Differentiable sampling based efficient architecture search for automatic fault diagnosis

Abstract

Intelligent diagnosis on rotating machinery has developed rapidly, but different methods have fluctuating performance and fussy design, causing poor effect in practical applications. Thus, it would be great to automatically generate the optimal method for given diagnosis tasks, as differentiable neural architecture search (DNAS) does. However, three challenges severely restrict DNAS methods in industrial scenarios: 1) vibration signals are multi-scale and non-stationary; 2) huge memory cost by supernet-based search is unsuitable to practical diagnosis; 3) manual architecture derivation causes performance collapse between architecture search and practical diagnosis. Thus, we propose Differentiable Sampling based Efficient Architecture Search (DS-EAS), which generates architecture by differentiable sampling. First, the operator involution is introduced to adaptively extract critical features from noisy signals. Second, Gumbel Max-Softmax is adopted to forward sample and backward propagate the gradient on single sub-architecture at one iteration, alleviating huge memory cost. Third, progressively pruning is proposed to eliminate manual discretization error, leading to the final architecture with zero operators. Based on the searched architecture, a deeper one is built to test its real performance. Traction motor experiment is performed to discuss the performance of DS-EAS on three different sample cases. Compared with other state-of-the-art methods, outperformance of DS-EAS is successfully verified.