Abstract
In recent years, machine learning techniques have been proven to be a promising tool for early fault detection of rolling bearings. In many actual applications, however, bearing whole-life data are not easy to be historically accumulated, while insufficient data may result in training a detection model that is not good enough. If utilizing the available data under different working conditions to facilitate model training, the data distribution of different bearings are usually quite different, which does not meet the precondition of i n d e p e n d e n t a n d i d e n t i c a l d i s t r i b u t i o n ( i . i . d ) and tends to cause performance reduction. In addition, disturbed by the unstable noise under complex conditions, most of the current detection methods are inclined to raise false alarms, so that the reliability of detection results needs to be improved. To solve these problems, a robust detection method for bearings early fault is proposed based on deep transfer learning. The method includes offline stage and online stage. In the offline stage, by introducing a deep auto-encoder network with domain adaptation, the distribution inconsistency of normal state data among different bearings can be weakened, then the common feature representation of the normal state is obtained. With the extracted common features, a new state assessment method based on the robust deep auto-encoder network is proposed to evaluate the boundary between normal state and early fault state in the low-rank feature space. By training a support vector machine classifier, the detection model is established. In the online stage, along with the data batch arriving sequentially, the features of target bearing are extracted using the common representation learnt in the offline stage, and online detection is conducted by feeding them into the SVM model. Experimental results on IEEE PHM Challenge 2012 bearing dataset and XJTU-SY dataset show that the proposed approach outperforms several state-of-the-art detection methods in terms of detection accuracy and false alarm rate.
Highlights
As an important part of common machinery equipment, rolling bearings are prone to various kinds of faults under complex working conditions like long-term heavy load and strong impact, etc.Faulty bearings will cause the performance deterioration of whole machinery
The proposed state assessment method includes two steps: (1) For the common feature set X of auxiliary bearings extracted by deep auto-encoder (DAE) with domain adaptation, we feed them into robust deep auto-encoder (RDA) and calculate L D, which is the low-rank public representation of auxiliary bearing data
The probability density distribution of nine training bearings is obviously different in raw time domain, but after the common feature mapping, the probability density distribution of all bearings tends to be consistent, approximately in accordance with the same distribution. These comparative results show that the DAE with domain adaptation is able to map the data of different bearings to a common feature subspace, which eliminates the phenomenon of inconsistent distribution in normal state
Summary
As an important part of common machinery equipment, rolling bearings are prone to various kinds of faults under complex working conditions like long-term heavy load and strong impact, etc. (2) In order to build an effective online detection model in complex environment and noise interference, it is necessary to find a state assessment method with strong anti-interference ability This method should be able to achieve accurate recognition of early fault state on different bearing data. Running with deep transfer learning, this method can accurately identify early fault state on the bearing data under different working conditions. This method has good anti-interference ability against irregular fluctuation in normal state data.
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