Many industrial processes have multiple operation modes due to different manufacturing strategies or varying feedstock. Fault detection for a multimode process is a complex problem, as monitoring for both stable and transitional modes should be taken into consideration. In this paper, a novel method based on the similarity of data characteristics is proposed to realize mode identification for modeling data. Different models are developed to capture the major tendencies of process variables. Especially, the transitional regions between neighboring stable modes, which have their particular dynamic characteristics, are modeled, respectively. Online monitoring procedures are formulated on the basis of mode identification. It is more efficient than a model matching strategy using traversing method. At last, the efficacy of the proposed method is illustrated by applying it to a continuous annealing line process and the Tennessee Eastman process. Both results of real application and simulation clearly demonstrate the effectiveness and feasibility of the proposed method.
Fatigued driving is a major cause of road accidents. For this reason, the method in this paper is based on the steering wheel angles (SWA) and yaw angles (YA) information under real driving conditions to detect drivers’ fatigue levels. It analyzes the operation features of SWA and YA under different fatigue statuses, then calculates the approximate entropy (ApEn) features of a short sliding window on time series. Using the nonlinear feature construction theory of dynamic time series, with the fatigue features as input, designs a “2-6-6-3” multi-level back propagation (BP) Neural Networks classifier to realize the fatigue detection. An approximately 15-h experiment is carried out on a real road, and the data retrieved are segmented and labeled with three fatigue levels after expert evaluation, namely “awake”, “drowsy” and “very drowsy”. The average accuracy of 88.02% in fatigue identification was achieved in the experiment, endorsing the value of the proposed method for engineering applications.
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