Alarm Graph Using Machine Learning

alarm ooding and that supports the operator in his decision-making task by providing the root causes of alarms and their probabilities of occurrence. In the following section, we rst introduce the basics of alarm models, in particular Bayesian networks. In Chapter 3, the knowledge- and the machine learning approach are presented.

Therefore, this work proposes the Alarm-Logic-Directed-Graph for a novel hybrid approach. This graph models the alarm logic inherent in the control code, enabling knowledge-based clustering and sorting of the alarm log, which then serves as input data for a subsequent Bayesian network learning. which then serves as input data for a

Alarm management systems have become indispensable in modern industry. Alarms inform the operator of abnormal situations, particularly in the case of equipment failures. Due to the interconnections between various parts of the system, each fault can affect other sections of the system operating normally. As a result, the fault propagates through faultless devices, increasing the number of

This paper presents Alarm Oracle, a sophisticated graph neural network engineered for conducting root cause analysis in the complex milieu of alarm cascade scenarios. Employing an unsupervised approach complemented by the Hawkes process, Alarm Oracle excels in the precise identification of initiating events in alarm cascades.

Most of them use deep learning to approach the RCA of the system. One research by Kai Qin et al. 2022, implemented binary extreme gradient boosting Bi-Xgboost to discover RCA of industrial faults, Bi-Xgboost is proposed for variable contribution analysis. Jiang Wenhao et al. 2022 proposed an alarm propagation graph neural network for

The dataset can be collected through sensors or cameras, then processed and analyzed for alarm prediction using machine-learning algorithms such as support vector machines SVMs, neural networks, and decision trees. Choosing the right algorithm is crucial, and the system must be trained on sequential alarm data.

Then, a method to graph alarm data is presented and graph embedding methods are reviewed. In Section VII, data clustering is described. Finally, in Section IX, a case study is used in machine learning algorithms. D. Node2Vec Method The Node2Vec method was presented in 2016 to enhance the Deepwalk method 13. This method tries to cover the

Therefore, it is of major interest to extend explainable machine learning techniques to ICU massive data analysis in order to improve ICU alarm systems. The purpose of this article is to propose a methodology to automatically identify the threshold values of the clinical variables at which alarm systems must warn healthcare personnel.

ing robust machine learning algorithms, this work ex-plores the use of such tools for alarm prediction. Thus, the main objective of this paper is to con-duct an analysis using real data to achieve effective alarm prediction. The study aims to explore Ma-chine Learning algorithms that can accurately pre-

A machine learning approach is used to perform supervised learning of Bayesian networks from existing alarm data. The goal of this paper is the development of a reusable, extensible alarm system that reduces the redundancy of alarms to avoid alarm flooding and that supports the operator in his decisionmaking task by providing the root causes of