Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Das, M.Subject: search.filters.subject.Fault Tree Analysis

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    Bayesian Belief Network Analysis for SPAD System in Railways
    (Institute of Electrical and Electronics Engineers Inc., 2024) Das, M.; Mohan, B.R.; Reddy G, R.M.; Chinmaya, C.; Umesh; Reddy G, V.M.; Vismay, P.
    Even with a very strong network of signaling and warning systems in the country, there have been many examples of trains crossing the red signal due to various factors, even in the modern day. These occurrences, known as Signal Passed at Danger (SPAD) events, could potentially result in severe consequences such as train derailments, train collisions, infrastructure collisions, and other dangerous events. Traditionally, these events have been analyzed using the Fault Tree Analysis (FTA) approach. However, when the system grows more complex, FTA too becomes more complex, and tough to maintain simplicity and ease of analysis. This opens the gateway to the exploration of other methods to model and assess such SPAD incidents and similar safety-critical systems in railways. Bayesian belief network (BBN) is considered to be a better model to represent this situation when it comes to handling complexity. This paper focuses on the implementation and advantages of the BBN model over FTA by considering the SPAD system as a case study. Both the FTA and BBN methods are then compared concerning modeling and analysis aspects. © 2024 IEEE.
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    Reliability Analysis Using Bayesian Belief Network on Drone System: A Case Study
    (Institute of Electrical and Electronics Engineers Inc., 2024) Das, M.; Mohan, B.R.; Ram Mohana Reddy, G.; Chhaparwal, E.; Krishna Kumar, K.; Chowdhury, S.; Sharma, S.
    Ensuring the reliability of software components is of paramount importance in safety-critical systems. Grave consequences might occur if software failures in such systems. Hence, predicting software reliability is important in these systems. This research uses Bayesian Belief Network(BBN) and leverages historical failure data to find fault interdependencies, giving much more insights than methodologies like Fault Tree Analysis (FTA) and Reliability Block Diagrams (RBD). By comparing BBNs with these traditional methods, the research shows the dynamic capabilities of BBNs. BBN also shows the capability of using real-time data and machine learning together to increase the software reliability of the software components, making this system much safer. © 2024 IEEE.