Purpose: Explore how historical data from a CMMS can be used in order to improve maintenance effectiveness and efficiency of activities, and investigate the possibilities for CBM in the manufacturing industry in the context of digitalization.  Research questions: RQ1: To what extent could condition-based maintenance or other maintenance types being used in order to predict, prevent or in other way eliminate historical breakdowns/faults?  RQ2: Which significance has an organization's degree of maturity to reduce the number of breakdowns?  Method: A case study was performed at Volvo Construction Equipment Operations in Eskilstuna, who manufactures machinery for the construction industry. The case study was compiled in two phases. Phase one was a quantitative study where raw data were collected from a CMMS and tabulated in order to later perform in-depth analysis. Phase two was designed to collect information that generated a wider understanding of the research area, by performing interviews and observations. A literature study was performed to compare the empirical findings with peer-reviewed information to ensure the quality of the study. The data is compiled and analyzed with an abductive approach. The analysis was followed by a discussion of how the research findings could support identifying possibilities of different maintenance types in the future.  Conclusion: The result showed that using historical breakdown data from a CMMS can be useful in order to identify organization’s current state and what possibilities different maintenance types have to decrease the number of breakdowns. To what extent the breakdowns can be decreased relies not only on the maintenance type but also an organizations maturity level. The case study´s result showed that by combining different maintenance types and increasing degree of maturity, Volvo could decrease the historical breakdowns with 86,5%. By only using CBM with current maturity level, 56% of the historical breakdowns could be predicted. However, to decide how many breakdowns that is cost-effective to prevent and precisely what maintenance type that should be used requires a cost analysis which this study is not covering.

Made available in DSpace on 2016-08-17T14:52:58Z (GMT). No. of bitstreams: 1 Raimundo Nonato Rosa Filho.pdf: 1125835 bytes, checksum: 91689e7b58443f6d0eb73d752860ce37 (MD5) Previous issue date: 2005-03-31 / In this work a methodology for reduction of maintenance cost in the on-load tap changers (OLTC) of extra high voltage is proposed. The methodology is based on the use of Artificial Neural Networks (ANN) for the intelligent processing of input signals of the commutator. The neural nets adequately trained allow to create an information system and dedicated diagnosis of the OLTC. This system can interpret and diagnosis the components through the real time input signals in order to delay the power transformer maintenance intervals, foreseeing when the OLTC is going to maintenance have intervention based on its condition. It has been adopted a multiperceptron ANN architecture in which the input vector has 22 components and the output considers only one component with the status of the OLTC condition in function of its operation time. This output information is used to determine the periods of maintenance of the commutators. It is reported an application of the proposed system considering the on load tap changer of an autotransformer bank of 500/230/13.8 kV, 600MVA of Centrais Elétricas do Norte do Brasil S/A (ELETRONORTE). The results indicate the advantages of the maintenance based on the condition using ANN. / Neste trabalho é proposta uma metodologia para redução de custo de manutenção nos comutadores de tap sob carga (OLTC) dos transformadores de potência de extra alta tensão. A metodologia está baseada na utilização de redes neurais artificiais (RNA) para o processamento inteligente dos sinais de entrada dos comutadores. As redes neurais adequadamente treinadas permitem criar um sistema de informação e diagnóstico dedicado a OLTC que podem interpretar e diagnosticar os componentes através das entradas em tempo real de forma a, postergar os intervalos de manutenção, prevendo quando o OLTC deverá sofrer intervenção de manutenção baseada na condição do OLTC. Foi adotada uma arquitetura de RNA de multiperceptron na qual a entrada considera um vetor com 22 entrada e apenas uma saída com o status da condição do OLTC em função do tempo de operação. Essa informação de saída é utilizada para determinar os períodos de manutenção dos comutadores de tap. É realizada uma aplicação do sistema proposto considerando o comutador de tap sob carga de um banco de autotransformador de 500/230/13.8kV, 600MVA da Centrais Elétricas do Norte do Brasil S/A( ELETRONORTE) e os resultados indicam as vantagens da manutenção baseada na condição usando RNA.

