O impacto do Homem sobre a Terra, desde a extração intensiva dos recursos da natureza, extinção das espécies, emissões para o ar e água, consumo de energia e água até as mudanças climáticas do planeta, exigem medidas de contenção e preservação do meio ambiente. O futuro da humanidade é questionado e algo deve ser feito. Nesse contexto surge o conceito de desenvolvimento sustentável. Responsável pelo consumo de 40 a 50% da matéria-prima mundial, 12% da água e 40% das emissões de gases de efeito estufa, a indústria da construção possui importante papel na mitigação dos efeitos nocivos para o meio ambiente e capacidade de atingir altos índices de sustentabilidade. Dentro do ciclo de vida de uma edificação, a fase de Operação/Uso provoca grandes consequências ao meio ambiente e oferece grandes oportunidades de redução dos efeitos adversos. Nessa fase as atividades de manutenção predial acabam por ter seu valor reconhecido. As diversas estratégias de manutenção, em especial a manutenção preventiva baseada em condição, colaboram efetivamente para a durabilidade, desempenho e função da edificação e seus sistemas prediais. Procedimentos de manutenção estão associados à baixa qualidade do ar. A manutenção de sistemas de ar condicionado tem especial destaque devido às consequências para a saúde, bem-estar e produtividade dos ocupantes do edifício. / Due to extensive extraction of natures resources, extinction of species, emission of gases into the atmosphere, water and power consumption, to climatic changes of the planet, mankinds impact on earth requires containment and preservation of the environment. The future of humanity is being questioned and something must be done. Responsible for the consumption of 40 to 50% of raw materials worldwide, 12% of water and 40% of emissions of gases into the atmosphere causing the greenhouse effect, the construction industry has an important role in alleviating the adverse impacts on the environment and the ability to achieve high sustainable levels. Within the lifecycle of a building, the phase of operation/use causes significant environmental consequences but also offers opportunities to alleviate adverse effects. In this phase, the value of building maintenance activities has been recognized. The various strategies for maintenance, principally preventive condition- based maintenance should collaborate effectively towards the durability, performance, function and systems of a building. Air-conditioning maintenance procedures are associated with poor air quality. The maintenance of air-conditioning systems has a special significance due to the effects on health, well-being and productivity of the occupants of the building.

Electrical contacts provide a well-proven solution to switching various loads in a wide variety of applications, such as power distribution, control applications, automotive and telecommunications. However, electrical contacts are known for limited reliability due to degradation effects upon the switching contacts due to arcing and fretting. Essentially, the life of the device may be determined by the limited life of the contacts. Failure to trip, spurious tripping and contact welding can, in critical applications such as control systems for avionics and nuclear power application, cause significant costs due to downtime, as well as safety implications. Prognostics provides a way to assess the remaining useful life (RUL) of a component based on its current state of health and its anticipated future usage and operating conditions. In this thesis, the effects of contact wear on a set of electromagnetic relays used in an avionic power controller is examined, and how contact resistance combined with a prognostic approach, can be used to ascertain the RUL of the device. Two methodologies are presented, firstly a Physics based Model (PbM) of the degradation using the predicted material loss due to arc damage. Secondly a computationally efficient technique using posterior degradation data to form a state space model in real time via a Sliding Window Recursive Least Squares (SWRLS) algorithm. Health monitoring using the presented techniques can provide knowledge of impending failure in high reliability applications where the risks associated with loss-of-functionality are too high to endure. The future states of the systems has been estimated based on a Particle and Kalman-filter projection of the models via a Bayesian framework. Performance of the prognostication health management algorithm during the contacts life has been quantified using performance evaluation metrics. Model predictions have been correlated with experimental data. Prognostic metrics including Prognostic Horizon (PH), alpha-Lamda (α-λ), and Relative Accuracy have been used to assess the performance of the damage proxies and a comparison of the two models made.

