In order to solve the problem that it is difficult to obtain a large amount of motor data support for complex industrial equipment and system modeling in actual scenarios, a virtual motor integrated platform based on machine learning algorithm is proposed to be developed for motor signal data prediction. Taking electrical signal data as an example, machine learning algorithm is combined with database system. It can not only provide visual software ecology for algorithmic prediction model, but also provide support for continuous optimization of algorithmic prediction model by using database management and data accumulation. The experimental results show that it is feasible to combine database and algorithm model to predict motor signal in university.
In order to achieve accurate diagnosis of motor faults, a technique based on wavelet analysis and RBF neural networks is used. The wavelet thresholding method is first used to reduce the noise of the motor sound and improve the signal-to-noise ratio in order to further extract fault features. Then the wavelet packet method is used to analyze the sound signals of the three-phase asynchronous motor in three states to extract the band energy, and finally the band energy is fed into the neural network for training to build a classifier for fault diagnosis. The experimental results show that the method of combining wavelet packet technology and RBF neural network has less time consumption and higher accuracy in diagnosing motor faults. It has the potential for further development.
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