envelope cepstral coefficients (SECC) analysis, where a 4-step filter bank based on spectral envelopes of vibration signals is used: (1) a linear•제1저자 : Hung N. Nguyen •교신저자 : 김종면

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Bearing Faults Identification of an Induction Motor using Acoustic Emission Signals and Histogram Modeling

장원철¹,

서준상²,

Kim³

2014

Journal of the Korea Society of Computer and Information

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This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner,•제1저자 : 장원철 •교신저자 : 김종면

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A Study on Robust Feature Vector Extraction for Fault Detection and Classification of Induction Motor in Noise Circumstance

Cited by 4 publications

References 8 publications

Bearing Multi-Faults Detection of an Induction Motor using Acoustic Emission Signals and Texture Analysis

Bearing Multi-Faults Detection of an Induction Motor using Acoustic Emission Signals and Texture Analysis

An Effective Feature Extraction Method for Fault Diagnosis of Induction Motors

Bearing Faults Identification of an Induction Motor using Acoustic Emission Signals and Histogram Modeling

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