Fault Diagnosis Method of Waterproof Valves in Engineering Mixing Machinery Based on a New Adaptive Feature Extraction Model

Due to the complex working medium of oil in construction engineering, the waterproof valve in mixing machinery can easily cause different degrees of failure. Moreover, under adverse working conditions and complicated noise backgrounds, it is very difficult to detect the fault of waterproof valves. Thus, a fault diagnosis method is proposed, especially for the fault detection of waterproof valves as a key component in the construction of mixing machinery. This fault diagnosis method is based on a new adaptive feature extraction model, with multi-path signals to the improved deep residual shrinkage network–stacked denoising convolutional autoencoder (named DRSN–SDCAE). Firstly, the noisy vibration signals collected by the two vibration sensors are preprocessed, and then transmitted to the parallel structure improved DRSN–SDCAE for adaptive denoising and feature extraction. Finally, these results are fused through the feature fusion strategy to realize the effective fault diagnosis of the waterproof valve. The effectiveness of this method was verified through theory and experiments. The experimental results show that the proposed fault diagnosis method based on the improved DRSN–SDCAE model can automatically and effectively extract fault features from noise for fault diagnosis without relying on signal processing technology and diagnosis experiences. When compared with other intelligent fault diagnosis methods, the features extracted from multi-path inputs were more comprehensive than those extracted from single-path inputs, and contained more complete features of hidden data, which significantly improved fault diagnosis accuracy based on these fault features. The contribution of this paper is to learn fault features autonomously in signals with strong and complex noise through a deep network structure, which extends the fault diagnosis method to the field of construction machinery to improve the safe operation and maintainability of engineering machinery.