Cette thèse est consacrée à la description, la prédiction et la prévention des défaillances de systèmes. Elle se compose de quatre parties relatives à la modélisation stochastique de dégradation, au pronostic de défaillance du système, à l'estimation du niveau de défaillance et à l'optimisation de maintenance.Le processus d'Ornstein-Uhlenbeck (OU) dépendant du temps est introduit dans un objectif de modélisation des dégradations. Sur la base de ce processus, le premier instant de passage d’un niveau de défaillance prédéfini est considéré comme l’instant de défaillance du système considéré. Différentes méthodes sont ensuite proposées pour réaliser le pronostic de défaillance. Dans la suite, le niveau de défaillance associé au processus de dégradation est estimé à partir de la distribution de durée de vie en résolvant un problème inverse de premier passage. Cette approche permet d’associer les enregistrements de défaillance et le suivi de dégradation pour améliorer la qualité du pronostic posé comme un problème de premier passage. Le pronostic de défaillances du système permet d'optimiser sa maintenance. Le cas d'un système contrôlé en permanence est considéré. La caractérisation de l’instant de premier passage permet une rationalisation de la prise de décision de maintenance préventive. L’aide à la décision se fait par la recherche d'un niveau virtuel de défaillance dont le calcul est optimisé en fonction de critères proposés / This thesis is dedicated to describe, predict and prevent system failures. It consists of four issues: i) stochastic degradation modeling, ii) prognosis of system failures, iii) failure level estimation and iv) maintenance optimization. The time-dependent Ornstein-Uhlenbeck (OU) process is introduced for degradation modeling. The time-dependent OU process is interesting from its statistical properties on controllable mean, variance and correlation. Based on such a process, the first passage time is considered as the system failure time to a pre-set failure level. Different methods are then proposed for the prognosis of system failures, which can be classified into three categories: analytical approximations, numerical algorithms and Monte-Carlo simulation methods. Moreover, the failure level is estimated from the lifetime distribution by solving inverse first passage problems. This is to make up the potential gap between failure and degradation records to reinforce the prognosis process via first passage problems. From the prognosis of system failures, the maintenance optimization for a continuously monitored system is performed. By introducing first passage problems, the arrangement of preventive maintenance is simplified. The maintenance decision rule is based on a virtual failure level, which is solution of an optimization problem for proposed objective functions

Aujourd’hui la prédiction des défaillances de certains systèmes industriels est devenue indispensable pour l’amélioration de la fiabilité et de la rentabilité de ces derniers. Cette prédiction s’appuie principalement sur l’analyse d’évolution du niveau de dégradation du système. Pour les systèmes dont l’état de détérioration n’est pas directement observable, la définition d’indicateurs de santé mesurables est nécessaire. Une modélisation du processus de dégradation à partir de ces données peut être ensuite effectuée. Dans cette thèse, nous considérons un ensemble d’indicateurs non-monotones pour un système opérant dans un environnement dynamique. Compte tenu des principales caractéristiques des données ainsi que de l’impact des conditions environnementales et de leur instabilité, une modélisation stochastique de l’évolution de ces indicateurs est proposée. Les modèles proposés se basent principalement sur une combinaison d’un processus de Wiener et de processus de sauts. Les motivations, les méthodes de calibration, l’utilité et les limites de chaque modèle sont discutées. Nous proposons ensuite une approche pour l’aide à la décision concernant les actions de maintenance préventive. Cette approche consiste à évaluer la valeur d’une option réelle qui présente la possibilité d’«Attendre avant d’Agir» suite à un signal d’avertissement sur une défaillance probable. Une application de cette approche pour le cas d'une éolienne équipée d’un système de surveillance et de gestion est traitée / A major concern for engineers and managers nowadays is to make high quality products and highly reliable systems. In this context, reliability analysis and failure prediction, besides of efficient maintenance decision-making are strongly required. Deterioration modeling and analysis is a fundamental step for the understanding and the anticipation of system behavior. Consider a functional system operating in unstable conditions or environment where the deterioration level is not observable and could not be determined by direct measures. For this system a set of measurable health indicator that indirectly reflects the system working conditions and deterioration level can be defined and examined. Considering these indicators, the