Observed and unobserved heterogeneity in failure data analysis

In reality, failure data are often collected under diffract operational conditions (covariates), leading to heterogeneity among the data. Heterogeneity can be classified as observed and unobserved heterogeneity. Un-observed heterogeneity is the effect of unknown, unrecorded, or missing covariates. In most reliability studies, the effect of unobserved covariates is neglected. This may lead to inaccurate reliability modeling, and consequently, wrong operation and maintenance decisions. There is a lack of a systematic approach to model the unobserved covariate in reliability analysis. This paper aims to present a framework for reliability analysis in the presence of unobserved and observed covariates. Here, the unobserved covariates will be analyzed using frailty models. A case study will illustrate the application of the framework

DECISION SUPPORT MODEL IN FAILURE-BASED COMPUTERIZED MAINTENANCE MANAGEMENT SYSTEM FOR SMALL AND MEDIUM INDUSTRIES

Maintenance decision support system is crucial to ensure maintainability and reliability of equipments in production lines. This thesis investigates a few decision support models to aid maintenance management activities in small and medium industries. In order to improve the reliability of resources in production lines, this study introduces a conceptual framework to be used in failure-based maintenance. Maintenance strategies are identified using the Decision-Making Grid model, based on two important factors, including the machines’ downtimes and their frequency of failures. The machines are categorized into three downtime criterions and frequency of failures, which are high, medium and low. This research derived a formula based on maintenance cost, to re-position the machines prior to Decision-Making Grid analysis. Subsequently, the formula on clustering analysis in the Decision-Making Grid model is improved to solve multiple-criteria problem. This research work also introduced a formula to estimate contractor’s response and repair time. The estimates are used as input parameters in the Analytical Hierarchy Process model. The decisions were synthesized using models based on the contractors’ technical skills such as experience in maintenance, skill to diagnose machines and ability to take prompt action during troubleshooting activities. Another important criteria considered in the Analytical Hierarchy Process is the business principles of the contractors, which includes the maintenance quality, tools, equipments and enthusiasm in problem-solving. The raw data collected through observation, interviews and surveys in the case studies to understand some risk factors in small and medium food processing industries. The risk factors are analysed with the Ishikawa Fishbone diagram to reveal delay time in machinery maintenance. The experimental studies are conducted using maintenance records in food processing industries. The Decision Making Grid model can detect the top ten worst production machines on the production lines. The Analytical Hierarchy Process model is used to rank the contractors and their best maintenance practice. This research recommends displaying the results on the production’s indicator boards and implements the strategies on the production shop floor. The proposed models can be used by decision makers to identify maintenance strategies and enhance competitiveness among contractors in failure-based maintenance. The models can be programmed as decision support sub-procedures in computerized maintenance management systems

FRAMEWORK FOR RELIABILITY, MAINTAINABILITY AND AVAILABILITY ANALYSIS OF GAS PROCESSING SYSTEM DURING OPERATION PHASE

In facing many operation challenges such as increased expectation in bottom line performances and escalating overhead costs, petrochemical plants nowadays need to continually strive for higher reliability and availability by means of effective improvement tools. Reliability, maintainability and availability (RAM) analysis has been recognised as one of the strategic tools to improve plant's reliability at operation phase. Nevertheless, the application of RAM among industrial practitioners is still limited generally due to the impracticality and complexity of existing approaches. Hence, it is important to enhance the approaches so that they can be practically applied by companies to assist them in achieving their operational goals. The objectives of this research are to develop frameworks for applying reliability, maintainability and availability analysis of gas processing system at operation phase to improve system operational and maintenance performances. In addition, the study focuses on ways to apply existing statistical approach and incorporate inputs from field experts for prediction of reliability related measures. Furthermore, it explores and highlights major issues involved in implementing RAM analysis in oil and gas industry and offers viable solutions. In this study, systematic analysis on each RAM components are proposed and their roles as strategic improvement and decision making tools are discussed and demonstrated using case studies of two plant systems. In reliability and maintainability (R&M) analysis, two main steps; exploratory and inferential are proposed. Tools such as Pareto, trend plot and hazard functions; Kaplan Meier (KM) and proportional hazard model (PHM), are used in exploratory phase to identify critical elements to system's R&M performances. In inferential analysis, a systematic methodology is presented to assess R&M related measures

A New Stochastic Model for Systems Under General Repairs

Numerous stochastic models for repairable systems have been developed by assuming different time trends, and re- pair effects. In this paper, a new general repair model based on the repair history is presented. Unlike the existing models, the closed- form solutions of the reliability metrics can be derived analytically by solving a set of differential equations. Consequently, the con- fidence bounds of these metrics can be easily estimated. The pro- posed model, as well as the estimation approach, overcomes the drawbacks of the existing models. The practical use of the proposed model is demonstrated by a much-discussed set of data. Compared to the existing models, the new model is convenient, and provides accurate estimation results

