| Summary: | 碩士 === 國立臺北科技大學 === 機電整合研究所 === 96 === This thesis is mainly devoted to developing an intelligent diagnosis system for motor rotary faults. The design of this system is for the common motor rotary faults, and adopts the dynamic structure neural network to establish the diagnosis functionality.
The main structure of system can be divided into three modules: measurement of vibration signal, filtering process and fault classification. The vibration signal measurement is done by mounting the sensing module on the motor cast, and utilizing the receiver to read vibration signal for follow-up processing. Because the measured signal is apt to be influenced by the mechanical structure or other environmental factors, the signal often contains noises. To solve the problem, this thesis adopts the wavelet mechanism to filter the noises out, which may reduce the error rate of fault diagnosis.
The fault classification method uses the improved dynamic structure neural network. For the conventional neural networks, there is lack of methods to determine the number of hidden neurons. It leads that the network structure is not optimal and the convergence problem arises in the learning process. Using the dynamic structure neural network, the optimal neural network could be obtained by adjusting the number of neurons.
For the motor rotary faults, it is known that the characteristics of motor faults appear in specific harmonic frequencies and different faults cause different frequency patterns. This thesis utilizes the fault characteristics and extracts the special frequency patterns as the input of the neural network. The output of the neural network is the classification result of the corresponding fault.
To implement the intelligent fault diagnose system, this thesis uses MATLAB software. The system includes the signal measurement, filtering, fault characteristics extraction, and diagnosis function. All the functions can be executed by using the friendly graphical user interface. From the experimental results, it is found that the classification results outperform than the previous results.
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