标题：Intelligent diagnosis of rolling bearing compound faults based on device state dictionary set sparse decomposition feature extraction–hidden Markov model
摘要：Identification of rolling bearing fault patterns, especially for the compound faults, has attracted notable attention and is
still a challenge in fault diagnosis. Intelligent diagnosis method is an effective method for compound faults of rolling
element bearing, and effective fault feature extraction is the key step to decide the intelligent diagnosis result to some
extent. The sparse decomposition method could capture the complex impulsive characteristic components of rolling
bearing more effectively than the other time–frequency analysis method when compound fault arises in rolling bearing.
Based on the self-learning dictionary under different operating states of the device corresponding to the special features
modes, an intelligent diagnosis method of rolling bearing compound faults based on device state dictionary set sparse
decomposition feature extraction–hidden Markov model is proposed in the article. First, characteristic dictionaries of
rolling bearing under different operating conditions are extracted by sparse decomposition self-learning method, and
state dictionary set of rolling bearing is constructed. Then, the compound fault signals of bearing are transformed into
sparse domain using the constructed dictionary set to extract sparse features. At last, the extracted sparse features are
used as training and testing vectors of hidden Markov model, and satisfactory intelligent diagnosis results are obtained.
The validity of the proposed method is verified by compound faults of rolling element bearing. In addition, the advantages
of the proposed method are also verified by comparing with the other feature extraction and intelligent diagnosis
methods, and the proposed method provides a feasible and efficient solution for fault diagnosis of rolling bearing
compound faults.
关键词：Intelligent diagnosis; rolling bearing; compound fault; dictionary set; sparse decomposition; hidden Markov model
