Abstract
The effective fault diagnosis of the motor bearings not only can ensure the smooth and efficient operation of equipment but also can detect and eliminate the running fault in time to prevent major accidents. Based on deep learning algorithm, this article constructs a stacked auto-encoder network. The input data are compressed and reduced by introducing sparsity constraint, so that the network can accurately extract the fault characteristics of the input data, and the fault recognition ability of the network can be improved by introducing random noise. The simulation result shows that the stacked auto-encoder network can not only overcome the shortcomings of traditional fault diagnosis method that requires to distinguish fault samples manually and needs a large number of prior knowledge but also realize the self-learning of fault signal feature. The accuracy rate of fault identification reaches 98%, 94%, 96%, and 95.5% in four different working conditions. What’s more, the network can exhibit strong robustness under different working conditions. Finally, the new research ideas of fault diagnosis in thermal power plant are put forward by copying the idea of fault diagnosis of motor bearing.
Highlights
Motor bearings are an important component of electric motors and are widely used in industrial fields such as electric power production
As vibration signals are highly accurate indicator of the health conditions of mechanical equipment, they are widely used in fault diagnosis.[2]
The sensor is used to collect the vibration signal of the motor bearing, the time domain and frequency domain analysis methods are used to analyze the collected signal, and the result is shown whether the motor bearing is faulty.[3,4]
Summary
Motor bearings are an important component of electric motors and are widely used in industrial fields such as electric power production. SAE adopts an unsupervised training method, which can independently learn data features and effectively avoid the problem of manually classifying fault data.[21] This article uses SAE to diagnose motor bearing faults in three steps: (1) constructing SAE deep neural network, adding sparse constraints to improve the compression capability, and introducing random noise to the input information to reconstruct the original data; (2) inputting the original vibration signal, training the SAE network layer by layer, and extracting feature information by self-learning; and (3) testing the accuracy of fault identification by the test data, and comparing the fault recognition rate with the other algorithms, which includes auto-encoder (AE), deep belief network (DBN), and SVM.
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