Fault diagnosis based on Fisher discriminant analysis and support vector machines

A sparse version of Kernel Fisher Discriminant Analysis using an approach based on Matching Pursuit (MPKFDA) has been shown to be competitive with Kernel Fisher Discriminant Analysis and the Support Vector Machines on publicly available datasets, with additional experiments showing that MPKFDA on average outperforms these algorithms in extremely high dimensional settings. In (nearly) all cases, the resulting classifier was sparser than the Support Vector Machine. Natural questions that arise are what is the relative importance of the use of the Fisher criterion for selecting bases and the deflation step? Can we speed the algorithm up without degrading performance? Here we analyse the algorithm in more detail, providing alternatives to the optimisation criterion and the deflation procedure of the algorithm, and also propose a stagewise version. We demonstrate empirically that these alternatives can provide considerable improvements in the computational complexity, whilst maintaining the performance of the original algorithm (and in some cases improving it).

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Application of nonlinear feature extraction and least square support vector machines for fault diagnosis of chemical process

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.00236 ◽

2010 ◽

Vol 30 (1) ◽

pp. 236-239 ◽

Cited By ~ 1

Author(s):

Liang XU

Keyword(s):

Feature Extraction ◽

Fault Diagnosis ◽

Support Vector Machines ◽

Chemical Process ◽

Least Square ◽

Support Vector ◽

Vector Machines ◽

Nonlinear Feature Extraction ◽

Nonlinear Feature

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Optimal multi-kernel local fisher discriminant analysis for feature dimensionality reduction and fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x211009335 ◽

2021 ◽

pp. 1748006X2110093

Author(s):

Qing Zhang ◽

Heng Li ◽

Xiaolong Zhang ◽

Haifeng Wang

Keyword(s):

Fault Diagnosis ◽

Discriminant Analysis ◽

Feature Space ◽

High Dimensional ◽

Fisher Discriminant Analysis ◽

Vibration Signals ◽

Fisher Discriminant ◽

Local Fisher Discriminant Analysis ◽

Diagnosis Accuracy ◽

Diagnosis Model

To achieve a more desirable fault diagnosis accuracy by applying multi-domain features of vibration signals, it is significative and challenging to refine the most representative and intrinsic feature components from the original high dimensional feature space. A novel dimensionality reduction method for fault diagnosis is proposed based on local Fisher discriminant analysis (LFDA) which takes both label information and local geometric structure of the high dimensional features into consideration. Multi-kernel trick is introduced into the LFDA to improve its performance in dealing with the nonlinearity of mapping high dimensional feature space into a lower one. To obtain an optimal diagnosis accuracy by the reduced features of low dimensionality, binary particle swarm optimization (BPSO) algorithm is utilized to search for the most appropriate parameters of kernels and K-nearest neighbor (kNN) recognition model. Samples with labels are used to train the optimal multi-kernel LFDA and kNN (OMKLFDA-kNN) fault diagnosis model to obtain the optimal transformation matrix. Consequently, the trained fault diagnosis model implements the recognition of machinery health condition with the most representative feature space of vibration signals. A bearing fault diagnosis experiment is conducted to verify the effectiveness of proposed diagnostic approach. Performance comparison with some other methods are investigated, and the improvement for fault diagnosis of the proposed method are confirmed in different aspects.

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