Abstract
Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used as the main architecture. To improve the robustness and stability, a novel weight strategy based on distance metric and standard deviation metric was employed to assign the weights of three sparse auto-encodes. Softmax classifier is used to classify the fault types of integrated features. The effectiveness of the proposed method is validated with extensive experiments, and comparisons with the related methods and researches on the widely-used motor bearing dataset verify the superiority of the proposed method. The results show that the testing accuracy and the standard deviation are 99.71% and 0.05%.
Highlights
With the upgrading of industrial capacity, the connection between machine equipment is increasingly inseparable
The contributions of this paper are summarized as follows: (1) A novel ensemble deep learning method-based multiple stacks sparse AE is proposed for bearing intelligent fault diagnosis
In the training process of AE, training samples usually contain a lot of redundant information, which means that the training samples only contain a small amount of useful information, and the hidden neurons are not all activated to represent the information of input data, especially when the dimension of input data is less than the number of hidden neurons
Summary
With the upgrading of industrial capacity, the connection between machine equipment is increasingly inseparable. He et al [18] proposed an ensemble error minimized learning machine method to recognize rolling bearing faults, empirical mode decomposition technology is adopted to extract the ensemble time-domain features These traditional intelligent methods did work and achieved an accurate diagnosis result, they still have two deficiencies: (1) the features are usually manually extracted depending on prior knowledge and diagnostic expertise, which accorded to a specific fault type and probably unsuitable for other faults [19,20]; (2) In real industries, the collected signals are usually exposed to environmental noises, which cause the signals to be complex and non-stationary, and signal processing technologies need to be employed to filter the collected signals to obtain the effective features [3,21]. A novel ensemble learning method based on multiple stacks sparse AEs is proposed for bearing intelligent fault diagnosis. (1) A novel ensemble deep learning method-based multiple stacks sparse AE is proposed for bearing intelligent fault diagnosis This method is a segmented adaptive feature extraction procedure and can automatically classify the health status of the rolling machinery.
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