A health-adaptive time-scale representation (HTSR) embedded convolutional neural network for gearbox fault diagnostics. (15th March 2022) - Kim, Yunhan; Na, Kyumin; Youn, Byeng D.
Highlights: We propose a novel health-adaptive time-scale representation (HTSR) embedded convolutional neural network (HTSR-CNN). HTSR is constructed to include the enriched fault-related information using multiscale convolutional filters. The multiscale filters can adaptively capture the fault-related signals through end-to-end learning of the HTSR-CNN. Experimental results demonstrate the superiority of the proposed HTSR-CNN method for gearbox fault diagnostics. Abstract: This research proposes a newly designed convolutional neural network (CNN) for gearbox fault diagnostics. A conventional CNN is a deep-learning model that offers distinctive performance for analyzing two-dimensional image data. To exploit this ability, prior work has been developed using time–frequency analysis, which derives image-like data that is fed into the CNN model. However, the existing time–frequency analysis approach employs fixed basis functions that are limited in their ability to capture fault-related signals in the image. To address this challenge, we propose a health-adaptive time-scale representation (HTSR) embedded CNN (HTSR-CNN). The proposed HTSR approach is designed to exploit the concept of TSR, which is informed by the physics of the time and frequency characteristics induced by the fault-related signals. Instead of using fixed basis functions, the HTSR is constructed using multiscale convolutional filters that behave like the adaptive basis functions. These multiscale filters are effectively learned to include the enriched fault-related information in the HTSR through end-to-end learning of the HTSR-CNN model. The performance of the proposed HTSR-CNN is validated by examining two case studies: vibration signals from a two-stage spur gearbox and vibration signals from a planetary gearbox. From the case study results, the proposed HTSR-CNN method is found to have superior performance for gearbox fault diagnostics, as compared to existing CNN-based fault diagnostic methods.