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This paper combines self-organizing mapping (SOM) and a long short-term memory network (SOM-LSTM) to construct an audio-based motor-fault diagnosis system for identifying the operating states of a rotary motor. This paper first uses an audio signal collector to measure the motor sound signal data, uses fast Fourier transform (FFT) to convert the actual measured sound–time-domain signal into a frequency-domain signal, and normalizes and calibrates the frequency-domain signal to ensure the consistency and accuracy of the signal. Secondly, the SOM is used to further analyze the characterized frequency-domain waveforms in order to reveal the intrinsic structure and pattern of the data. The LSTM network is used to process the secondary data generated via SOM. Dimensional data aggregation and the prediction of sequence data in long-term dependencies accurately identify different operating states and possible abnormal patterns. This paper also uses the experimental design of the Taguchi method to optimize the parameters of SOM-LSTM in order to increase the execution efficiency of fault diagnosis. Finally, the fault diagnosis system is applied to the real-time monitoring of the motor operation, the work of identifying the motor-fault type is performed, and tests under different loads and environments are attempted to evaluate its feasibility. The completion of this paper provides a diagnostic strategy that can be followed when it comes to motor faults. Through this fault diagnosis system, abnormal conditions in motor equipment can be detected, which can help with preventive maintenance, make work more efficient and save a lot of time and costs, and improve the industry’s ability to monitor motor operation information. |
