Research on Mechanical Equipment Fault Diagnosis Method Based on Deep Learning and Information Fusion

Abstract

Solving the problem of the transmission of mechanical equipment is complicated, and the interconnection between equipment components in a complex industrial environment can easily lead to faults. A multi-scale-sensor information fusion method is proposed, overcoming the shortcomings of fault diagnosis methods based on the analysis of one signal, in terms of diagnosis accuracy and efficiency. First, different sizes of convolution kernels are applied to extract multi-scale features from original signals using a multi-scale one-dimensional convolutional neural network (1DCNN); this not only improves the learning ability of the features but also enables the fine characterization of the features. Then, using Dempster–Shafer (DS) evidence theory, improved by multi-sensor information fusion strategy, the feature signals extracted by the multi-scale 1DCNN are fused to realize the fault detection and location. Finally, the experimental results of fault detection on a flash furnace show that the accuracy of the proposed method is more than 99.65% and has better fault diagnosis, which proves the feasibility and effectiveness of the proposed method.

Fault Diagnosis Method for Space Fluid Loop Systems Based on Improved Evidence Theory

“…Through multi-sensor information fusion technology, equipment condition monitoring and fault diagnosis are achieved. As one of the methods of multi-sensor data fusion, the D-S evidence theory can effectively describe and express uncertain information without prior probabilities, making it widely applied in fault diagnosis [18][19][20][21][22][23][24], state assessment [25][26][27], and classification [28,29]. However, the evidence theory tends to fail when fusing conflicting evidence [30], and numerous studies have proposed improvements.…”

Research on fault diagnosis of automobile engines based on the deep learning 1D-CNN method

“…The results show that the correct diagnosis rate of deep learning is obviously better than that of extreme learning machine. On this basis, information fusion technology is used to fuse the diagnosis results of the above two methods at the decision level, which further improves the accuracy of fault diagnosis [12]. Xu et al [13] proposed a fault diagnosis method for a probabilistic neural net-work of a steam turbine generator.…”