development of a mathematical model describing the system behavior is required.In this thesis, we consider a set of non-monotone indicators evolving in a dynamic environment. Taking into account the major features of the data evolution as well as the impact of dynamic environment consequences and potential shocks, stochastic models based on Wiener and jump processes are proposed for these indicators. Each model is calibrated and tested, and their limits are discussed. A decision-making approach for preventive maintenance strategies is then proposed. In this approach, knowing the RUL of the system, a simulation-based real options analysis is used in order to determine the best date to maintain. Considering a case study of a wind turbine with PHM structure, the decision optimization approach is described

De nos jours, les systèmes industriels sont de plus en plus complexes tant du point de vue de leur structure logique que des diverses dépendances (dépendances économique, stochastiques et structurelles) entre leurs composants qui peuvent influencer l'optimisation de la maintenance. La Maintenance conditionnelle qui permet de gérer les activités de maintenance en fonction de l’information de surveillance a fait l’objet de beaucoup d'attention au cours des dernières années, mais les dépendances stochastiques sont rarement utilisées dans le processus de prise de décision. Par conséquent, cette thèse a pour objectif de proposer des politiques de maintenance conditionnelle tenant compte des dépendances économiques et stochastiques pour les systèmes multi-composant. En termes de dépendance économique, les politiques proposées sont conçues pour permettre de favoriser les opportunités de grouper des actions de maintenance. Une règle de décision est établie qui permet le groupement de maintenances avec des périodes d'inspection différentes. La dépendance stochastique causée par une part de dégradation commune est modélisée par copules de Lévy. Des politiques de maintenance conditionnelle sont proposées pour profiter de la dépendance stochastique.Nos travaux montrent la nécessité de tenir compte des dépendances économiques et stochastiques pour la prise de décision de maintenance. Les résultats numériques confirment l’avantage de nos politiques par rapport à d’autres politiques existant dans la littérature / Nowadays, industrial systems contain numerous components so that they become more and more complex regarding the logical structures as well as the various dependences (economic, stochastic and structural dependences) between components. The dependences between components have an impact on the maintenance optimization as well as the reliability analysis. Condition-based maintenance which enables to manage maintenance activities based on information collected through monitoring has gained a lot of attention over recent years but stochastic dependences are rarely used in the decision making process. Therefore, this thesis is devoted to propose condition-based maintenance policies which take advantage of both economic and stochastic dependences for multi-component systems. In terms of economic dependence, the proposed maintenance policies are designed to be maximally effective in providing opportunities for maintenance grouping. A decision rule is established to permit the maintenance grouping with different inspection periods. Stochastic dependence due to a common degradation part is modelled by Lévy and Nested Lévy copulas. Condition-based maintenance policies with non-periodic inspection scheme are proposed to make use of stochastic dependence. Our studies show the necessity of taking account of both economic and stochastic dependences in the maintenance decisions. Numerical experiments confirm the advantages of our maintenance policies when compared with other existing policies in the literature

Industriellt underhåll har upplevt en utveckling från det ursprungliga akuta avhjälpande underhållet till dagens möjligheter till underhåll baserat på data, så kallat tillståndsbaserat underhåll (CBM). För CBM genomförs endast underhåll vid behov och detta bestäms av aktuell data från den studerade utrustningen. Onödigt underhåll minimeras och antalet plötsliga haverier minskar. Utvecklingen mot Internet of Things (IoT) ger upphov till en stor mängd data som potentiellt kan användas vid CBM-underhåll. En utmaning uppstår dock i att identifiera sådan data och hur denna data kan användas. Denna studie har syftat till att undersöka hur sådan data kan identifieras och hur den kan tänkas användas vid CBM-underhåll. Studien har utförts tillsammans med Quant i Karlskrona där Quant genomför alla underhållsrelaterade aktiviteter åt ABB High Voltage Cables, ett industriföretag som tillverkar högspänningskablar. Arbetet har utgått från tre frågeställningar som har syftat till att: Identifiera datavariabler som kan tänkas ha relevans för CBM-underhåll. Tolka de identifierade datavariablerna för att bedöma hur de kan användas i CBM. Bedöma lämpligheten av en potentiell CBM-implementation baserat på identifierad