A local alignment approach to similarity analysis of industrial alarm flood sequences

Hu, Wenkai; Wang, Jiandong; Chen, Tongwen

doi:10.1016/j.conengprac.2016.05.021

Cited by 58 publications

(11 citation statements)

References 25 publications

(32 reference statements)

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“…For example, Cheng 9 proposed an improved Smith–Waterman (SW) algorithm, but the calculation amount increased during the process. To improve computational efficiency, Hu 10 proposed a basic local alignment search tool (BLAST) local comparison algorithm in consideration of priority and other factors. Inspired from pattern matching, Bouillard 11 proposed a new methodology to find alarm correlations with or without prior knowledge about the monitored system.…”

Section: Introductionmentioning

confidence: 99%

Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning

Song

Liu²,

Dong

et al. 2022

ACS Omega

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Due to the abrupt nature of the chemical process, a large number of alarms are often generated at the same time. As a result of the flood of alarms, it largely hinders the operator from making accurate judgments and correct actions for the root cause of the alarm. The existing diagnosis methods for the root cause of alarms are relatively single, and their ability to accurately find out complex accident chains and assist decision making is weak. This paper introduces a method that integrates the knowledge-driven method and the data-driven method to establish an alarm causal network model and then traces the source to realize the alarm root cause diagnosis, and develops the related system modules. The knowledge-driven method uses the hidden causality in the optimized hazard and operability analysis (HAZOP) report, while the data-driven method combines the autoregressive integrated moving average model (ARIMA) and Granger causality test, and the traceability mechanism uses the time-based retrospective reasoning method. In the case study, the practical application of the method is compared with the experimental application in a real petrochemical plant. The results show that this method helps to improve the accuracy of correct diagnosis of the root cause of the alarm and can assist the operators in decision making. Using this method, the root cause diagnosis of alarm can be realized quickly and scientifically, and the probability of misjudgment by operators can be reduced, which has a certain degree of scientificity.

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Section: Introductionmentioning

confidence: 99%

Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning

Song

Liu²,

Dong

et al. 2022

ACS Omega

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“…15 and applied it to multisequence alignment. Hu et al 17 applied a local alignment algorithm with priority information as the judgment criterion. They applied the same cluster structure as that applied in ref.…”

Section: Introductionmentioning

confidence: 99%

“…15 but with a higher alignment accuracy and calculation speed and a greater sensitivity to priority information. 17 Guo et al 18 proposed a sequence matching algorithm based on the accelerated alignment comparison technique to evaluate the similarity between alarm flood sequences. Hu et al 19 determined patterns in alarm flood sequences based on frequent item sets, thereby providing a basis for the configuration of dynamic suppression alarms.…”

Section: Introductionmentioning

confidence: 99%

“…19 Xia and Duan 20 combined the merits of the methods and proposed a multisequence matching strategy considering priority information. 16,17 Most of the above algorithms mine patterns from the perspective of sequence alignment but cannot extract patterns from multiple sequences. Certain methods more focused on the multisequence processing aspect have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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Pattern Mining of Alarm Flood Sequences Using an Improved PrefixSpan Algorithm with Tolerance to Short-Term Order Ambiguity

Zhu

Jin

et al. 2021

Ind. Eng. Chem. Res.

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The alarm system monitors industrial plants in real-time to ensure safe operation. The scale of modern plants is expanding rapidly, processes are becoming increasingly complicated, and the cost of alarm configuration in modern control systems is decreasing. However, alarm systems suffer from low performance. A large number of alarms are often indicated to operators within a short period, known as alarm floods. Analysis and mining of similar patterns among different alarm floods is an efficient approach. Alarm flood analysis facilitates cause analysis of historical flood data, thus locating poorly configured alarms and predicting incoming alarm floods. The strongly correlated alarms almost appear simultaneously, and the order is uncertain. A pattern containing strongly correlated alarms cannot be directly extracted according to the order of occurrence. In this paper, an improved PrefixSpan algorithm is proposed to identify similar patterns with a tolerance to short-term order ambiguity, which treats alarm floods as time-stamped sequences. The effectiveness of the proposed algorithm is verified with Tennessee Eastman process simulations.

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“…In this case, operators cannot handle every alarm properly but can only acknowledge these alarms. As a consequence, the root causes of alarms or critical alarms may be overlooked, and serious negative consequences would arise due to the lack of responses to these alarms [5]. The network will get worse and worse and lead to the disruption of the communication services.…”

Section: Introductionmentioning

confidence: 99%

A Belief Network Reasoning Framework for Fault Localization in Communication Networks

Liang

Liu

2020

Sensors

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A small fault in a large communication network may cause abrupt and large alarms, making the localization of the root cause of failure a difficult task. Traditionally, fault localization is carried out by an operator who uses alarms in alarm lists; however, fault localization process complexity needs to be addressed using more autonomous and intelligent approaches. Here, we present an overall framework that uses a message propagation mechanism of belief networks to address fault localization problems in communication networks. The proposed framework allows for knowledge storage, inference, and message transmission, and can identify a fault’s root cause in an event-driven manner to improve the automation of the fault localization process. Avoiding the computational complexity of traditional Bayesian networks, we perform fault inference in polytrees with a noisy OR-gate model (PTNORgate), which can reduce computational complexity. We also offer a solution to store parameters in a network parameter table, similar to a routing table in communication networks, with the aim of facilitating the development of the algorithm. Case studies and a performance evaluation show that the solution is suitable for fault localization in communication networks in terms of speed and reliability.

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A local alignment approach to similarity analysis of industrial alarm flood sequences

Cited by 58 publications

References 25 publications

Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning

Chemical Process Alarm Root Cause Diagnosis Method Based on the Combination of Data-Knowledge-Driven Method and Time Retrospective Reasoning

Pattern Mining of Alarm Flood Sequences Using an Improved PrefixSpan Algorithm with Tolerance to Short-Term Order Ambiguity

A Belief Network Reasoning Framework for Fault Localization in Communication Networks

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