Alarm systems play critically important roles for the safe and efficient operation of modern industrial plants. However, most existing industrial alarm systems suffer from poor performance, noticeably having too many alarms to be handled by operators in control rooms. Such alarm overloading is extremely detrimental to the important role played by alarm systems. This paper provides an overview of industrial alarm systems. Four main causes are identified as the culprits for alarm overloading, namely, chattering alarms due to noise and disturbance, alarm variables incorrectly configured, alarm design isolated from related variables, and abnormality propagation owing to physical connections. Industrial examples from a large-scale thermal power plant are provided as supportive evidences. The current research status for industrial alarm systems is summarized by focusing on existing studies related to these main causes. Eight fundamental research problems to be solved are formulated for the complete lifecycle of alarm variables including alarm configuration, alarm design, and alarm removal. Note to Practitioners-Alarm systems are critical assets for operational safety and efficiency of plants in various industrial sectors, such as power and utility, process and manufacturing, and oil and gas. However, industrial alarm systems are generally suffering from alarm overloading. This paper provides an overview of industrial alarm systems, by proposing main causes for alarm overloading, summarizing current research status and formulating open problems. In presenting this overview, we hope to attract direct attentions from more researchers and engineers into the study of industrial alarm systems.

The detection of direct causality, as opposed to indirect causality, is an important and challenging problem in root cause and hazard propagation analysis. Several methods provide effective solutions to this problem when linear relationships between variables are involved. For nonlinear relationships, currently only overall causality analysis can be conducted, but direct causality cannot be identified for such processes. In this paper, we describe a direct causality detection approach suitable for both linear and nonlinear connections. Based on an extension of the transfer entropy approach, a direct transfer entropy (DTE) concept is proposed to detect whether there is a direct information flow pathway from one variable to another. Especially, a differential direct transfer entropy concept is defined for continuous random variables, and a normalization method for the differential direct transfer entropy is presented to determine the connectivity strength of direct causality. The effectiveness of the proposed method is illustrated by several examples, including one experimental case study and one industrial case study.

Recently, a new process monitoring and fault diagnosis method based on slow feature analysis has been developed, which enables concurrent monitoring of both operating point and process dynamics. In this paper, a recursive slow feature analysis algorithm for adaptive process monitoring is put forward to accommodate time-varying processes by updating model parameters and monitoring statistics once a new sample arrives. An important algebraic property of slow feature analysis is first established. We then show that such a property can be violated by online updating with a forgetting factor used, and a remedy is suggested. A novel algorithm based on the rank-one modification and the orthogonal iteration procedure is proposed to recursively adjust the solution to the generalized eigenvalue problem, model parameters, and associated monitoring statistics in a cost-efficient way. In addition, an improved stopping criterion for model updating is proposed based on the statistics relevant to process dynamics, which yields an intelligent maintenance mechanism of monitoring systems. The efficacy of the proposed method is finally evaluated on a real crude heating furnace system.