Multiple-Timescale Feature Learning Strategy for Valve Stiction Detection Based on Convolutional Neural Network

Abstract

This article proposes a valve stiction detection strategy based on a convolutional neural network. Considering the commonly existing characteristics of industrial time-series signals, the strategy is developed to learn features on multiple timescales automatically. Unlike the traditional approaches using hand-crafted features, the proposed strategy can automatically learn representative features on the time-series data collected from industrial control loops. The strategy is composed of two complementary data conversion methods, a mixed feature learning stage and a fusion decision stage, and it has the following merits: 1) the interaction of different pairs of time series can be effectively captured; and 2) the whole process of feature learning is automatic, and no manual feature extraction is needed. The effectiveness of the proposed strategy is evaluated through the comprehensive data, including the International Stiction Data Base, and the real data collected from the real hardware experimental system and the industrial environment. Compared with four traditional methods and three deep-learning-based methods, the experimental results demonstrate that the proposed strategy outperforms the other methods. Besides performance evaluation, we give the implementation procedure of practical application of the proposed strategy and provide the detailed analysis from the perspective of the data conversion methods and the number of timescales.