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An enhanced intelligent diagnosis method for rotary equipment is proposed based on multi-sensor data-fusion and an improved deep convolutional neural network (CNN) models. An improved CNN based on LeNet-5 is constructed which can enhance the features of the samples by stacking bottleneck layers without changing the size of the samples. A new conversion approaches are also proposed for converting multi-sensor vibration signals into color images, and it can refine features and enlarge the differences between different types of fault signals by the fused images transformed in red-green-blue (RGB) color space. In the last stage of network learning, visual clustering is realized with t-distributed stochastic neighbor embedding (t-SNE) to evaluate the performance of the network. To verify the effectiveness of the proposed method, examples in practice such as the diagnosis for the wind power test rigs and industrial fan system are provided with the prediction accuracies of 99.89% and 99.77%, respectively. In addition, the efficiency of other comparative baseline approaches such as the deep belief network and support vector machine (SVM) is evaluated. In conclusion, the proposed intelligent diagnosis method based on multi-sensor data-fusion and CNN shows higher prediction accuracy and more obvious visualization clustering effects.
Wang et al. (Fri,) studied this question.