Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions

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.

A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network

“…The weight determines the influence of the previous speed of the particle on the current speed, which plays a role in balancing the global search and the local search. As shown in equation (29), the weights are linear decay with iterations. This makes the particle swarm algorithm have strong search ability at the beginning of the iteration, and good local search ability in the later stage 45…”

“…Considering the increase in energy when the ball passes through the fault, the frequency values are divided by instantaneous speed and corresponding amplitude to form a new fault feature array, and the Euclidean distance classifier was used for recognition. Tong et al 29 proposed domain adaptation using transferable features (DATF) to solve the diagnosis of different working conditions. They used maximum mean discrepancy (MMD) to reduce the marginal and conditional distributions simultaneously during domains across.…”

Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning

“…Jian et al [30] proposed a fusion CNN model that combines 1DCNN and Dempster-Shafer evidence theory to enhance the cross-domain adaptive capability for fault diagnosis. Tong et al [31] proposed a bearing fault diagnosis domain adaptation method to find transferable features across domains, which were obtained by reducing marginal and conditional distributions simultaneously based on MMD. Li et al [32] presented a deep domain adaptation method for bearing fault diagnosis on the basis of the multikernel maximum mean discrepancies between domains in multiple layers to learn representations from the source domain applied to the target domain.…”

A New Deep Convolutional Domain Adaptation Network for Bearing Fault Diagnosis under Different Working Conditions

“…In recent years, a variety of intelligent bearing fault diagnosis methods based on domain adaptation have been proposed [21][22][23]. A domain adaptation bearing fault diagnosis method was proposed in [24] by fast Fourier transformation of the original signal. Ren et al [25] used multiscale permutation entropy and time-domain features as network input to train neural network, and it is verified by experiments that the proposed method can implement fault diagnosis under different working conditions.…”

“…Ren et al [25] used multiscale permutation entropy and time-domain features as network input to train neural network, and it is verified by experiments that the proposed method can implement fault diagnosis under different working conditions. However, the diagnostic performance of the methods in [24][25] is also affected by the quality of manually extracted features. Similarly, deep learning can also be used to achieve transfer learning.…”