A novel geodesic flow kernel based domain adaptation approach for intelligent fault diagnosis under varying working condition

Abstract Domain adaptation techniques have drawn much attention for mechanical defect diagnosis in recent years. Nevertheless, the traditional domain adaptation approaches may suffer two shortcomings: (1) Poor performance is obtained for many traditional domain adaptation approaches when the sample is insufficient. (2) The diagnosis results are not stable, that is to say, the traditional domain adaptation approaches may have poor robustness. In order to overcome these deficiencies, we propose a novel domain adaptation model named DAGSZ based on geodesic flow kernel (GFK), strengthened feature extraction and Z-score normalization. Firstly, time domain average and square for the power spectral density (PSD) matrix is applied for preprocessing the original vibration data to learn more representative features. Then, the geodesic flow kernel (GFK), an unsupervised domain adaptation method, is adopted for learning the transferable features. Finally, Z-score normalization is employed to normalize the learned transferable features and softmax regression is utilized to classify the health conditions. The real-world dataset of gears and bearings are employed to validate the effectiveness and robustness of our method. The result shows that DAGSZ obtains fairly high detection accuracies and is superior to the existing methods for mechanical fault detection.