Cross-condition bearing fault detection based on online drift detection and domain adaptation

Aiming at the problem that the data distribution of bearings across operating conditions generates offset resulting in insufficient diagnostic accuracy of the original model for new data, a cross-condition bearing fault detection method based on online drift detection and domain adaptation is proposed. First, the original one-dimensional vibration signals collected are transformed by a two-dimensional wavelet transform to convert the time-frequency image dataset. Second, the drift detection of the data across operating conditions is carried out using Random Forest (RF), and the 3σ criterion as well as the drift detection judgment criteria are set. Next, the source domain model based on Googlenet is used to extract features from the target domain data, and the Whale Optimization Algorithm to Improve Local Preserving Projection Algorithm (WOA-LPP) algorithm is combined to construct a brand-new projection space to align the features of the source and target domains. Then, the source and target domain features are reconstructed by combining the LPP optimal projection matrix to construct a fully connected network trained by the source domain features. Finally, probabilistic label-based decision fusion is proposed to integrate multiple classifiers to reduce the effects of model training randomness and strong noise interference. Validated by the publicly available Western Reserve University bearing data, the method proposed in this paper has good detection accuracy as well as robustness across operating conditions, which can effectively improve the defects of shifting data distribution and degradation of model accuracy under variable speed.