Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference

Raj, Ananya; Muthialu, Nagarajan

doi:10.1109/tie.2012.2188259

Cited by 141 publications

(73 citation statements)

References 14 publications

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“…The accuracy obtained from the proposed method gives the best accuracy for group A, and it is equivalent to the best presented. This is in corroboration with earlier reporting's [18,21]. Specifically datasets A-II, which is a four class classification for FD of 7 being, irrespective of load is implemented by authors in [21] excluding load 0HP condition.…”

Section: Discussionsupporting

confidence: 87%

“…This is in corroboration with earlier reporting's [18,21]. Specifically datasets A-II, which is a four class classification for FD of 7 being, irrespective of load is implemented by authors in [21] excluding load 0HP condition. Further WPD for feature extraction and mRMR for feature selection and DE-EAM for classification are employed and average accuracy of 96.1% is obtained.…”

Section: Discussionsupporting

confidence: 87%

“…Overall 63 feature set combinations from 6 features have been employed for bearing fault diagnosis of 5 groups of data involving 15 datasets which has been drawn in combinations of location of fault and load condition. Though condition monitoring schemes of bearing faults involve Feature extraction, feature selection, features reduction and classification processes which will be handled by various methodologies like WT for feature extraction and minimum-redundancy maximum-relevancy method for feature selection and differential evolution algorithm for classification [21] and spectrum imaging has been implemented for feature extraction and enhancement [22] in previous works. The present work focuses on the simplest scheme development to serve the purpose;…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

Nayana¹,

Tjprc²

2018

IJMPERD

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Section: Discussionsupporting

confidence: 87%

Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

Nayana¹,

Tjprc²

2018

IJMPERD

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“…Morphological filter can be employed to decompose a signal into several physical components according to the geometric characteristics of a certain structural element (SE). Recently MM has been introduced to vibration signal analysis [20] and used as noise filter [21,22] or feature extractor [23,24] in bearing fault detection. These studies have demonstrated the effectiveness of MM method in bearing fault detection.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

Lin

Shangfei

et al. 2017

Shock and Vibration

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The fault signals of rolling element bearing are often characterized by the presence of periodic impulses, which are modulated high-frequency harmonic components. The features of early fault in rolling bearing are very weak, which are often masked by background noise. The impulsiveness of the vibration signal has affected the identification of characteristic frequency for the early fault detection of the bearing. In this paper, a novel approach based on morphological operators is presented for impulsive signal extraction of early fault in rolling element bearing. The combination Top-Hat (CTH) is proposed to extract the impulsive signal and enhance the impulsiveness of the bearing fault signal, and the envelope analysis is applied to reveal the fault-related signatures. The impulsive extraction performance of the proposed CTH is compared with that of finite impulse response filter (FIR) by analyzing the simulated bearing fault signals, and the result indicates that the CTH is more effective in extracting impulsive signals. The method is evaluated using real fault signals from defective bearings with early rolling element fault and early fault located on the outer race. The results show that the proposed method is able to enhance the impulsiveness of early bearing fault signals.

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“…Recently, advanced signal processing has been widely applied for noise cancelation [18] and deriving nonlinear characteristic fault signatures [19]. Different probabilistic models [8], [20] and high-resolution frequency analysis [21] have been applied to obtain reliable fault signatures.…”

Section: Introductionmentioning

confidence: 99%

Iterative Condition Monitoring and Fault Diagnosis Scheme of Electric Motor for Harsh Industrial Application

Choi

Pazouki

Baek

et al. 2015

IEEE Trans. Ind. Electron.

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This paper presents a robust diagnosis technique by iteratively analyzing the pattern of multiple fault signatures in a motor current signal. It is mathematically and experimentally proved that the proposed diagnosis algorithm provides highly accurate monitoring performance while minimizing both false detection and miss detection rate under high noise and nonlinear machine operating condition. These results are verified on a digital-signal-processor-based motor drive system where motor control and fault diagnosis are performed in real time.

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Early Classification of Bearing Faults Using Morphological Operators and Fuzzy Inference

Cited by 141 publications

References 14 publications

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

Effective Time Domain Features for Identification of Bearing Fault using LDA and NB Classifiers

A Novel Approach of Impulsive Signal Extraction for Early Fault Detection of Rolling Element Bearing

Iterative Condition Monitoring and Fault Diagnosis Scheme of Electric Motor for Harsh Industrial Application

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