A weighted k-nearest neighbor process monitoring method based on statistical volume pattern analysis

Abstract

Process monitoring technology has developed rapidly in response to the increasing demand for safer and more reliable systems in modern process operations. Online process monitoring plays an important role not only in ensuring process safety through the timely detection of process faults but also in improving process productivity and product quality. One of the outstanding features of modern manufacturing facilities, which are large in scale and highly complex, is that the processes contain numerous variables operating under closed-loop control. Fully exploiting and utilizing the valuable information in these variables will benefit early and accurate fault detection and diagnosis of processes, minimizing downtime, increasing plant operational safety, and reducing manufacturing costs. The development of process monitoring technology is of great importance to ensure the operational safety, reliability, and economy of complex industrial processes. With the continuous development of industrial processes, the collection and use of process data are gradually increasing, and data-driven multivariate statistical process monitoring methods have developed significantly. A weighted k-nearest neighbor process monitoring method based on SPA is proposed for the problem of multimodal properties of process statistics. When the covariance difference between different modes is large, the multimodal characteristics of the original variable space are retained in the statistic feature space. By introducing weights and assigning weights to the distances between statistic samples, the proposed method can regulate the distances between statistic samples in modes with larger covariance and those with smaller covariance to the same scale, overcoming the limitations of the SPA-based process monitoring method in multimodal fault detection.