Feature group optimisation for machinery fault diagnosis based on fuzzy measures

With the development of modern multi-sensor based data acquisition technology often used with advanced signal processing techniques, more and more features are being extracted for the purposes of fault diagnostics and prognostics of machinery integrity. Applying multiple features can enhance the condition monitoring capability and improve the fault diagnosis accuracy. However, an excessive number of features also increases the complexity of the data analysis task and often increases the time associated with the analysis process. A method of bringing some efficiency into this process is to choose the most sensitive feature subset instead. Fuzzy measures are helpful in this regard and have the ability to represent the importance and interactions among different criteria. Based on fuzzy measure theory, a feature selection approach for machinery fault diagnosis is presented in this paper. A heuristic least mean square algorithm is adopted to identify the fuzzy measures using training data set. Shapley values with respect to the fuzzy measures are applied as importance indexes to help choose the most sensitive features from a set of features. Interaction indexes with respect to the fuzzy measures are then employed to remove the redundant features. Vibration signals from a rolling element bearing test rig are used to validate the method. The results show that the proposed feature selection approach based on fuzzy measures is effective for fault diagnosis.