Multichannel profile-based monitoring method and its application in the basic oxygen furnace steelmaking process. (October 2021)

• Record Type: Journal Article
• Authors: Qian, Qingting; Fang, Xiaolei; Xu, Jinwu; Li, Min
• Is Part Of: Journal of manufacturing systems. Volume 61(2021)
• Page Start: 375
• Page End: 390
• Publication Date: 2021-10
• Subjects: Condition monitoring -- Functional data analysis -- Mahalanobis distance -- Support vector data description; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2021.09.010
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: Provide a novel monitoring scheme for multi-channel high-dimensional profile data. Effective for problems of data irregularity, information obscurity, and complex correlation. Enhance the variations associated with anomalies by extracting the first-order derivative. Address the strong linear correlation by transforming data based on Mahalanobis distance. Successfully monitor the splashing anomaly of steelmaking process and ensure the product quality. Abstract: Many industrial processes are equipped with a large number of sensors, which usually generate multichannel high-dimensional profiles that can be used to monitor the health condition and detect anomalies of the processes. However, the data irregularity, information obscurity, complex correlations, and nonlinear structures of the multichannel data pose significant challenges for the development of anomaly detection methodologies. To address these challenges, this article proposes a method, Mahalanobis Distance-based Functional Derivative Support Vector Data Description (MD-FDSVDD), for the process monitoring of applications with multichannel profiles. The proposed method first estimates a smooth function of each profile from its irregularly acquired observations and then takes its derivative function to enhance the characteristics associated with anomalies. Next, the smoothed derivative functions are transformed based on Mahalanobis distance to address the strong linear correlation challenge. Finally, the transformed derivative data are used to construct a functional SVDD model to detect anomalies. The effectiveness of the proposed method is evaluated using a simulated dataset and a real-world dataset from a Basic Oxygen Furnace steelmaking process.