Robust Detection of Bearing Early Fault Based on Deep Transfer Learning

方位(导航) 人工智能 深度学习 计算机科学 故障检测与隔离 断层(地质) 学习迁移 噪音(视频) 模式识别(心理学) 可靠性(半导体) 降噪 代表(政治) 特征(语言学) 功率(物理) 执行机构 政治 量子力学 政治学 哲学 地震学 地质学 法学 语言学 物理 图像(数学)
作者
Wentao Mao,Di Zhang,Siyu Tian,Jiamei Tang
出处
期刊:Electronics [Multidisciplinary Digital Publishing Institute]
卷期号:9 (2): 323-323 被引量:14
标识
DOI:10.3390/electronics9020323
摘要

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.
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