Fault diagnosis of motor bearing based on deep learning

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.

Application of a new one-dimensional deep convolutional neural network for intelligent fault diagnosis of rolling bearings

“…In order to make a comparative study on the capability of three models, the vibration data provided in the Open Bearing Database of Case Western Reserve University (CWRU) are applied for analysis, the corresponding experimental platform is shown in Figure 4. 28 It consists of a torque transducer, a dynamometer and a power motor. The leftmost motor is used to generate driving force, and the right-most dynamometer is used to generate rated loads (0hp, 1hp, 2hP and 3hp), which connected with a mediated torque sensor.…”

Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills

“…e optimum SAE model has important effects on the accuracy rate of fault identification. In [25,26], the unsupervised learning effect of SAE is reported to be affected by parameters of model, such as the number of nodes in the input and hidden layers, sparse parameter, and the number of times of network training. e experimental data of Experiment 1 are used as training samples, and relevant experiments are conducted to determine the optimum parameters of the SAE model.…”

Rotating machinery fault diagnosis by deep adversarial transfer learning based on subdomain adaptation

“…2,3 Deep learning is also widely used to solve fault diagnosis due to its powerful feature learning ability. [4][5][6] The success of these methods depends on the large amount of labeled data available for supervised learning, and they require training and testing data come from the same distribution. However, manual large scale labeled data are too expensive and sometimes cannot be collected in practice.…”