Bearing fault diagnosis with nonlinear adaptive dictionarylearning

摘要

The monitoring of rotating machinery condition has been a critical component of the Industry 4.0 revolution in enhancing machine reliability and facilitating intelligent manufacturing. The introduction of condition-based monitoring has effectively reduced the catastrophic events and maintenance cost across various industries. One of the major challenges of the diagnosis remains as majority of the diagnostic model requires off-line analysis and human intervention. The offline analysis, which is normally done by previous experience, involves tuning model parameters to improve the performance of the diagnostic model. However, for newly developed models, the knowledge of the unknown parameters does not exist. One way to resolve this issue is through learning using adaptation. The adaptation algorithm adjusts itself by newly acquired data. Hence, improvement of the model performance is achieved. In this paper, a nonlinear adaptive dictionary learning algorithm is proposed to achieve early fault detection of bearing elements without using the conventional computation heavy algorithm to update the dictionary. Deterministic and random data separation is implemented using the autoregressive model to reduce the background noise. The filtered data is further analyzed by the Infogram toreveal the impulsiveness and cyclostationary signature of the vibration signal.The dictionary is initialized using random parameters. Instead of using thek means singular value decomposition algorithm to computethe dictionary for adaptation, the unscented Kalman filter (UKF) is implementedto update the dictionaries using the filtered signal from the Infogram. Theupdating algorithm does not require computation of the dictionary, and noprevious knowledge of the dictionary’s parameters is needed. The updateddictionary contains the detected fault signature from the Infogram and,therefore, is used for further fault analysis. The proposed algorithm has theadvantage of self-adaptation, the capability to map the non-linear relationshipof the signal and dictionary weights. The algorithm can be used in the variouscondition-based monitoring of rotating machineries to avoid additional humanefforts and improve the performance of the diagnostic model.