Fault Diagnosis Model Based on Multi-level Information Fusion for CNC Machine Tools

The difficulty of CNC machine tools fault diagnosis is bigger than other general equipments because of the complex structure and the coupling among subsystems. The fault diagnosis model based on multi-level information fusion and hybrid intelligence is studied to improve reliability of fault diagnosis. Information from built-in sensors is used to monitor the status of CNC machine tools. The diagnosis principles of internal parameters-motor current, torque, temperature and following error are analyzed. Internal information and external sensors are two main sources which provide data to diagnosis. In order to detect effective fault signal, features of time domain, frequency domain and time-frequency domain are extracted. All these features constitute the feature set. The features are selected by the method of Kernel Principal Component Analysis (KCPA). Then the sensitive feature set is obtained. The method of multiple classifier fusion based on fuzzy comprehensive evaluation is researched. The determination method of weight based on information entropy is proposed. This diagnosis model has been tested feed system mechanical fault diagnosis of CNC machine tools and the results show which is effective and versatile.