Abstract
Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties. However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy‐dropping problem of fault diagnosis. Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge. In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF). The datasets of normal bearing and faulty bearings are obtained through the fast Fourier transformation (FFT) of raw vibration signals under different motor speeds and load conditions. Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy (MMD) in feature space by refining pseudo test labels, which can be obtained by the nearest‐neighbor (NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations. With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final. Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.
Highlights
Bearings are the most critical components and widely used in rotating machinery, whose health conditions, for example, the fault degree in different places under different motor speeds and loads, may have a huge effect on the performance, reliability, and residual life of the equipment [1] or even can lead to heavy casualties [2,3,4]
With the vibration signals under different conditions being collected by sensors [6], many intelligent fault diagnosis methods have already achieved significant success in the field of fault diagnosis
Reducing the dimensions is conducted for the sake of computational efficiency, such as principal component analysis (PCA) [11], locally linear embedding (LLE) [12], and linear discriminant analysis (LDA) [13]
Summary
Bearings are the most critical components and widely used in rotating machinery, whose health conditions, for example, the fault degree in different places under different motor speeds and loads, may have a huge effect on the performance, reliability, and residual life of the equipment [1] or even can lead to heavy casualties [2,3,4]. It is important to diagnose bearings under different working conditions. With the vibration signals under different conditions being collected by sensors [6], many intelligent fault diagnosis methods have already achieved significant success in the field of fault diagnosis. N. Saravanan et al [8] proposed fault diagnosis method based on DWT and ANN, and it has been proved such approach had the potential to diagnose various faults of the gearbox. It is important for fault diagnosis to achieve effective features [9]. Many signal processing approaches are applied to feature extraction from vibration signals. Such as, time-domain statistical analysis, frequency domain analysis [10], and timefrequency domain analysis [2].
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