An enhanced kernel learning data-driven method for multiple fault detection and identification in industrial systems

Traditional fault detection methods focus mainly on a single abnormal condition of the sys-tem. However, successive multiple faults are more common than a single fault in industrial systems. Hence, this paper proposes a novel algorithm for detecting and identifying mul-tiple faults associated with the quality indicators of the process. Considering the dynamic feature and measurement noise in the system, an enhanced kernel learning data-driven (EKLDD) algorithm is designed to improve the performance of modeling and multiple fault detection. In addition, a monitoring scheme is proposed to evaluate the quality status under every fault based on the fault line and the angle statistics. Lastly, a simulation case and a real-world case are presented to illustrate the feasibility and effectiveness of the pro-posed EKLDD method.(c) 2022 Elsevier Inc. All rights reserved.