Generalized models of repairable systems: A survey via stochastic processes formalism

In this article, we survey the developments in the generalised models of repairable systems reliability during 1990s, particularly the last five years. In this field, we notice the sharp fundamental problem that voluminous complex models were developed but there is an absence of sufficient data of interest for justifying the success in tackling the real engineering problems. Instead of following the myth of using simple models to face the complex reality, we select and review some practical models, particularly the stochastic processes behind them. The Models in three quick growth areas: age models, condition monitoring technique related models, say, proportional intensity and their extensions, and shock and wearing models, including the delay-time models are reviewed. With the belief that only those stochastic processes reflecting the instinct nature of the actual physical processes of repairable systems, without excessive assumptions, may have a better chance to meet the demands of engineers and managers

Resilience, Reliability, and Recoverability (3Rs)

Recent natural and human-made disasters, mortgage derivatives crises, and the need for stable systems in different areas have renewed interest in the concept of resilience, especially as it relates to complex industrial systems with mechanical failures. This concept in the engineering systems (infrastructure) domain could be interpreted as the probability that system conditions exceed an irrevocable tipping point. But the probability in this subject covers the different areas that different approaches and indicators can evaluate. In this context, reliability engineering is used the reliability (uptime) and recoverability (downtime) indicators (or performance indicators) as the most useful probabilistic tools for performance measurement. Therefore, our research penalty area is the resilience concept in combination with reliability and recoverability. It must be said that the resilience evaluators must be considering a diversity of knowledge sources. In this thesis, the literature review points to several important implications for understanding and applying resilience in the engineering area and The Arctic condition. Indeed, we try to understand the application and interaction of different performance-based resilience concepts. In this way, a collection of the most popular performance-based resilience analysis methods with an engineering perspective is added as a state-of-the-art review. The performance indicators studies reveal that operational conditions significantly affect the components, industry activities, and infrastructures performance in various ways. These influential factors (or heterogeneity) can broadly be studied into two groups: observable and unobservable risk factors in probability analysis of system performance. The covariate-based models (regression), such as proportional hazard models (PHM), and their extent are the most popular methods for quantifying observable and unobservable risk factors. The report is organized as follows: After a brief introduction of resilience, chapters 2,3 priorly provide a comprehensive statistical overview of the reliability and recoverability domain research by using large scientific databases such as Scopus and Web of Science. As the first subsection, a detailed review of publications in the reliability and recoverability assessment of the engineering systems in recent years (since 2015) is provided. The second subsection of these chapters focuses on research done in the Arctic region. The last subsection presents covariate-based reliability and recoverability models. Finally, in chapter 4, the first part presents the concept and definitions of resilience. The literature reviews four main perspectives: resilience in engineering systems, resilience in the Arctic area, the integration of “Resilience, Reliability, and Recoverability (3Rs)”, and performance-based resilience models

Study On Impact Of Dust Particles Towards Planetary Ball Milling Machine's Maintenance, Reliability And Performance Using DOE.

Di industri pengeluaran, keupayaan untuk memenuhi kehendak pelanggan dari segi masa penghantaran dan qualiti produk merupakan objektif utama bagi setiap pengeluar. Salah satu kriteria untuk mencapai objektif ini ialah dengan memastikan mesin-mesin untuk proses pengeluaran beroperasi dengan lancar tanpa atau kurang berlakunya kerosakan secara tiba-tiba . Preventive Maintenance (PM) is one of the strategies that can be applied to reduce the machine breakdown problem due to unplanned maintenance. However, the application of PM in term of when is the best time to carry out the PM is an important issue. The answer to this question should be based on an adequate maintenance analysis

Regression analysis of caterpillar 793D haul truck engine failure data and through-life diagnostic information using the proportional hazards model