data jämfört med existerande underhållsmetod. Arbetet har avgränsats genom att fokusera på ett enskilt företag och en enskild fabrik. Utöver detta har en avgränsning gjorts där fokus lagts på några få enskilda maskiner och komponenter. Sekretess har även behövt beaktas vid hantering av känslig information. Studien har huvudsakligen utförts kvalitativt, genom att på djupet fokusera på ett fåtal maskiner och komponenter. Arbetet har genomförts i nära samarbete med några av Quants anställda. Processdata har samlats in direkt från maskinerna och analyserats genom att identifiera och studera avvikelser i data. Intervjuer av olika slag, kompletterade med dokument, har varit en viktig metod för att inhämta information från anställda på Quant, både kring hur data kan tolkas men även kring hur olika processer fungerar. Analytic hierarchy process (AHP) genomfördes i fokusgrupp med anställda för att bedöma lämplig underhållsstrategi. Ett potentiellt tillvägagångssätt har identifierats som tillåter användning av processdata för CBM hos en särskild komponenttyp på företaget. Metoden behöver implementeras och testas men potential finns att minska underhållskostnaderna. Intressanta avvikelser i processdata har identifierats hos en annan komponent som bör studeras vidare för att förstå om processdata och avvikelserna kan användas i en CBM-kontext eller inte. Förbättringsområden hos företaget har identifierats i tillämpningen av vibrationsmätning, vilket är en metod med god potential att användas för CBM-underhåll och därmed minska underhållskostnaderna. Oljeanalys tillämpas redan men en intressant fundering är hur företagets oljefiltrering påverkar möjligheterna att implementera ett prediktivt underhåll i framtiden. Detta är ett område som framtida studier behöver titta på och bedöma hur det ska tacklas. AHP har även bekräftats vara en användbar metod för att bedöma lämpligaste underhållspolicyn. / Industrial maintenance has experienced an evolution from the initial corrective maintenance to the possibility of using data based maintenance techniques, so called condition-based maintenance (CBM). Maintenance is only performed when needed under CBM and this is decided based on the data retrieved from the studied equipment. Unnecessary maintenance is minimized and the number of sudden breakdowns decreases. The trend towards Internet of Things (IoT) gives rise to a large amount of data that can potentially be used in CBM maintenance. 'A challenge arises in identifying and using such data. This study has aimed to investigate how such data can be identified and how it might be used in CBM maintenance. This study has been carried out together with Quant in Karlskrona, Sweden, where Quant performs all maintenance related activities for ABB High Voltage Cables, an industrial manufacturing company. The study has been based on three questions that have aimed to: Identify data variables that might be relevant for CBM maintenance. Interpret the identified data variables to assess how they can be used in CBM. Assess the suitability of a potential CBM implementation based on the identified data compared to the existing maintenance method. The study has been delimited by focusing on a single company and a single factory. In addition, a delimitation has been made to focus on a few individual machines and components. A nondisclosure agreement also had to be considered when dealing with sensitive information. This study has mainly been conducted qualitatively, by focusing in-depth on a few machines and components. The work has been done in close collaboration with Quant’s employees. Process data has been collected from the machines and analyzed by identifying and studying data anomalies. Interviews, complemented with documents, has been an important method in obtaining information from Quant employees, both regarding how data can be interpreted but also on how the various processes work. Analytic hierarchy process (AHP) was conducted in a focus group with employees to determine the most appropriate maintenance strategy. One potential approach has been identified that allows the use of process data for CBM on a particular type of component at the company. The method needs to be implemented and tested but the potential exists to reduce maintenance costs. Interesting anomalies in the process data have been identified in another component which should be studied further to understand if the process data and the anomalies can be used in a CBM context or not. Areas for improvement at the company have been identified in the application of vibration measurements, which is a method with good potential to be used in CBM maintenance, thereby reducing maintenance costs. Oil analysis is already used but an interesting question is how the company’s oil filtration affects its ability to implement a predictive maintenance scheme in the future. This is an area that future studies need to look at and assess how it should be tackled. AHP has also been confirmed to be a useful method to determine the most appropriate maintenance policy.

