A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox

Li, Bing; Zhang, Peilin; Tian, Hui; Mi, Shuang-shan; Li, Dongsheng; Ren, Guoquan

doi:10.1016/j.eswa.2011.02.008

Cited by 96 publications

(52 citation statements)

References 46 publications

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“…The combined method is used to diagnose faults [8]. Li et al combined the transformation method, NMF method, mutual information method, and multiobjective evolutionary algorithm [9]. Li et al also, respectively, combined generalized transform method to NMF method and 2DNMF method [10,11].…”

Section: Introducementioning

confidence: 99%

The Research on Information Representation of Φ-OTDR Distributed Vibration Signals

Wang

et al. 2017

Journal of Sensors

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This paper mainly focuses on the representable problem of Φ-OTDR distributed vibration signals. The research included a signal extraction part and a signal representation part. Firstly, in order to extract the better Φ-OTDR signal, the time-domain data should be fully preserved. The 2D-TESP method is used to extract data in this paper. There are 29 characters in the traditional TESP method. The characters' number is reduced from 29 to 13 and the characters' dimension is expanded from 1 to 2 in the 2D-TESP method. Secondly, in order to represent Φ-OTDR signal better, the characteristics of Φ-OTDR data and damped vibration signals are combined in the paper. The EMD method and the NMF method are combined to form the new method in the paper. Some parameters in the proposed method are optimized and adjusted by GA method. After Φ-OTDR data is represented by the proposed method, there is excellent performance both on the length dimension and on the time dimension. Lastly, some experiments are carried out according to the physical truth in this paper. The experiments are carried out in the semianechoic room. The methods of the paper have better performance. The methods are proved to be effective through these experiments.

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Section: Introducementioning

confidence: 99%

The Research on Information Representation of Φ-OTDR Distributed Vibration Signals

Wang

et al. 2017

Journal of Sensors

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“…1 Gearbox defect may cause failure of whole system, leading to significant economic losses, costly downtime, and even catastrophic damage. Thus, online monitoring and fault diagnosis of gearboxes are of great importance to achieve a high degree of availability, reliability, and operation safety.…”

Section: Introductionmentioning

confidence: 99%

Integrative intrinsic time-scale decomposition and hierarchical temporal memory approach to gearbox diagnosis under variable operating conditions

Duan

Yao

Wang

et al. 2016

Advances in Mechanical Engineering

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Gearbox diagnosis under stationary operating conditions has been extensively investigated; however, variable operating conditions such as load and speed changes play important roles in affecting the accuracy of gearbox diagnosis. This article presents an integrative approach of intrinsic time-scale decomposition and hierarchical temporal memory for gearbox diagnosis under variable operating conditions. A total of two modules are emphasized including a feature extraction method and an integrative feature fusion and classification model. Intrinsic time-scale decomposition method is investigated to extract the gearbox features which are insensitive to variable operating conditions, and its performance overcomes the commonly used empirical mode decomposition in terms of decomposition result and computational efficiency. Hierarchical temporal memory integrates feature fusion and pattern classification in one model to autonomously diagnose gearbox defect. Performance comparison among the presented method, back-propagation neural network, support vector machine, and fuzzy c-means clustering using experimental data demonstrate the effectiveness of the presented method.

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“…Many researchers have presented different algorithms for feature selection and fault classification. Li, Zhang, Tian, Mi, Liu and Ren (2011) proposed a novel feature extraction and selection scheme for hybrid fault diagnosis of gearbox based on S transform, non-negative matrix factorization (NMF), mutual information and multi-objective evolutionary algorithms. A two stage feature selection approach combining filter and wrapper techniques based on mutual information and non-dominated sorting genetic algorithms II (NSGA-II) was presented to get a more compact feature subset for accurate classification of hybrid faults of gearbox.…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis system for newly assembled transmission

Shang

Zhou

Yuan

2014

Expert Systems with Applications

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A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox

Cited by 96 publications

References 46 publications

The Research on Information Representation of Φ-OTDR Distributed Vibration Signals

The Research on Information Representation of Φ-OTDR Distributed Vibration Signals

Integrative intrinsic time-scale decomposition and hierarchical temporal memory approach to gearbox diagnosis under variable operating conditions

An intelligent fault diagnosis system for newly assembled transmission

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