A knowledge‐based system for preventive maintenance

Fonseca, Daniel J.

doi:10.1111/1468-0394.00146

Cited by 12 publications

(4 citation statements)

References 5 publications

(5 reference statements)

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“…Studies [65][66][67][68][69][70] introduced prescriptive maintenance techniques that may not only predict the condition of a machine in the future but also suggest proactively timed decisions for certain maintenance activities such as inspection, repair, and replacement. Studies [71][72][73][74][75][76] considered the use of knowledge-based approaches in predictive, condition-based, prescriptive, and preventive maintenance.…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Systems and Maintenance Models

Ulansky

Raza

2023

Aerospace

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Maintenance is an essential part of long-term overall equipment effectiveness. Therefore, it is essential to evaluate maintenance policies’ effectiveness in addition to planning them. This study provides a classification of technical systems for selecting maintenance effectiveness indicators and a classification of maintenance models for calculating these indicators. We classified the systems according to signs, such as system maintainability, failure consequences, economic assessment of the failure consequences, and temporary mode of system use. The classification of systems makes it possible to identify 13 subgroups of systems with different indicators of maintenance effectiveness, such as achieved availability, inherent availability, and average maintenance costs per unit of time. When classifying maintenance models, we used signs such as the system structure in terms of reliability, type of inspection, degree of unit restoration, and external manifestations of failure. We identified one hundred and sixty-eight subgroups of maintenance models that differed in their values for specified signs. To illustrate the proposed classification of maintenance models, we derived mathematical equations to calculate all considered effectiveness indicators for one subgroup of models related to condition-based maintenance. Mathematical models have been developed for the case of arbitrary time-to-failure law and imperfect inspection. We show that the use of condition-based maintenance significantly increases availability and reduces the number of inspections by more than half compared with corrective maintenance.

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Section: Literature Reviewmentioning

confidence: 99%

Classification of Systems and Maintenance Models

Ulansky

Raza

2023

Aerospace

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“…Additionally, if you always do what you've always done, you will always get what you've always got (MRO, 2004b). Generally an Expert System for preventive maintenance operations lacks a comprehensive frame work for prioritization of preventive maintenance (Fonseca, 2000).Thus a step change in Predictive Maintenance of Assets requires a clear understanding of what an organization aspires to achieve with their assets and a radically new way of designing and implementing Asset Information Management Systems.…”

Section: Recommendations For Future Asset Information Management Systemsmentioning

confidence: 99%

Decision Making for Predictive Maintenance in Asset Information Management

Faiz¹,

Edirisinghe²

2009

IJIKM

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Asset management is a process of identification, design, construction, operation, and maintenance of physical assets (Wenzler, 2005). An asset-centric approach is vital for the success of an asset intensive organisation as the effective management of assets is a major determinant of organisational success. One key issue in asset information management is the availability of information at the right time, in the right format, before the right person, against the right query, and at the right level. This paper provides a comprehensive and in-depth critical analysis from literature which fulfils an identified need of fusing asset information for predictive maintenance so that decision making can be improved. The critical literature review included also highlights the need for an expert system which integrates reliable information with effective decision-support, under the umbrella of Asset Management. Various elements of asset management were critically reviewed, highlighting the need for more robust Predictive maintenance management for assets. We argue that this is best achieved by a system that, in particular, incorporates Expert System to enhance the quality of predictive maintenance through accurate decision analysis. In addition, it should have fuzzy logic reasoning ability that assists in the decision-making process. Our analysis leads us to propose that Expert System when combined with fuzzy logic provides a better way of decision making in predictive maintenance management of assets.

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“…Much research has been carried out to automate the process. The literature shows an increasing interest in the utilization of intelligent‐knowledge‐based systems for quick regeneration of failure modes, likelihood, and criticality (Fonseca, 2000). However, most of the methods require a considerable amount of modeling to be used effectively (Teoh and Case, 2004).…”

Section: Comparing Existing Approaches With Six‐sigmamentioning

confidence: 99%

Integrating Six‐Sigma with other reliability improvement methods in equipment reliability and maintenance applications

Al‐Mishari

Suliman

2008

Journal of Quality in Maintenance Engineering

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PurposeThe purpose of this paper is to address reported weaknesses with existing equipment reliability improvement methods through their integration into the Six‐Sigma DMAIC methodology.Design/methodology/approachThe evaluation was done by assessing the weaknesses of traditional methods such as reliability centered maintenance (RCM), evaluating what Six‐Sigma could potentially offer to close the gaps, and testing potential improvements through an example application.FindingsIt is concluded that Six‐Sigma addresses many RCM flaws and weaknesses. It is also concluded that Six‐Sigma, if integrated with other reliability techniques, can produce results that are far more objective and dependable.Research limitations/implicationsSix‐Sigma, however, still bears its own cons and limitations. It requires good data which are sometimes unavailable. Six‐Sigma is also lengthier and consumes more resources per single problem since it focuses at one problem at a time.Originality/valueThe introduction of Six‐Sigma into equipment reliability/maintenance applications is quite original since this methodology has traditionally been limited to manufacturing and only recently to administrative processes. The outcome is of significant value as it opens up a new perspective into the development of reliability improvement measures for plant equipment.

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A knowledge‐based system for preventive maintenance

Cited by 12 publications

References 5 publications

Classification of Systems and Maintenance Models

Classification of Systems and Maintenance Models

Decision Making for Predictive Maintenance in Asset Information Management

Integrating Six‐Sigma with other reliability improvement methods in equipment reliability and maintenance applications

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