MULTI–STATE ANALYSIS OF PROCESS STATUS USING MULTILEVEL FLOW MODELLING AND BAYESIAN NETWORK

2016 ◽  
Vol 78 (8-3) ◽  
Author(s):  
Mohamed A. R. Khalil ◽  
Arshad Ahmad ◽  
Tuan Amran Tuan Abdullah ◽  
Ali Al-Shatri ◽  
Ali Al-Shanini
Keyword(s):  
Bayesian Network ◽  
Fault Detection And Diagnosis ◽  
Success Rates ◽  
Probabilistic Information ◽  
Modelling Framework ◽  
Predicting Success ◽  
Detection And Diagnosis ◽  
Probability Information ◽  
Multilevel Flow Modeling

Multilevel Flow Modeling (MFM) model maps functionality of components in a system through logical interconnections and is effective in predicting success rates of tasks undertaken. However, the output of this model is binary, which is taken at its extrema, i.e., success and failure, while in reality, the operational status of plant components often spans between these end. In this paper, a multi-state model is proposed by adding probabilistic information to the modelling framework. Using a heat exchanger pilot plant as a case study, the MFM model is transformed into its fault tree [1] equivalent to incorporate failure probability information. To facilitate computations, the FT model is transformed into Bayesian Network model, and applied for fault detection and diagnosis problems. The results obtained illustrate the effectiveness and feasibility of the proposed method.

scholarly journals A Hierarchical Approach to Improve the Interpretability of Causality Maps for Plant-Wide Fault Identification

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 823
Author(s):  
Natali van Zijl ◽  
Steven Martin Bradshaw ◽  
Lidia Auret ◽  
Tobias Muller Louw
Keyword(s):  
Fault Detection And Diagnosis ◽  
Causality Analysis ◽  
Hierarchical Approach ◽  
Transitive Reduction ◽  
Root Cause ◽  
Processing Plants ◽  
Detection And Diagnosis ◽  
Fault State ◽  
Root Cause Identification

Modern mineral processing plants utilise fault detection and diagnosis to minimise time spent under faulty conditions. However, a fault originating in one plant section (PS) can propagate throughout the entire plant, obscuring its root cause. Causality analysis identifies the cause–effect relationships between process variables and presents them in a causality map to inform root cause identification. This paper presents a novel hierarchical approach for plant-wide causality analysis that decreases the number of nodes in a causality map, improving interpretability and enabling causality analysis as a tool for plant-wide fault diagnosis. Two causality maps are constructed in subsequent stages: first, a dimensionally reduced, plant-wide causality map used to localise the fault to a PS; second, a causality map of the identified PS used to identify the root cause. The hierarchical approach accurately identified the true root cause in a well-understood case study; its plant-wide map consisted of only three nodes compared to 15 nodes in the standard causality map and its transitive reduction. The plant-wide map required less fault-state data, time series in the order of hours or days instead of weeks or months, further motivating its application in rapid root cause analysis.

scholarly journals An Autonomic Cycle of Data Analysis Tasks for the Supervision of HVAC Systems of Smart Building

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3103
Author(s):  
Jose Aguilar ◽  
Douglas Ardila ◽  
Andrés Avendaño ◽  
Felipe Macias ◽  
Camila White ◽  
...  
Keyword(s):  
Data Analysis ◽  
Fault Detection ◽  
Hvac Systems ◽  
Economic Benefits ◽  
Fault Detection And Diagnosis ◽  
Hvac System ◽  
Smart Buildings ◽  
Detection And Diagnosis ◽  
Early Fault Detection

Early fault detection and diagnosis in heating, ventilation and air conditioning (HVAC) systems may reduce the damage of equipment, improving the reliability and safety of smart buildings, generating social and economic benefits. Data models for fault detection and diagnosis are increasingly used for extracting knowledge in the supervisory tasks. This article proposes an autonomic cycle of data analysis tasks (ACODAT) for the supervision of the building’s HVAC systems. Data analysis tasks incorporate data mining models for extracting knowledge from the system monitoring, analyzing abnormal situations and automatically identifying and taking corrective actions. This article shows a case study of a real building’s HVAC system, for the supervision with our ACODAT, where the HVAC subsystems have been installed over the years, providing a good example of a heterogeneous facility. The proposed supervisory functionality of the HVAC system is capable of detecting deviations, such as faults or gradual increment of energy consumption in similar working conditions. The case study shows this capability of the supervisory autonomic cycle, usually a key objective for smart buildings.

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