Nonlinear Quality‐Related Fault Detection Using Neighborhood Embedding Neural Orthogonal Mapping Algorithm for Batch Process

Abstract

AbstractQuality‐related fault detection has become a hot research topic in recent years. It is not reliable to measure quality‐related relationships only by mutual information among process variables and single‐quality variables. Frequent alarms for quality‐unrelated faults seriously affect the normal operation of industrial production. At the same time, the strong nonlinearity of the process data leads to the difficulty of feature extraction. In this paper, we propose a fault detection algorithm based on nonlinear quality‐related neighborhood embedding neural orthogonal mapping (QR‐NENOM). First, quality‐related and quality‐unrelated variables are selected by Bayesian fusion mutual information, and the weighted method of mutual information is used to enhance the quality‐related information and suppress the quality‐unrelated information. Second, local manifold information is obtained by reconstructing nearest neighbors of process data, and key features are extracted by the nonlinear method composed of neural network and orthogonal mapping. Then, statistical indicators are established to complete fault detection. Finally, the nonlinear feature extraction ability of NENOM is verified by numerical examples, and the QR‐NENOM algorithm proposed in this paper is applied to the penicillin fermentation process. Comparative experiments show that QR‐NENOM has better detection performance for quality‐related faults and fewer alarms for quality‐unrelated faults.