Pattern mining in alarm flood sequences using a modified PrefixSpan algorithm
Related Experiment Video
Updated: Jan 29, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
Tahereh Niyazmand1, Iman Izadi1
1Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran.
Abstract:
Proper monitoring of performance of an alarm system throughout its life cycle is an important factor in safety and reliability of industrial plants. Complexity and extent of modern industrial plants and poor design and management of alarm systems, have increased the importance of monitoring of alarm systems. Alarm floods, defined as a large number of alarms triggered in a short interval, is one of the problems that modern complexes are facing regularly. Many researchers have been focusing on this issue both in academia and industry. One approach to deal with alarm flood is analyzing alarms triggered in different floods and finding similar patterns. The identified patterns could help in locating the root cause of an alarm flood. In this paper a modified PrefixSpan sequential pattern recognition algorithm is used to find alarm patterns in different floods. The effectiveness of the algorithm is demonstrated with real alarm floods from a natural gas processing plant.
Related Concept Videos
Responses to Drought and Flooding
Fixed Action Patterns
Trial and Error and Algorithm
Cis-regulatory Sequences
Design Example: Analyzing Capacity Contours for Flood Risk Assessment
Patterns of Fever

