Abstract
A novel fault detection method is proposed for detection process with nonlinearity and multimodal batches. Calculating the Mahalanobis distance of samples, the data with the similar characteristics are replaced by the mean of them; thus, the number of training data is reduced easily. Moreover, the super ball regions of mean and variance of training data are presented, which not only retains the statistical properties of original training data but also avoids the reduction of data unlimitedly. To accurately identify faults, two control limits are determined during investigating the distributions of distances and angles between training samples to their nearest neighboring samples in the reduced database; thus, the traditionalk-nearest neighbors (only considering distances) fault detection (FD-kNN) method is developed. Another feature of the proposed detection method is that the control limits vary with updating database such that an adaptive fault detection technique is obtained. Finally, numerical examples and case study are given to illustrate the effectiveness and advantages of the proposed method.
Highlights
Fault detection has been one focus of recent efforts since there existed a growing need for the quality monitoring and safe operation in the practical process engineering [1,2,3,4]
In Example 1, firstly, we give the results of reducing training data set under different thresholds and the main aim is to show the effect of thresholds on the number of reducing training data; secondly, we verify the effectiveness of the detection method proposed in this paper for nonlinear process and illustrate that faults can be identified better under two control limits than one control limit; in addition, comparative results are given to show the advantages of this paper
This paper studies an adaptive fault detection method faced to process engineering with nonlinear and multimodal behaviors
Summary
Fault detection has been one focus of recent efforts since there existed a growing need for the quality monitoring and safe operation in the practical process engineering [1,2,3,4]. Reference [14] proposed an adaptive local model based on the monitoring approach for online monitoring of nonlinear and multiple mode processes with non-Gaussian information. Reference [13] reduced and updated training database, and it presented JIT fault detection method. This paper is concerned with the two time-varying control limits design used for online fault detection for the multi-mode and nonlinear processes. It is worth pointing out that two control limits vary according to the updating database such that an adaptive fault detection technique that can effectively eliminate the impact of data drift and shift on the performance of detection process is obtained. (2) Different from [15], we propose a new fault detection framework used to reduce and update database, as well as vary control limits.
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