Fault Diagnosis Based on Multi-sensor Data Fusion for Numerical Control Machine

Abstract

Fault diagnosis for numerical control machine is more difficult than that for other mechanical equipments due to its structural complexity and the coupling feature among different faults. In order to improve the accuracy and reliability of fault diagnosis for numerical control machine, an intelligent fault diagnosis model is studied. Besides the traditional method that multiple sensors are mounted on different locations, internal operation parameters from machine tool itself or NC program are introduced into the condition monitoring system because numerical machine tool is equipped with different kinds of sensors. These two information sources establish the multi-dimensional information system which provides the original information for diagnosis. On this base, the method based on multi-sensor data fusion is developed in this paper. Multiple characteristic parameters in time domain, frequency domain and time-frequency domain are extracted from the processed signal to mine the fault information. The sensitive parameter set which is regarded as the input characteristic vectors of classifiers is obtained on the base of correlation analysis. Multiple classifiers are enabled respectively and simultaneously to fuse all the sensitive parameters quantitatively and diagnose the fault type. Finally the results of multiple classifiers are fused in the form of global decision fusion by the method of fuzzy comprehensive evaluation to obtain the final diagnosis result. The determination method of weight based on classifier output's entropy is discussed in this paper and the formula is given. This model and method has been tested in rolling bearing fault diagnosis for numerical control machine and the results of the proposed model show which is effective and versatile.