Thesis (MScEng)--Stellenbosch University, 2012.ENGLISH ABSTRACT: Physical Asset Management (PAM) is becoming a greater concern for companies in industry today. The widely accepted British Standards Institutes’ specification for optimized management of physical assets and infrastructure is PAS55. According to PAS55, PAM is the “systematic and co-ordinated activities and practices through which an organization optimally manages its physical assets, and their associated performance, risks and expenditures over their life cycle for the purpose of achieving its organizational strategic plan”. One key performance area of PAM is Asset Care Plans (ACP). These plans are maintenance strategies which improve or ensure acceptable asset reliability and performance during its useful life. Maintenance strategies such as Condition Based Maintenance (CBM) acts upon Condition Monitoring (CM) data, disregarding the previous failure histories of an asset. Other maintenance strategies, such as Usage Based Maintenance (UBM), is based on previous failure histories, and does not consider CM data. Regression models make use of both CM data and previous failure histories to develop a model which represents the underlying failure behaviour of the asset under study. These models can be of high value in ACP development due to the fact that Residual Useful Life (RUL) can be estimated and/or the long term life cycle cost can be optimized. The objective of this thesis was to model historical failure data and CM data well enough so that RUL or optimized preventive maintenance instant estimations can be made. These estimates were used in decision models to develop maintenance schedules, i.e. ACPs. Several regression models were evaluated to determine the most suitable model to achieve the objectives of this thesis. The model found to be most suitable for this research project was the Proportional Hazards Model (PHM). A comprehensive investigation on the PHM was undertaken focussing on the mathematics and the practical implementation thereof. Data obtained from the South African mining industry was modelled with the Weibull PHM. It was found that the developed model produced estimates which were accurate representations of reality. These findings provide an exciting basis for the development of futureWeibull PHMs that could result in huge maintenance cost savings and reduced failure occurrences.AFRIKAANSE OPSOMMING: Fisiese Bate Bestuur (FBB) is besig om ’n groter bekommernis vir maatskappye in die bedryf te word. Die Britse Standaarde Instituut se spesifikasie vir optimale bestuur van fisiese bates en infrastruktuur is PAS55. Volgens PAS55 is FBB die “sistematiese en gekoördineerde aktiwiteite en praktyke wat deur ’n organisasie optimaal sy fisiese bates, hul verwante prestasie, risiko’s en uitgawes vir die doel van die bereiking van sy organisatoriese strategiese plan beheer oor hul volle lewensiklus te bestuur”. Een Sleutel Fokus Area (SFA) van FBB is Bate Versorgings Plan (BVP) ontwikkeling. Hierdie is onderhouds strategieë wat bate betroubaarheid verbeter of verseker tydens die volle bruikbare lewe van die bate. Een onderhoud strategie is Toestands Gebasseeerde Onderhoud (TGO) wat besluite baseer op Toestand Monitering (TM) informasie maar neem nie die vorige falingsgeskiedenis van die bate in ag nie. Ander onderhoud strategieë soos Gebruik Gebasseerde Onderhoud (GGO) is gebaseer op historiese falingsdata maar neem nie TM inligting in ag nie. Regressiemodelle neem beide TM data en historiese falings geskiedenis data in ag ten einde die onderliggende falings gedrag van die gegewe bate te verteenwoordig. Hierdie modelle kan baie nuttig wees vir BVP ontwikkeling te danke aan die feit dat Bruikbare Oorblywende Lewe (BOL) geskat kan word en/of die langtermyn lewenssilus koste geoptimeer kan word. Die doelwit van hierdie tesis was om historiese falingsdata en TT data goed genoeg te modelleer sodat BOL of optimale langtermyn lewensiklus kostes bepaal kan word om opgeneem te word in BVP ontwikkeling. Hierdie bepalings word dan gebruik in besluitnemings modelle wat gebruik kan word om onderhoud skedules op te stel, d.w.s. om ’n BVP te ontwikkel. Verskeie regressiemodelle was geëvalueer om die regte model te vind waarmee die doel van hierdie tesis te bereik kan word. Die mees geskikte model vir die navorsingsprojek was die Proporsionele Gevaarkoers Model (PGM). ’n Omvattende ondersoek oor die PGM is onderneem wat fokus op die wiskunde en die praktiese implementering daarvan. Data is van die Suid-Afrikaanse mynbedryf verkry en is gemodelleer met behulp van die Weibull PGM. Dit was bevind dat die ontwikkelde model resultate geproduseer het wat ’n akkurate verteenwoordinging van realiteit is. Hierdie bevindinge bied ’n opwindende basis vir die ontwikkeling van toekomstige Weibull Proporsionele Gevaarkoers Modelle wat kan lei tot groot onderhoudskoste besparings en minder onverwagte falings

Resilience Assessment: A Performance‐Based Importance Measure

The resilience of a system can be considered as a function of its reliability and recoverability. Hence, for effective resilience management, the reliability and recoverability of all components which build up the system need to be identified. After that, their importance should be identified using an appropriate model for future resource allocation. The critical infrastructures are under dynamic stress due to operational conditions. Such stress can significantly affect the recoverability and reliability of a system‘s components, the system configuration, and consequently, the importance of components. Hence, their effect on the developed importance measure needs to be identified and then quantified appropriately. The dynamic operational condition can be modeled using the risk factors. However, in most of the available importance measures, the effect of risk factors has not been addressed properly. In this paper, a reliability importance measure has been used to determine the critical components considering the effect of risk factors. The application of the model has been shown through a case study