A novel scheme for fault detection of reciprocating compressor valves based on basis pursuit, wave matching and support vector machine

被引：59
作者：
Qin, Qiang [1 ]
Jiang, Zhi-Nong [1 ]
Feng, Kun [1 ]
He, Wei [1 ]
机构：
[1] Beijing Univ Chem Technol, Diag & Self Recovery Engn Res Ctr, Beijing 100029, Peoples R China
来源：
MEASUREMENT | 2012年 / 45卷 / 05期
基金：
中国国家自然科学基金; 国家高技术研究发展计划(863计划);
关键词：
Compressor; Fault diagnosis; Basis pursuit; Differential evolution; Wave matching; Support vector machine; DIFFERENTIAL EVOLUTION; DIAGNOSIS;
D O I：
10.1016/j.measurement.2012.02.005
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
A scheme for fault detection of compressor valves based on basis pursuit (BP), wave matching and support vector machine (SVM) is presented. BP is applied to extract the main vibration component in the signal and suppress background noise. Wave matching is a new feature extraction method proposed in this paper. Instead of extracting features through commonly used indicators such as statistic measures or information entropy, wave matching extracts features by matching the vibration signal with parameterized waveform optimized by differential evolution (DE) algorithm. It only produces a small number of features and the features have clear physical meaning. SVM is employed in the fault classification because of its superiority in dealing with small sample problems. The results of real compressor valve signal analysis confirm that the proposed scheme can differentiate compressor valve faults with high accuracy and reliability. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码：897 / 908
页数：12
相关论文
共 50 条
  • [41] A Method for Fault Detection in Wireless Sensor Network Based on Pearson's Correlation Coefficient and Support Vector Machine Classification
    Biswas, Priyajit
    Samanta, Tuhina
    WIRELESS PERSONAL COMMUNICATIONS, 2022, 123 (03) : 2649 - 2664
  • [42] Rolling element bearing fault detection based on the complex Morlet wavelet transform and performance evaluation using artificial neural network and support vector machine
    Malla C.
    Rai A.
    Kaul V.
    Panigrahi I.
    Noise and Vibration Worldwide, 2019, 50 (9-11) : 313 - 327
  • [43] Heart Disease Prediction Based on Age Detection using Novel Logistic Regression over Support Vector Machine
    Karthi, C. B. M.
    Kalaivani, A.
    CARDIOMETRY, 2022, (25): : 1711 - 1717
  • [44] A nonlinear support vector machine-based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process
    Onel, Melis
    Kieslich, Chris A.
    Pistikopoulos, Efstratios N.
    AICHE JOURNAL, 2019, 65 (03) : 992 - 1005
  • [45] A Novel Multi-Class Fault Diagnosis Approach Based on Support Vector Machine of Particle Swarm Optimization and Huffman Tree
    Wu, Fan
    Hu, Weiwei
    Sun, Yufeng
    2015 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM), 2015, : 825 - 829
  • [46] A Novel Hybrid Feature Selection Model for Classification of Neuromuscular Dystrophies Using Bhattacharyya Coefficient, Genetic Algorithm and Radial Basis Function Based Support Vector Machine
    Anand, Divya
    Pandey, Babita
    Pandey, Devendra K.
    INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES, 2018, 10 (02) : 244 - 250
  • [47] Experimental Study on Support Vector Machine-Based Early Detection for Sensor Faults and Operator-Based Robust Fault Tolerant Control
    Deng, Mingcong
    Tanaka, Yuki
    Li, Ximei
    MACHINES, 2022, 10 (02)
  • [48] Novel image matching confidence fusion evaluation algorithm based on Support Vector Machine - art. no. 67881D
    Zou, Lamei
    Cao, Zhiguo
    Zhang, Tianxu
    MIPPR 2007: PATTERN RECOGNITION AND COMPUTER VISION, 2007, 6788 : D7881 - D7881
  • [49] A combined convolutional neural network model and support vector machine technique for fault detection and classification based on electroluminescence images of photovoltaic modules
    Et-taleby, Abdelilah
    Chaibi, Yassine
    Allouhi, Amine
    Boussetta, Mohammed
    Benslimane, Mohamed
    SUSTAINABLE ENERGY GRIDS & NETWORKS, 2022, 32
  • [50] Fault Detection of Wheelset Bearings through Vibration-Sound Fusion Data Based on Grey Wolf Optimizer and Support Vector Machine
    Wang, Tianhao
    Meng, Hongying
    Zhang, Fan
    Qin, Rui
    TECHNOLOGIES, 2024, 12 (09)
12345