Ensemble deep contractive auto-encoders for intelligent fault diagnosis of machines under noisy environment

Abstract Intelligent fault diagnosis methods based on deep auto-encoder have achieved great success in the past several years. However, these methods cannot effectively handle the data collected under noisy environment. Therefore, this paper proposes a new ensemble deep contractive auto-encoder (EDCAE) to address the problem. First, we design fifteen deep contractive auto-encoders (DCAE) to learn invariant feature representation automatically. Due to the Jacobian penalty term in DCAE and different characteristics, these models can deal with various noisy data effectively. Second, fisher discriminant analysis is applied to select low-dimensional features with the maximum class separability. Softmax classifier is adopted to identify the selected features and produce fifteen classification results. Finally, a new combination strategy is developed to combine these individual results. Benefitting from the combination strategy, it can produce accurate diagnosis results even under strong background noise. Additionally, to prove the effectiveness of EDCAE, theory analysis about error bound is conducted. The proposed method is verified on three case studies including bearing, gear box and self-priming centrifugal pump. Experiments are conducted under seven different signal-to-noise-ratios. Results show that EDCAE is better than other intelligent diagnosis methods, including individual DCAE, deep auto-encoder, sparse deep auto-encoder, deep denoising auto-encoder and several ensemble methods.