Stepwise Intelligent Diagnosis Method for Rotor System with Sliding Bearing Based on Statistical Filter and Stacked Auto-Encoder

Abstract

Since the raw signal collected from the sliding bearing is contaminated with background noise, and it is difficult to obtain high-precision results for the traditional methods due to the low signal-to-noise ratio (SNR). Therefore, a stepwise intelligent diagnosis method based on statistical filter and stacked auto-encoder (SAE) that is established with several auto-encoders is proposed to identify several faults of sliding bearing in a rotor system. Firstly, the statistical filter is utilized to reduce the interference information for the different abnormal states and to increase the SNR. Secondly, the stepwise intelligent diagnosis based on SAE is performed to learn the useful fault features, and it can automatically complete the fault diagnosis which is contributed with the superiority binary classification to fully mine the relationship between the fault characteristics and the health condition of bearing. Finally, the diagnosis of the oil whirl and structural faults in a rotor system is cited as an example to demonstrate the effectiveness of proposed method. It can effectively illustrate the advantages of the stepwise diagnosis method to obtain the maximum diagnostic accuracy.

A novel intelligent bearing fault diagnosis method based on signal process and multi-kernel joint distribution adaptation

“…The statistical filter removes noise by calculating mean and standard deviation (as in Eq. ( 1 )) of each part (total M parts) and selecting valuable information with the distinction index ( DI ) 24 . where and are the mean values of the i th spectrum part calculated by the raw signal at normal and abnormal states, respectively.…”

“…First, most existing intelligent fault diagnosis methods 4 , 5 assume that the same distribution is present in the training and testing data. However, the distribution discrepancy 6 is generally present in both datasets because of the different operating conditions and noise interference. This can lead to the compulsory relearning the diagnostic model with the training data when applied to diverse application conditions, wherein the trained model can guarantee high accuracy.…”

“…Over the past decade, intelligent fault diagnosis methods 1 , especially data-driven method, were proposed to perform diagnostic decisions intelligently and produce satisfactory diagnosis results without prior expertise 2 , 3 . However, those methods have several disadvantages before their flexible applications in the industry.…”