MAntRA: A framework for model agnostic reliability analysis

Mathpati, Yogesh Chandrakant; More, Kalpesh Sanjay; Tripura, Tapas; Nayek, Rajdip; Chakraborty, Souvik

doi:10.1016/j.ress.2023.109233

Cited by 8 publications

(1 citation statement)

References 54 publications

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“…A strategy based on Bayesian networks (BNs) was put forth by Fan et al (2022) [82] to maximize the gas supply reliability in networks of natural gas pipelines. For timedependent reliability analysis, Mathpati et al (2023) [83] presented a novel modelagnostic data-driven reliability analysis paradigm. By utilizing the ideas of the wellknown multifactorial evolutionary algorithm, Chowdury et al (2023) [84] utilized the ideas of the well-known multifactorial evolutionary algorithm (MFEA) to present a novel method for evolutionary multi-task optimization in the reliability redundancy allocation problem.…”

Section: Reliability Centered Maintenance (Rcm)mentioning

confidence: 99%

A review of reliability techniques for the evaluation of Programmable logic controller

Obele,

Aikhuele,

Nwosu

2024

WJEEE

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PLCs, or programmable logic controllers, are essential parts of contemporary industrial automation systems and are responsible for managing and keeping an eye on a variety of operations. PLC reliability is critical to maintaining industrial systems' continuous and secure operation. A wide range of reliability strategies were used to improve the reliability of Programmable Logic Controllers, and this article methodically looks at them all. The evaluation classified PLC reliability techniques into Root Cause Analysis (RCA), Reliability Centered Maintenance (RCM), Hazard analysis (HA), Reliability block diagram (RBD), Fault tree analysis (FTA), Physics of failure (PoF) and FMEA/FMECA, after thoroughly reviewing the body of literature. The proportion of reviewed papers using either RCA, RCM, FMEA/FMECA, FTA, RBD, RCM, PoF, or Hazard analysis to increase the reliability of PLCs showed that RCA, which makes up 20% of the publications reviewed, has been used the most to increase the reliability of the PLC, followed by HA, RCM, RBD, FTA, and PoF, which account for 17%, 16%, 16%,13%, 10%, and 8% of the articles reviewed, respectively. The paper discusses new developments and trends in PLC reliability, such as the application of machine learning (ML) and artificial intelligence (AI) to fault detection and predictive maintenance.

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