A Fast Anomaly Diagnosis Approach Based on Modified CNN and Multisensor Data Fusion

In this article, a modified convolutional neural network (CNN) algorithm, namely 1D-GAPCNN-SVM, is proposed to address the early anomaly diagnosis problem. Considering the fact that traditional 2D-CNN based approaches contain too many model parameters and are not suitable for fast diagnosis applications using multisensor 1-D time-series measurements, 1D-CNN is introduced to deal with this problem. To reduce the number of parameters, a 1-D global average pooling layer is designed to substitute the fully connected layer with two or three layers. In order to further improve the diagnosis accuracy, a nonlinear multiclass support vector machine (SVM) is adopted to replace the traditional Softmax classifier as the final discriminator. Raw multisensor 1-D time-series data are directly fed into the diagnosis model, then the diagnosis result can be automatically generated. Two experiments, which are rolling bearing and GPS anomaly detection, have been conducted to demonstrate the effectiveness and the superior performance of the proposed method compared to the conventional SVM, K-nearest neighbor, deep neural network (DNN), and traditional 2D-CNN.