| Summary: | 碩士 === 國立臺灣師範大學 === 機電科技學系 === 102 === Tool machines are essential in many manufacturing processes. Gearboxes are the common used component in a tool machine. Gearbox failures could lead to unpredictable productivity losses for production facilities. Therefore, gearbox fault diagnosis has attracted significant attention from the research and engineering communities over the past decades. In general, a data-driven fault diagnosis system consists of three general steps: vibration data acquisition, feature extraction and fault condition classification. In this paper, four multiscale scale analysis algorithms including composite multiscale entropy (CMSE), composite multiscale permutation entropy (CMPE), multiband spectrum entropy (MBSE), and multiscale singular value decomposition entropy (MSVDE) are applied to extract the features of vibration signals collected from different gearbox faults. Support vector machine (SVM) and artificial neural network (NN) are used as classifiers to distinguish the fault types of gearbox respectively.
The experimental platform is provided by Industrial Technology Research Institute (ITRI). Four different conditions including normal, imbalance, tooth-wear and tooth-broken are considered in these experiments. The vibration signals of gearbox were collected for several different motor speeds from 446rpm to 2121 rpm with a resolution of 12rpm.
To evaluate the feasibility of the proposed algorithm for multi-speeds gearbox fault diagnosis. Vibration data for five different speeds were grouped and considered as the same class. Two experiments are performed in this study: (1) data used to train a classifier come from all five different speeds; (2) data used to train a classifier came from only one specified speeds. Simulation results indicate that if the training data come from all different speeds, the accuracy of prediction of the proposed diagnosis algorithm is very high (up to 99.8%). However, the accuracy of prediction of the proposed diagnosis algorithm will decrease dramatically if the training data come from only one speed. We wish this study can provide some contribution in developing a multi-speeds gearbox fault diagnosis system.
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