Abstract
In the large amount of available data, information insensitive to faults in historical data interferes in gear fault feature extraction. Furthermore, as most of the fault diagnosis models are learned from offline data collected under single/fixed working condition only, this may cause unsatisfactory performance for complex working conditions (including multiple and unknown working conditions) if not properly dealt with. This paper proposes a transfer learning-based fault diagnosis method of gear faults to reduce the negative effects of the abovementioned problems. In the proposed method, a cohesion evaluation method is applied to select sensitive features to the task with a transfer learning-based sparse autoencoder to transfer the knowledge learnt under single working condition to complex working conditions. The experimental results on wind turbine drivetrain diagnostics simulator show that the proposed method is effective in complex working conditions and the achieved results are better than those of traditional algorithms.
Highlights
With the extensive application of technology in industrial production, fault diagnosis is playing an increasingly important role
Based on the above literature review, there is no research work at present that investigates the effects of both two difficulties simultaneously. e contributions of this paper are listed as follows: (1) the problem with both complex working conditions and insensitive information is investigated; (2) a deep transfer learning-based fault diagnosis method with sensitive features selection and the combination of Sparse autoencoder (SAE) based on transfer learning is proposed for the abovementioned problems
E fault diagnosis process of the proposed algorithm is divided into 7 steps, as shown in Table 2: sensitive feature selection from step 1 to step 3, network training of source data in step 4, and network adaptation by transfer learning from step 5 to step 7. e detailed information of each part is explained as follows: (1) Sensitive feature selection: first, training data set Ds and testing data set Dc are collected under single and complex working conditions of rotating machinery; second, features are computed and sorted by cohesion evaluation shown in Table 1; third, sensitive features are chosen according to sensitive factor ηj in (2), which are reserved to constitute sensitivity parameter set as input data
Summary
With the extensive application of technology in industrial production, fault diagnosis is playing an increasingly important role. Qian proposed a new transfer learning method to detect faults of rotating machine under variant working conditions [30] These previously mentioned research works have limitations: first, there is no discussion or analysis on both multiple working conditions and unknown working conditions; second, the handling of insensitive information which can affect the fault diagnosis performance is not comprehensive. E contributions of this paper are listed as follows: (1) the problem with both complex working conditions and insensitive information is investigated; (2) a deep transfer learning-based fault diagnosis method with sensitive features selection and the combination of SAE based on transfer learning is proposed for the abovementioned problems.
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