ART–KOHONEN neural network for fault diagnosis of rotating machinery

Yang, Bo‐Suk; Han, Tian; An, Jing-Long

doi:10.1016/s0888-3270(03)00073-6

Cited by 174 publications

(82 citation statements)

References 5 publications

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“…In order to maintain a better diagnosis performance, the selection of sensitive features which provide characteristic information for the diagnosis system needs to be done, and irrelevant or redundant features must be removed. Here, a new parameter evaluation technique based on distance discriminant technique [19] is proposed, which includes the similarity formula (3). The feature selection process can be described as follows.…”

Section: Features Selectionmentioning

confidence: 99%

Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

Yang

Zhang

et al. 2017

Mathematical Problems in Engineering

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This paper presents a novel method for fault diagnosis based on an improved adaptive resonance theory (ART) neural network and ensemble technique. The method consists of three stages. Firstly, the improved ART neural network is comprised of the soft competition technique based on fuzzy competitive learning (FCL) and ART based on Yu's norm, the neural nodes in the competition layer are trained according to the degree of membership between the mode node and the input, and then fault samples are classified in turn. Secondly, with the distance evaluation technique, the optimal features are obtained from the statistical characteristics of original signals and wavelet coefficients. Finally, the optimal features are input into the neural network ensemble (NNE) based on voting method to identify the different fault categories. The proposed method is applied to the fault diagnosis of rolling element bearings, and testing results show that the neural network ensemble can reliably classify different fault categories and the degree of faults, which has a better classification performance compared with the single neural network.

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Section: Features Selectionmentioning

confidence: 99%

Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

Yang

Zhang

et al. 2017

Mathematical Problems in Engineering

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“…The corresponding effectiveness factor of the jth (j =1 ,2 ,⋯, 95) feature is denoted as α j (see Ref. [19] for more details on the distance-based evaluation approach). The features with a larger effectiveness factor are more sensitive to these particular fault types.…”

Section: Tkdmentioning

confidence: 99%

Fault Diagnosis and Health Assessment for Rotating Machinery Based on Kernel Density Estimation and Kullback-Leibler Divergence

Yu¹,

Yan²,

Zhang³

2017

Fault Diagnosis and Detection

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To avoid severe damages and unexpected shutdowns, fault diagnosis and health assessment of rotating machinery have received considerable attention in recent years. On the other hand, as a great amount of data become acquirable and accessible in industry, data-driven tools have become an emerging research area, acting as a complement to the model-based (or physics-based) fault diagnosis and health assessment methods. In this chapter, based on the kernel density estimation (KDE) and the Kullback-Leibler divergence (KLID), a new data-driven fault diagnosis approach and a new health assessment approach are introduced. By utilizing the KDE, the statistical distribution of selected features can be readily estimated without assuming any parametric family of distributions, whereas the KLID is able to quantify the discrepancy between two probability distributions of selected features. An integrated Kullback-Leibler divergence, which aggregates the KLID of all the selected features, is introduced to discriminate various fault types or health status of rotating machinery. The effectiveness of the proposed approaches is demonstrated through three case studies of fault diagnosis and health assessment of rotating machinery.

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“…But, applying too many feature parameters can be a burden to networks since calculating the results can be very timeconsuming. A feature extraction technique [15] is applied here to extract some parameters which can properly represent the fault features taken from total parameters.…”

Section: Feature Extractionmentioning

confidence: 99%

Improving Stator Winding Fault Diagnosis in Induction Motor using Dempster-Shafer Theory

Gholamshahi¹,

Poshtan²,

Poshtan³

2014

ELELIJ

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Due to the important role of induction motors in industry, reliability in fault detection of these motors is of great importance. In this paper, a method is proposed for increasing the reliability of stator winding fault diagnosis using Dempster-Shafer theory. Out of the measured current and vibration signals, some features are extracted and selected according to the method, and a neural network based on DempsterShafer theory is trained by the selected features. The proposed method is then tested for various fault.Finally it is shown how data fusion can increase the accuracy of fault diagnosis. The system performance is also evaluated on a laboratory motor showing satisfactory results.

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ART–KOHONEN neural network for fault diagnosis of rotating machinery

Cited by 174 publications

References 5 publications

Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

Based on Soft Competition ART Neural Network Ensemble and Its Application to the Fault Diagnosis of Bearing

Fault Diagnosis and Health Assessment for Rotating Machinery Based on Kernel Density Estimation and Kullback-Leibler Divergence

Improving Stator Winding Fault Diagnosis in Induction Motor using Dempster-Shafer Theory

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