Data driven Bayesian network to predict critical alarm

Modern industrial plants rely on alarm systems to ensure their safe and effective functioning. Alarms give the operator knowledge about the current state of the industrial plants. Trip alarms indicating a trip event indicate the shutdown of systems. Trip events in power plants can be costly and critical for the running of the operation.This paper demonstrates how trips events based on an alarm log from an offshore gas production can be reliably predicted using a Bayesian network. If a trip event is reliably predicted and the main cause of it is identified, it will allow the operator to prevent it. The Bayesian network model developed to predict trip events is purely data-driven and relies only on historic data from the alarms log from offshore gas production. We describe the method used to build the Bayesian network and the approach used to identify the most key alarm related to the Trip. We then assess theperformance of the Bayesian network on the alarm log of an offshore gas production. The preliminary performance results show significant potential in predicting trips and identifying key alarms. The model is developed to support decision-making of a human operator and increase the performance of the plant

Supporting Telecommunication Alarm Management System with Trouble Ticket Prediction

Fault alarm data emanated from heterogeneous telecommunication network services and infrastructures are exploding with network expansions. Managing and tracking the alarms with Trouble Tickets using manual or expert rule- based methods has become challenging due to increase in the complexity of Alarm Management Systems and demand for deployment of highly trained experts. As the size and complexity of networks hike immensely, identifying semantically identical alarms, generated from heterogeneous network elements from diverse vendors, with data-driven methodologies has become imperative to enhance efficiency. In this paper, a data-driven Trouble Ticket prediction models are proposed to leverage Alarm Management Systems. To improve performance, feature extraction, using a sliding time-window and feature engineering, from related history alarm streams is also introduced. The models were trained and validated with a data-set provided by the largest telecommunication provider in Italy. The experimental results showed the promising efficacy of the proposed approach in suppressing false positive alarms with Trouble Ticket prediction

Conversation Exchange Dynamics: A New Signal Primitive for Computer Network Intrusion Detection

As distributed network intrusion detection systems expand to integrate hundreds and possibly thousands of sensors, managing and presenting the associated sensor data becomes an increasingly complex task. Methods of intelligent data reduction are needed to make sense of the wide dimensional variations. We present a new signal primitive we call conversation exchange dynamics (CED) that accentuates anomalies in traffic flow. This signal provides an aggregated primitive that may be used by intrusion detection systems to base detection strategies upon. Indications of the signal in a variety of simulated and actual anomalous network traffic from distributed sensor collections are presented. Specifically, attacks from the MIT Lawrence Livermore IDS data set are considered. We conclude that CED presents a useful signal primitive for assistance in conducting IDS

Agents enabling cyber-physical production systems

In order to be prepared for future challenges facing the industrial production domain, Cyber-Physical Production Systems (CPPS) consisting of intelligent entities which collaborate and exchange information globally are being proclaimed recently as part of Industrie 4.0. In this article the requirements of CPPS and abilities of agents as enabling technology are discussed. The applicability of agents for realizing CPPS is exemplarily shown based on three selected use cases with different requirements regarding real-time and dependability. The paper finally concludes with opportunities and open research issues that need to be faced in order to achieve agent-based CPPSs.info:eu-repo/semantics/publishedVersio