Sequence Mining Based Alarm Suppression

Dörgő, Gyula; Abonyi, János

doi:10.1109/access.2018.2797247

Cited by 25 publications

(9 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A well-designed log file is essential for in-depth process analysis tasks . An alarm represents a state of the system where a process variable exceeds the warning or alarm limits . To predict potential alarm escalation scenarios, typical alarm sequence patterns have to be explored.…”

Section: Integrated Sequence Compression and Alignment Methods To Pre...mentioning

confidence: 99%

Sequence Compression and Alignment-Based Process Alarm Prediction

Bántay

Sas

Dörgő

et al. 2023

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

With the increasing complexity of production technologies, alarm management becomes more and more important in industrial process control. The overall safety of the plant relies heavily on the situation-aware response time of the staff. This kind of awareness has to be supported by a state-of-the-art alarm management system, which requires broad and up-to-date process-relevant knowledge. The proposed method provides a solution when such information is not fully available. With the utilization of machine learning algorithms, a real-time event scenario prediction can be gained by comparing the frequent event patterns extracted from historical event-log data with the actual online data stream. This study discusses an integrated solution, which combines sequence compression and sequence alignment to predict the most probable alarm progression. The effectiveness and limitations of the proposed method are tested using the data of an industrial delayed-coker plant. The results confirm that the presented parameter-free method identifies the characteristic patternsoperational statesand their progression with high confidence in real time, suggesting it for a wider adoption for sequence analysis.

show abstract

Section: Integrated Sequence Compression and Alignment Methods To Pre...mentioning

confidence: 99%

Sequence Compression and Alignment-Based Process Alarm Prediction

Bántay

Sas

Dörgő

et al. 2023

Ind. Eng. Chem. Res.

View full text Add to dashboard Cite

show abstract

“…A couple of studies in the last decade have tried to address the prioritization problem for alarm management. Naghoosi et al [37] and Foong et al [19] developed a solution with the fuzzy logic method, Kondo et al [38], Dorgo and Abonyi [39] and Niyazmand and Izadi [40] applied data mining to prioritize alarms, Bayesian network modelling like Wunderlich and Niggemann [41], Stief et al [42], Naderpour et al [43]. All the mentioned researchers offer substantial ways to prioritize alarms, but they need sufficient actual operational datasets in all operational modes to increase their accuracy; therefore, it is impossible to apply these methods in the design phase.…”

Section: Alarm Prioritizationmentioning

confidence: 99%

Enhancing Alarm Prioritization in the Alarm Management Lifecycle

2022

View full text Add to dashboard Cite

Despite significant improvements being made in control and safety systems, near-miss incidents and adverse accidents continue to occur in the industry. Indeed, humans have a vital role in process control success or failure due to their responses to abnormal situations and alarms. A broad study on the alarm system performance shows that good rationalization and accurate prioritization of alarms should increase the efficacy of alarm systems and improve operator decision performances. This paper discusses current gaps in alarm prioritization approaches. It then proposes a method based on Graph theory and metrics capabilities to facilitate and improve the alarm prioritization process. The method is developed based on the causal and layer of protection modeling, followed by measuring the graph metrics for prioritization purposes. Finally, the proposed method is evaluated through implementation in a simulated case study. Results show that this approach facilitates similar achievement to the alarm workshop and produces more valuable data to the cascade of abnormal situations in a structured method and shorter time.

show abstract

“…Generally speaking, predictable alarms do not contain useful information for the operators. erefore, automation solutions should handle them before they occur, or completely useless alarms should be suppressed before an announcement is made [2]. Future alarm sequences can also be predicted using historical knowledge of the process [3].…”

Section: Introductionmentioning

confidence: 99%

“…First, correlation analysis-based techniques are widespread, where the aim is to find frequently co-occurring alarms over a short period of time, which can be considered redundant [9]. Second, the frequently occurring longer operational patterns can be considered to be the symptoms of the same malfunction and can be revealed by frequent pattern mining algorithms [10], as well as applied in terms of alarm prediction and suppression [2]. It is also advantageous to apply highly efficient data-driven solutions, like deep learning [11] or decision tree-based classifiers [12].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Process Mining of Process Events and Operator Actions for Alarm Management

et al. 2022

Self Cite

View full text Add to dashboard Cite

Alarm management is an important task to ensure the safety of industrial process technologies. A well-designed alarm system can reduce the workload of operators parallel with the support of the production, which is in line with the approach of Industry 5.0. Using Process Mining tools to explore the operator-related event scenarios requires a goal-oriented log file format that contains the start and the end of the alarms along with the triggered operator actions. The key contribution of the work is that a method is presented that transforms the historical event data of control systems into goal-oriented log files used as inputs of process mining algorithms. The applicability of the proposed process mining-based method is presented concerning the analysis of a hydrofluoric acid alkylation plant. The detailed application examples illustrate how the extracted process models can be interpreted and utilized. The results confirm that applying the tools of process mining in alarm management requires a goal-oriented log-file design.

show abstract

Sequence Mining Based Alarm Suppression

Cited by 25 publications

References 29 publications

Sequence Compression and Alignment-Based Process Alarm Prediction

Sequence Compression and Alignment-Based Process Alarm Prediction

Enhancing Alarm Prioritization in the Alarm Management Lifecycle

Simultaneous Process Mining of Process Events and Operator Actions for Alarm Management

Contact Info

Product

Resources

About