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Fault diagnosis of rolling bearing based on PSO and continuous Gaussian mixture HMM

Liao GuangchunZhu HaipingLiu KangjunLiao Jiawei

Huazhong University of Science & Technology

摘要:As the hidden Markov model(HMM) has a strong ability of time sequence modeling,the continuous Gaussian mixture HMM is used to establish a model base of the rolling bearing fault.An adaptive particle swarm optimization(APSO) with extremum disturbed operator and dynamic change of inertia weights is introduced to the traditional training algorithm for solving the local extremum problem.The vibration signal is collected for extracting 12 order LPC coefficients as a feature vector through the dispose of adding window.In the given feature vector,the HMM is built for bearing fault condition monitoring and fault diagnosis.Then,different fault conditions experiment are carried out on the motor bearing test-bed.The experiment result shows that the method can use a small amount of samples for training HMM,and it is more effective and has higher classification accuracy in fault diagnosis compared with the traditional training algorithm.
会议名称:

2015 2nd International Conference on Machinery,Materials Engineering,Chemical Engineering and Biotechnology(MMECEB 2015)

会议时间:

2015-11-28

会议地点:

Chongqing,China

  • 专辑:

    工程科技Ⅱ辑

  • 专题:

    机械工业

  • 分类号:

    TH133.33

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