A New Support Vector Regression Model for Equipment Health Diagnosis with Small Sample Data Missing and Its Application

Actually, it is difficult to obtain a large number of sample data due to equipment failure, and small sample data may also be missing. This paper proposes a novel small sample data missing filling method based on support vector regression (SVR) and genetic algorithm (GA) to improve equipment health diagnosis effect. First, the genetic algorithm is used to optimize support vector regression, and a new method GA-SVR can be proposed. The GA-SVR model is trained by using other data of the variable to which the missing data belongs, and the single-variable prediction method can be obtained. The correlation analysis is used to reconstruct the training set, and the GA-SVR is trained by using the data of the variables related to the missing data to obtain the multivariate prediction method. Then, the dynamic weight is presented to combine the single-variable prediction method with the multiple-variable prediction method based on certain principles, and the missing data are filled with the combined prediction methods. The filled data are used as input of GA-SVM to diagnose equipment failure. Finally, a case study is given to verify the applicability and effectiveness of the proposed method.

[1]  Ruijin Liao,et al.  A New Support Vector Machine Model Based on Improved Imperialist Competitive Algorithm for Fault Diagnosis of Oil-immersed Transformers , 2017 .

[2]  David He,et al.  A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology , 2007 .

[3]  Xuejun Li,et al.  Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm , 2015 .

[4]  Xinping Yan,et al.  A Belief Rule-Based Expert System for Fault Diagnosis of Marine Diesel Engines , 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[5]  Fan Yang,et al.  Support vector machine with genetic algorithm for machinery fault diagnosis of high voltage circuit breaker , 2011 .

[6]  Q. Zhang,et al.  A HYBRID APPROACH TO HYDRAULIC VANE PUMP CONDITION MONITORING AND FAULT DETECTION , 2006 .

[7]  Sheng-wei Fei,et al.  Fault diagnosis of power transformer based on support vector machine with genetic algorithm , 2009, Expert Syst. Appl..

[8]  Weiwei Qian,et al.  A novel class imbalance-robust network for bearing fault diagnosis utilizing raw vibration signals , 2020 .

[9]  Shuai Yang,et al.  Transfer Learning Based Fault Diagnosis with Missing Data Due to Multi-Rate Sampling , 2019, Sensors.

[10]  Huang Xinyi,et al.  Fault diagnosis of transformer based on modified grey wolf optimization algorithm and support vector machine , 2020 .

[11]  R. S. Gunerkar,et al.  Fault diagnosis of rolling element bearing based on artificial neural network , 2019, Journal of Mechanical Science and Technology.

[12]  Zhengdao Zhang,et al.  Fault detection and diagnosis for missing data systems with a three time-slice dynamic Bayesian network approach , 2014 .

[13]  Li,et al.  A fault diagnosis method of reciprocating compressor based on sensitive feature evaluation and artificial neural network , 2015 .

[14]  Minping Jia,et al.  A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing , 2018, Neurocomputing.

[15]  Yan Han,et al.  An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes , 2019, Comput. Ind..

[16]  Zakir Husain,et al.  Fuzzy Logic Expert System for Incipient Fault Diagnosis of Power Transformers , 2018, International Journal on Electrical Engineering and Informatics.

[17]  Yonghong Liu,et al.  Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge , 2015 .

[18]  Xianmin Zhang,et al.  Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions , 2020, Knowl. Based Syst..

[19]  Wentao Mao,et al.  Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine , 2017 .

[20]  Toufik Berredjem,et al.  Bearing faults diagnosis using fuzzy expert system relying on an Improved Range Overlaps and Similarity method , 2018, Expert Syst. Appl..

[21]  Lin Lin,et al.  A novel gas turbine fault diagnosis method based on transfer learning with CNN , 2019, Measurement.

[22]  Xu Li,et al.  Machinery fault diagnosis with imbalanced data using deep generative adversarial networks , 2020 .

[23]  Rui Yang,et al.  Rotating Machinery Fault Diagnosis Using Long-short-term Memory Recurrent Neural Network , 2018 .

[24]  Ahmed Cheriet,et al.  Expert System Based on Fuzzy Logic: Application on Faults Detection and Diagnosis of DFIG , 2018 .

[25]  Pengcheng Jiang,et al.  Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network , 2019, Comput. Ind..