Integrating the root cause analysis to machine learning interpretation for predicting future failure

Aditiyawarman, Taufik; Soedarsono, Johny Wahyuadi; Kaban, Agus Paul Setiawan; Suryadi, Suryadi; Rahmadani, Haryo; Riastuti, Rini

doi:10.1016/j.heliyon.2023.e16946

Cited by 3 publications

(1 citation statement)

References 48 publications

(47 reference statements)

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“…In terms of artificial intelligence algorithms, attribution analysis, and matrix plots, they make a significant contribution by effectively identifying crucial parameters. In addition, the use of machine learning algorithms in conjunction with specialized software's enables the development of efficient monitoring systems for real-time applications [ [22] , [23] , [24] , [25] ].…”

Section: Introductionmentioning

confidence: 99%

Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

Khamaj,

Ali,

Saminathan

et al. 2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

Khamaj,

Ali,

Saminathan

et al. 2024

Heliyon

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Failure Analytics of Defects in Mechanical Engineering Parts Using Root Cause Analysis: Case Study

Berladir,

Hovorun,

Trojanowska

et al. 2024

Lecture Notes in Mechanical Engineering

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Role of artificial intelligence (AI) and machine learning (ML) in the corrosion monitoring processes

Saminathan,

Nashali,

Haqawi

et al. 2024

Zas Mat

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When it comes to the upkeep of engineering structures in a variety of industries, corrosion monitoring systems are an extremely important components. In particular, applications such as storage tanks for hazardous chemicals and weight-bearing structures of large engineering constructions are at the forefront of providing attention to relevance. This is due to the fact that failures experienced by these applications can potentially result in catastrophic consequences. As a result, contemporary methods make use of the application of concepts connected with machine learning and artificial intelligence in order to efficiently monitor and identify corrosion related damages. As a consequence of this, the monitoring system is able to provide the control of the industrial structures with minute-by-minute updates. Therefore, the catastrophe is prevented to a significant degree, and there is a significant possibility of lowering the costs associated with technical procedures that require maintenance. Within the scope of this paper, a comprehensive analysis is conducted on the applications of artificial intelligence and machine learning techniques that are utilized in corrosion monitoring systems across a wide range of industries. Through this assessment, the solutions and efficient corrosion monitoring methods that are specific to the domains made available. Consequently, the purpose of this work is to determine the appropriate technique of monitoring systems for each and every corrosion-related disorder.

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Integrating the root cause analysis to machine learning interpretation for predicting future failure

Cited by 3 publications

References 48 publications

Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

Human factors engineering simulated analysis in administrative, operational and maintenance loops of nuclear reactor control unit using artificial intelligence and machine learning techniques

Failure Analytics of Defects in Mechanical Engineering Parts Using Root Cause Analysis: Case Study

Role of artificial intelligence (AI) and machine learning (ML) in the corrosion monitoring processes

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