Abstract
The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various industrial sectors. Many traditional FDD techniques have been developed for checking the existence of a trend or pattern in the process or whether a certain process variable behaves normally or not. However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics. In this paper, we present current research and developments of FDD approaches for process monitoring as well as a broad literature review of many useful FDD approaches.
Highlights
IntroductionWith the advent of industry 4.0, current industrial processes are transforming into smart ones
With the advent of industry 4.0, current industrial processes are transforming into smart ones.In particular, many modernized industrial processes are equipped with several well-elaborated sensors to gather process-related data for discovering faults existing or arising in processes as well as monitoring the process status
The proposed method is an extended version of Kalman filter (KF)-based fault detection and isolation (FDI) for detecting three more critical faults existing in processes
Summary
With the advent of industry 4.0, current industrial processes are transforming into smart ones. Many modernized industrial processes are equipped with several well-elaborated sensors to gather process-related data for discovering faults existing or arising in processes as well as monitoring the process status. For this change of industrial environments with the full-automation of equipment and process (i.e., operation), more cautious supervision that includes process control and suitable corrective actions is required to guarantee the process efficiency [1,2,3]. Among various process supervision techniques, the fault detection and diagnosis (FDD) is a significantly critical control method for accomplishing this task because most industries hope to improve their process performance through a higher level of FDD capability. A malfunction is defined as a sporadic interruption of a process or system execution and is usually a result of faults [5]
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