An Influence-Based Approach for Root Cause Alarm Discovery in Telecom Networks

Zhang, Keli; Kalander, Marcus; Zhou, Min; Zhang, Xi; Ye, Junjian

doi:10.1007/978-3-030-76352-7_16

Cited by 10 publications

(2 citation statements)

References 19 publications

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“…Root Cause Analysis (RCA) is a commonly used method for ensuring the reliability and efficiency of production systems in various domains, including IT operations (Soldani and Brogi, 2022), telecommunications (Zhang et al, 2021), and epidemiology (Landsittel et al, 2020). Incidents that indicate the actual inability of a module to perform its function in the system are inevitable in practice due to the complexity of modern systems.…”

Section: Introductionmentioning

confidence: 99%

AI Empowered Net-RCA for 6G

Qiu¹,

Yang²,

Wang³

et al. 2023

IEEE Network

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We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps). It provides a holistic framework to uncover the complicated metric causal dependencies and automatically locate root causes of incidents. It offers a unified interface for multiple commonly used RCA models, encompassing both graph construction and scoring tasks. This library aims to provide IT operations staff, data scientists, and researchers a one-step solution to rapid model development, model evaluation and deployment to online applications. In particular, our library includes various causal discovery methods to support causal graph construction, and multiple types of root cause scoring methods inspired by Bayesian analysis, graph analysis and causal analysis, etc. Our GUI dashboard offers practitioners an intuitive pointand-click interface, empowering them to easily inject expert knowledge through human interaction. With the ability to visualize causal graphs and the root cause of incidents, practitioners can quickly gain insights and improve their workflow efficiency. This technical report introduces PyRCA's architecture and major functionalities, while also presenting benchmark performance numbers in comparison to various baseline models. Additionally, we demonstrate PyRCA's capabilities through several example use cases.

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Section: Introductionmentioning

confidence: 99%

AI Empowered Net-RCA for 6G

Qiu¹,

Yang²,

Wang³

et al. 2023

IEEE Network

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“…Moreover, Ghosh’s 25 work demonstrated the suitability and applicability of process-accident models in capturing temporal dependence using process data. Zhang et al 26 proposed a novel data-driven framework for root cause alarm localization, combining both causal inference and network embedding techniques. The improvement of the above methods mainly focuses on improving the learning efficiency, and the improvement of the accuracy mainly depends on the process knowledge.…”

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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