Medical rolling bearing fault prognostics based on improved extreme learning machine

He, Cheng; Liu, Changchun; Wu, Tao; Xu, Ying; Wu, Yang Chang; Chen, Tong

doi:10.1007/s10878-019-00494-y

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Ali et al [15] used empirical mode decomposition to extract 10 time-domain statistical features and an artificial neural network is used to identify the health conditions of rolling bearing. 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. However, although 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].…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning

Ouyang

Chen

et al. 2020

Sensors

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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%.

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