Incipient fault detection based on dense feature ensemble net

With modern industrial processes becoming more and more complex, the occurrence of faults may cause unmitigated disaster. Therefore, incipient fault detection is very important and has attracted increasing attention Recently, a feature ensemble net (FENet) has been proposed based on a deep feature ensemble framework, which can detect partial notorious faults (such as faults 3, 9 and 15 in the Tennessee Eastman process (TEP)). However, the feature transformer layers in FENet can only mine the process information from the upper layer's output rather than all features from previous layers. Therefore, a novel dense feature ensemble net (DenseFENet) is proposed to deeply extract the features of data for incipient fault detection in industrial process. DenseFENet firstly constructs base detectors with capabilities for extracting nonlinear features, such as one-class support vector machine, isolation forest, one-class back propagation neural network, one-class long short term memory network and one-class temporal convolutional network, etc. Then, a dense net structure with short paths between non-adjacent feature transformer layers is developed, improving the reutilization capability for shallow knowledge. In addition, a numerical simulation and TEP are adopted to demonstrate the performance of the proposed method. The fault detection rate of DenseFENet has improved by 5.05% and 3.27% correspondingly, revealing the superiority of this approach.