Unsupervised machine fault diagnosis for noisy domain adaptation using marginal denoising autoencoder based on acoustic signals

Abstract

Recently, with the desperate demand for data-driven deep learning methods in practical industrial applications, increasing popularity of deep learning methods for machine fault diagnosis under noisy environments has been observed. However, the existing studies have encountered two limitations: 1) the domain shift problem has not been researched enough to ensure a functional adaptation model across different noisy domains; 2) those deep learning frameworks are generally complicated and require massive computational space. To address those problems, we propose a Noisy Domain Adaptive marginal Stacking Denoising Auto-encoder (NDAmSDA) based on acoustic signals for domain adaptation between different noise levels. Specifically, a modified mSDA, integrated with a dimensionality reduction method based on Transfer Component Analysis (TCA), not only facilitates the training acceleration by replacing the traditional gradient descent method of backpropagation with a forward closed-form solution, but also promotes gapping the discrepancy between various noise levels and transferring the classifier built from one noisy domain to others. This enables us to extend diagnostic models’ capability to real-world scenarios because of the unavoidable background noise in the collected signals. To validate the effectiveness of our proposed approach, datasets mixed with additive white Gaussian noise (AWGN) and binary masking noise (BMN) from the gear tests and motor tests are constructed for validation. Comparisons with representative and popular fault diagnosis approaches confirm the superiority of the proposed method.