The phenomenon of digitization is transforming industries worldwide by introducing new valueproducing opportunities. In the railway industry, market liberalization has resulted in increased competition. To remain profitable in this new market environment, rail operators need to transform and acquire new digital capabilities and tools. By digitizing information-intensive processes with an information system, railway companies can reduce loss of operation time and reduce total maintenance costs. At the same time, the limited research exploring information systems in maintenance management has made it challenging for companies wanting to digitize. Significant attention has been devoted to the separate topics, however research overlapping the two areas of study has been inadequate. The thesis aims to contribute with knowledge to bridge this gap in literature by investigating the opportunities a maintenance organization potentially can capture with an information system and the success factors needed to succeed. By conducting the thesis in collaboration with the Swedish railway maintenance company MTR Tech AB the potential uses of an information system have been identified and assessed. Findings indicate that there are three main business opportunities to obtain from an information system: support of the troubleshooting process, better planning of reactive maintenance and enabling the performance of condition-based maintenance. At the same time, the profitability of an information system was found to be directly linked to its degree of utilization. Our findings have therefore allowed us to conclude that the business opportunity to pursue is the one that is most likely to be carried out fully and successfully in the prevailing circumstances. Lastly, the findings conclude that the success factors needed to capture the desired business opportunities are a dedicated project group, clear communication and information sharing, as well as adequate personnel. / Digitalisering har påverkat och transformerat företag över hela världen genom att erbjuda nya värdeproducerande möjligheter. För att bibehålla konkurrenskraft i en föränderlig omvärld måste järnvägsoperatörer transformera sina företag och förvärva nya digitala lösningar och verktyg kopplade till järnvägsteknologier. Genom att digitalisera informationsintensiva processer med hjälp av informationssystem, blir det möjligt för järnvägsföretag att minska förlust av drifttid samt minska den totala underhållskostnaden. Samtidigt har den begränsade forskningen gällande användning av informationssystem i underhållsorganisationer försvårat digitaliseringsförsöken. Litteratur och tidigare studier har behandlat de två ämnena separat, dock har överlappande forskning varit otillräcklig. Denna studie syftar till att bidra med kunskap för att överbrygga gapet i litteraturen genom att undersöka de vinningar en underhållsorganisation kan erhålla med hjälp av ett informationssystem och de framgångsfaktorer som krävs för att uppnå dem. Genom att utföra denna studie i samarbete med det svenska underhållsbolaget MTR Tech AB har de potentiella användningsområdena av ett informationssystem identifierats. De tre huvudsakliga affärsmöjligheterna som kan erhållas från ett informationssystem är: stödjande av felsökningsprocessen, bättre planering av avhjälpande underhåll, samt möjliggörandet av tillståndsbaserat underhåll. Samtidigt har det visat sig att lönsamheten av ett informationssystem är direkt kopplat till dess utnyttjandegrad. Vi har således dragit slutsatsen att den affärsmöjlighet som bör eftersträvas är den som med största sannolikhet kommer att genomföras framgångsrikt under rådande omständigheter. Slutligen visar våra resultat att de framgångsfaktorer som krävs för att uppnå affärsmöjligheterna är en dedikerad projektgrupp, tydlig kommunikation och informationsdelning, samt lämplig personal.

Condition monitoring (CM) is widely used in industry, and there is a growing interest in applying CM on rail vehicle systems. Condition based maintenance has the possibility to increase system safety and availability while at the sametime reduce the total maintenance costs.This thesis investigates the feasibility of using condition monitoring of suspension element components, in this case dampers, in rail vehicles. There are different methods utilized to detect degradations, ranging from mathematicalmodelling of the system to pure "knowledge-based" methods, using only large amount of data to detect patterns on a larger scale. In this thesis the latter approach is explored, where acceleration signals are evaluated on severalplaces on the axleboxes, bogieframes and the carbody of a rail vehicle simulation model. These signals are picked close to the dampers that are monitored in this study, and frequency response functions (FRF) are computed between axleboxes and bogieframes as well as between bogieframes and carbody. The idea is that the FRF will change as the condition of the dampers change, and thus act as indicators of faults. The FRF are then fed to different classificationalgorithms, that are trained and tested to distinguish between the different damper faults.This thesis further