Variable predictive model class discrimination using novel predictive models and adaptive feature selection for bearing fault identification

Tang, Tao; Lin, Bo; Liu, Xiaofeng; Sun, Bing; Wei, Daiping

doi:10.1016/j.jsv.2018.03.032

Cited by 27 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method makes RBFNN have excellent recognition performance for bearing fault diagnosis by DE/BBO. Tang et al (2018) proposed an adaptive feature selection method to reduce the dimensions and multiple correlations of the feature space. The method was based on variable predictive model class discrimination (VPMCD), affinity propagation (AP) clustering, RReliefF, sequential forward search, and was combined with RBFNN to construct a new fault diagnosis model variable predictive model class discrimination-radial basis function (VPMCD-RBF) for fault diagnosis of rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis method of rolling bearings based on Welch power spectrum transformation with radial basis function neural network

Jin

Han

Zhang

et al. 2020

Journal of Vibration and Control

View full text Add to dashboard Cite

In the intelligent fault diagnosis of rolling bearings, the high recognition accuracy is hardly achieved when small training samples and strong noise happen. In this article, a novel fault diagnosis method is proposed, that is radial basis function neural network with power spectrum of Welch method. This fault diagnosis model adopts the way of end-to-end operating mode. It takes the original vibration signal (time-domain signal) as input, and Welch method transforms the data from time-domain signals to power spectrums and suppresses high strength noise. Then the results of Welch method are classified by radial basis function neural network. To test the performance of radial basis function neural network with power spectrum of Welch method, the method is compared with some advanced fault diagnosis methods, and the limit performance test for radial basis function neural network with power spectrum of Welch method is carried out to obtain its ultimate diagnosis ability. The results show that the proposed method can realize the high diagnostic precision without the complex feature extraction from the signal. At the same time, in the case of a small amount of training data, this method also can achieve the diagnosis in high precision. Moreover, the anti-noise performance of radial basis function neural network with power spectrum of Welch method is better than the performance of some fault diagnosis methods proposed in recent years.

show abstract

Section: Introductionmentioning

confidence: 99%

An intelligent fault diagnosis method of rolling bearings based on Welch power spectrum transformation with radial basis function neural network

Jin

Han

Zhang

et al. 2020

Journal of Vibration and Control

View full text Add to dashboard Cite

show abstract

“…With timefrequency domain analysis methods, the evolvement of the local frequency components can be obtained [18] [19]. There are many time-frequency domain analysis methods, such as wavelet analysis [20] [21] [22], empirical mode decomposition [23] [24], ReliefF algorithm [25] and other methods. Fault identification is also called fault pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on the Coherent Demodulation Model

2020

View full text Add to dashboard Cite

“…In general, feature selection algorithms include filter models, wrapper models, and embedded models. 19,20,22,23,34,35 Filter models evaluate the general characteristics of the training data to select a feature subset without employing any learning algorithm; thus, it has less computation cost. 23 Nevertheless, it might obtain the feature subsets irrelevant to classes.…”

Section: Introductionmentioning

confidence: 99%

A novel feature selection method to boost variable predictive model–based class discrimination performance and its application to intelligent multi-fault diagnosis

Luo

Yang

Tang

2020

Measurement and Control

View full text Add to dashboard Cite

Effective and efficient incipient fault diagnosis is vital to the maintenance and safe application of large-scale key mechanical system. Variable predictive model–based class discrimination is a recently developed multiclass discrimination method and has been proved to be potential tool for multi-fault detection. However, the vibration signals from dynamic mechanical system always present non-normal distribution so that the original variable predictive model–based class discrimination might produce the inaccurate outcomes. An improved variable predictive model–based class discrimination method is introduced at first in this work. At the same time, variable predictive model–based class discrimination will suffer computation difficulty in the case of high-dimension input features. Therefore, a novel feature selection method based on similarity-fuzzy entropy is presented to boost the performance of the variable predictive model–based class discrimination classifier. In this method, the ideal feature vectors are optimized to acquire more accurate similarity-fuzzy entropies for the input features. And, the one with the largest similarity-fuzzy entropy value is removed to refine input feature subset. Moreover, the optimal input features are repeatedly evaluated using the improved variable predictive model–based class discrimination classifier until the expected results are achieved. Finally, the incipient multi-fault diagnosis model for a hydraulic piston pump is established and verified by experimental test. Some comparisons with commonly used methods were made, and the results indicate that the proposed method is more effective and efficient.

show abstract

Variable predictive model class discrimination using novel predictive models and adaptive feature selection for bearing fault identification

Cited by 27 publications

References 27 publications

An intelligent fault diagnosis method of rolling bearings based on Welch power spectrum transformation with radial basis function neural network

An intelligent fault diagnosis method of rolling bearings based on Welch power spectrum transformation with radial basis function neural network

Rolling Bearing Fault Diagnosis Based on the Coherent Demodulation Model

A novel feature selection method to boost variable predictive model–based class discrimination performance and its application to intelligent multi-fault diagnosis

Contact Info

Product

Resources

About