Optimized DTW-Resnet for Fault Diagnosis by Data Augmentation Toward Unequal Length Time Series
Hewei Gao, Xin Huo, Rong Hu, Changchun He
Abstract
In this paper, a fault diagnosis method based on data augmentation technology is proposed to investigate the unequal length series by combining Dynamic Time Warping (DTW) with Deep Residual Network (Resnet) for excavator, where the hydraulic and power units are operated in a hybrid manner. A data augmentation method is designed to segment time series with unequal length by enhancing appropriate statistical features based on the working characteristics of excavators. Based on instantaneous calculation, an optimized DTW algorithm is proposed to reduce the calculation cost and ensure the effectiveness of the searched optimal warping path. In order to achieve fault diagnosis in industrial applications, a DTW-Resnet model combining optimized DTW algorithm with Resnet model is proposed, which overcomes the disadvantage of neural networks being unable to learn time series with unequal length easily. Results with respect to SANY excavator datasets and four publicly available datasets have well indicated that the proposed method is provided with preferable diagnostic performance compared with state-of-the-art neural networks and traditional models. Furthermore, the ablative experiment shows that the data augmentation and optimized DTW are of great significance to improve the classification accuracy of the model.