investigates which classification algorithm shows promising results for the problem, and which algorithm performs best in terms of classification accuracy as well as two other measures. Another aspect explored is thepossibility to apply dimensionality reduction to the extracted indicators (features). This thesis is also looking into how the three performance measures used are affected by typical varying operational conditions for a rail vehicle,such as varying excitation and carbody mass. The Linear Support Vector Machine classifier using the whole feature space, and the Linear Discriminant Analysis classifier combined with Principal Component Analysis dimensionality reduction on the feature space both show promising results for the taskof correctly classifying upcoming damper degradations. / Tillståndsövervakning används brett inom industrin och det finns ett ökat intresse för att applicera tillståndsövervakning inom spårfordons olika system. Tillståndsbaserat underhåll kan potentiellt öka ett systems säkerhet och tillgänglighetsamtidigt som det kan minska de totala underhållskostnaderna.Detta examensarbete undersöker möjligheten att applicera tillståndsövervakning av komponenter i fjädringssystem, i detta fall dämpare, hos spårfordon. Det finns olika metoder för att upptäcka försämringar i komponenternas skick, från matematisk modellering av systemet till mer ”kunskaps-baserade” metodersom endast använder stora mängder data för att upptäcka mönster i en större skala. I detta arbete utforskas den sistnämnda metoden, där accelerationssignaler inhämtas från axelboxar, boggieramar samt vagnskorg från en simuleringsmodellav ett spårfordon. Dessa signaler är extraherade nära de dämpare som övervakas, och används för att beräkna frekvenssvarsfunktioner mellan axelboxar och boggieramar, samt mellan boggieramar och vagnskorg. Tanken är att frekvenssvarsfunktionerna förändras när dämparnas skick förändras ochpå så sätt fungera som indikatorer av dämparnas skick. Frekvenssvarsfunktionerna används sedan för att träna och testa olika klassificeringsalgoritmer för att kunna urskilja olika dämparfel.Detta arbete undersöker vidare vilka klassificeringsalgoritmer som visar lovande resultat för detta problem, och vilka av dessa som presterar bäst med avseende på noggrannheten i prediktionerna, samt två andra mått på algoritmernasprestanda. En annan aspekt som undersöks är möjligheten att applicera dimensionalitetsminskning på de extraherade indikatorerna. Detta arbete undersöker också hur de tre prestandamåtten som används påverkas av typiska förändringar i driftsförhållanden för ett spårfordon såsom varierande exciteringfrån spåret och vagnkorgsmassa. Resultaten visar lovande prestanda för klassificeringsalgoritmen ”Linear Support Vector Machine” som använder hela rymden med felindikatorer, samt algoritmen ”Linear Discriminant Analysis” i kombination med ”Principal Component Analysis” dimensionalitetsreducering.

A growing use of sensors on the electric grid has opened the door to new methods of asset management: Distribution System Operators are now looking into conditionbased maintenance, as opposed to the traditional corrective or time-based methods. As an emerging field, the methodology must be constructed from the ground, with the little data available. The focus is put on cross-linked polyethylene medium voltage overhead lines (XLPE MVOHL)asthe asset to manage, and the aim of this work is to study under which conditions the use of sensors to improve maintenance scheduling on those lines is economically profitable. Solving this problem starts with a necessary review of key ageing mechanisms of XLPE MV overhead lines, followed by the identification of sensors which can monitor the quantities behind these mechanisms. Statistical models for the lifetime of electrical assets and economic models for the analysis of investments are also described. From this preliminary study, a condition-based maintenance methodology was devised using the concept of Health Index to gather data from multiple types of sensors into one unique indicator. Using existing literature, this health index is used to dynamically estimate the failure rate of the line. This failure rate is the key to condition-based maintenance scheduling: maintenance operations are triggered when the failure rate reaches a threshold. Selecting one ageing mechanism- electrical stress-, and one type of sensorpartial discharge inductive sensors-, a Python simulation was built (and is shared at the end of this thesis) allowing to compare the cost of predictive maintenance to the cost of corrective maintenance over several decades, with the key parameters clearly identified and analysed. Beyond the methodology in itself, the main result of the work is that the use of sensors is economically profitable in most of the studied conditions. This project also reveals the strong influence of some parameters on this profitability: condition monitoring is particularly justified for short-lived assets, with a narrow distribution of failures. The failure rate threshold must be set carefully as it has a major impact on the analysis: setting it too high leads to an unprofitable