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Article type: Research Article
Authors: Abdul Lathif, Syed Ismaila; * | Cruz Antony, J.b | Noel Jeygar Robert, V.c | Aishwarya, D.b
Affiliations: [a] Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamilnadu, India | [b] Department of CSE, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India | [c] School of Computer Science and Engineering, VIT University Chennai Campus, Tamilnadu, India
Correspondence: [*] Corresponding author. Syed Ismail Abdul Lathif, Department of CSE, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai-600062 Tamilnadu, India. E-mail: cse.ismail15@gmail.com.
Abstract: A failure risk assessment must be carried out and potential drilling equipment failure risks must be promptly addressed in order to prevent drilling fluid pollution during offshore oil drilling. The qualitative, comprehensive, and quantitative failure risks for Drilling Permanent Magnetic Synchronous Motors (DPMSM) are examined in this article using a hybrid methodology. First, the Drilling PMSM using Failure Mode Analysis (FMA) method is combined with the Risk Matrix (RM) approach to analyse the risk levels of risk factors individually. Next, the Borda number is introduced to compare the risk levels exactly. To execute a Fuzzy Comprehensive Evaluation (FCE) of the system failure risk, a fuzzy relation matrix of risk factors is generated, and the weight of each risk component is calculated using importance analysis. The failure rate is then determined using fuzzy inference, and the Fault Tree (FT) is then built based on the risk variables. Fault tree analysis is used to compute the system failure rate, and the significance of the bottom event is evaluated. The Bayesian network (BN) is used to depict the Fuzzy Fault Tree (FFT) analysis. By utilizing Bayesian forward causal inference and reverse diagnostic inference to calculate the leaf node failure rate and root node posterior probability, the system’s weak points and potential failure causes are determined.
Keywords: Risk matrix, fuzzy comprehensive evaluation, fault tree, bayesian network, failure mode analysis
DOI: 10.3233/JIFS-224462
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 6, pp. 9281-9295, 2023
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