scenario. / Den ökande användningen av sensorer i elnätet har öppnat dörren för nya metoder för förvaltning av tillgångar: Distributionsnätsoperatörer tittar nu på tillståndsbaserat underhåll, i motsats till de traditionella korrigerande eller tidsbaserade metoderna. Eftersom det rör sig om ett nytt område måste metoden byggas upp från grunden, med de få data som finns tillgängliga. Fokus ligger på luftledningar av tvärbanden polyeten med medelhög spänning (XLPE MV OHL) som den tillgång som ska förvaltas, och syftet med detta arbete är att undersöka under vilka förhållanden det är ekonomiskt lönsamt att använda sensorer för att förbättra underhållsplaneringen på dessa ledningar. För att lösa detta problem börjar man med en nödvändig genomgång av de viktigaste åldringsmekanismerna för XLPE MV luftledningar, följt av identifiering av sensorer som kan övervaka de kvantiteter som ligger bakom dessa mekanismer. Statistiska modeller för livslängden för elektriska tillgångar och ekonomiska modeller för analys av investeringar beskrivs också. Utifrån denna preliminära studie utarbetades en metod för tillståndsbaserat underhåll med hjälp av begreppet hälsoindex för att samla data från flera olika typer av sensorer till en unik indikator. Med hjälp av befintlig litteratur används detta hälsoindex för att dynamiskt uppskatta felfrekvensen för ledningen. Denna felfrekvens är nyckeln till en tillståndsbaserad underhållsplanering: underhålls åtgärder utlöses när felfrekvensen når ett tröskelvärde. Genom att välja en åldringsmekanism- elektrisk belastning- och en typ av sensor- induktiva sensorer med partiell urladdning- byggdes en Python-simulering (som delas i slutet av denna avhandling) som gör det möjligt att jämföra kostnaden för förebyggande underhåll med kostnaden för korrigerande underhåll under flera decennier, med de viktigaste parametrarna tydligt identifierade och analyserade. Utöver själva metoden är det viktigaste resultatet av arbetet att användningen av sensorer är ekonomiskt lönsam under de flesta av de studerade förhållandena. Projektet visar också att vissa parametrar har ett starkt inflytande på denna lönsamhet: tillståndskontroll är särskilt motiverat för tillgångar med kort livslängd och en snäv fördelning av fel. Tröskelvärdet för felfrekvensen måste sättas med omsorg eftersom det har stor inverkan på analysen: om det sätts för högt leder det till ett olönsamt scenario.

This thesis describes the role of cost effective maintenance in achieving competitive advantages. It explores by means of a survey which maintenance practices are used, and how maintenance policies are selected in Swedish industries. Also, it suggests a model for selecting the most cost effective maintenance policy, and how to improve the effectiveness of condition based maintenance decision-making. Finally it discusses how to assess the impact of maintenance practices on business strategic objectives. The main results achieved in the thesis are 1) A better understanding of maintenance organisation, management, systems and maintenance status in Swedish industry. For example, it was found that about 70% of Swedish companies still consider maintenance as a cost centre. Preventive and predictive maintenance approaches are also emphasised. 2) Most Swedish firms, i.e. about 81%, use the accumulated knowledge and experience within the company as a method for maintenance selection. Besides, about 31% use a method based on modelling the time to failure and optimisation. About 10% use failure mode effect and criticality analysis (FMECA) and decision trees and only 2% use multiple criterion decision-making (MCDM). However, the most used maintenance selection method is not the one most satisfactory to its users. Furthermore, about 30% use a combination of at least two methods. 3) A practical model for selecting and improving the most cost effective maintenance policy was developed. It is characterised by incorporating all the strengths of the four methods used in industry. 4) A mechanistic model for predicting the value of vibration level was verified both at the lab and in a case study. 5) A model for identifying, assessing, monitoring and improving the economic impact of maintenance was developed and tested in a case study. Thus it was proved that maintenance is no longer a cost centre, but could be a profit-generating function. To achieve competitive advantages, companies should do the right thing, e.g. use the most cost effective maintenance policy, and they should do it right, e.g. ensure that they have the right competence. Furthermore, they should apply the never-ending improvement cycle, i.e. Plan-Do-Check-Act, which requires identifying problem areas by assessing the savings and profits generated by maintenance and monitoring the economic impact of the applied maintenance policy. Thus, they would know where investments should be allocated to eliminate the basic reasons for losses and increase savings. The major conclusion is that proper maintenance would improve the quality, efficiency and effectiveness of production systems, and hence enhances company competitiveness, i.e. productivity and value advantages, and long-term profitability.