The thesis presents a framework for integrating models, simulation, and experimental data to diagnose incipient failure modes and prognosticate the remaining useful life of critical components, with an application to the main transmission of a helicopter. Although the helicopter example is used to illustrate the methodology presented, by appropriately adapting modules, the architecture can be applied to a variety of similar engineering systems. Models of the kind referenced are commonly referred to in the literature as physical or physics-based models. Such models utilize a mathematical description of some of the natural laws that govern system behaviors. The methodology presented considers separately the aspects of diagnosis and prognosis of engineering systems, but a similar generic framework is proposed for both. The methodology is tested and validated through comparison of results to data from experiments carried out on helicopters in operation and a test cell employing a prototypical helicopter gearbox. Two kinds of experiments have been used. The first one retrieved vibration data from several healthy and faulted aircraft transmissions in operation. The second is a seeded-fault damage-progression test providing gearbox vibration data and ground truth data of increasing crack lengths. For both kinds of experiments, vibration data were collected through a number of accelerometers mounted on the frame of the transmission gearbox. The applied architecture consists of modules with such key elements as the modeling of vibration signatures, extraction of descriptive vibratory features, finite element analysis of a gearbox component, and characterization of fracture progression. Contributions of the thesis include: (1) generic model-based fault diagnosis and failure prognosis methodologies, readily applicable to a dynamic large-scale mechanical system; (2) the characterization of the vibration signals of a class of complex rotary systems through model-based techniques; (3) a reverse engineering approach for fault identification using simulated vibration data; (4) the utilization of models of a faulted planetary gear transmission to classify descriptive system parameters either as fault-sensitive or fault-insensitive; and (5) guidelines for the integration of the model-based diagnosis and prognosis architectures into prognostic algorithms aimed at determining the remaining useful life of failing components.

Condition-Based Maintenance (CBM) and Prognostics and Health Management (PHM) technologies aim at improving the availability, reliability, maintainability, and safety of systems through the development of fault diagnostic and failure prognostic algorithms. In complex engineering systems, such as aircraft, power plants, etc., the prognostic activities have been limited to the component-level, primarily due to the complexity of large-scale engineering systems. However, the output of these prognostic algorithms can be practically useful for the system managers, operators, or maintenance personnel, only if it helps them in making decisions, which are based on system-level parameters. Therefore, there is an emerging need to build health assessment methodologies at the system-level. This research employs techniques from the field of design-of-experiments to build response surface metamodels at the system-level that are built on the foundations provided by component-level damage models.

O impacto do Homem sobre a Terra, desde a extração intensiva dos recursos da natureza, extinção das espécies, emissões para o ar e água, consumo de energia e água até as mudanças climáticas do planeta, exigem medidas de contenção e preservação do meio ambiente. O futuro da humanidade é questionado e algo deve ser feito. Nesse contexto surge o conceito de desenvolvimento sustentável. Responsável pelo consumo de 40 a 50% da matéria-prima mundial, 12% da água e 40% das emissões de gases de efeito estufa, a indústria da construção possui importante papel na mitigação dos efeitos nocivos para o meio ambiente e capacidade de atingir altos índices de sustentabilidade. Dentro do ciclo de vida de uma edificação, a fase de Operação/Uso provoca grandes consequências ao meio ambiente e oferece grandes oportunidades de redução dos efeitos adversos. Nessa fase as atividades de manutenção predial acabam por ter seu valor reconhecido. As diversas estratégias de manutenção, em especial a manutenção preventiva baseada em condição, colaboram efetivamente para a durabilidade, desempenho e função da edificação e seus sistemas prediais. Procedimentos de manutenção estão associados à baixa qualidade do ar. A manutenção de sistemas de ar condicionado tem especial destaque devido às consequências para a saúde, bem-estar e produtividade dos ocupantes do edifício. / Due to extensive extraction of natures resources, extinction of species, emission of gases into the atmosphere, water and power consumption, to climatic changes of the planet, mankinds impact on earth requires containment and preservation of the environment. The future of humanity is being questioned and something must be done. Responsible for the consumption of 40 to 50% of raw materials worldwide, 12% of water and 40% of emissions of gases into the atmosphere causing the greenhouse effect, the construction industry has an important role in alleviating the adverse impacts on the environment and the ability to achieve high sustainable levels. Within the lifecycle of a building, the phase of operation/use causes significant environmental consequences but also offers opportunities to alleviate adverse effects. In this phase, the value of building maintenance activities has been recognized. The various strategies for maintenance, principally preventive condition- based maintenance should collaborate effectively towards the durability, performance, function and systems of a building. Air-conditioning maintenance procedures are associated with poor air quality. The maintenance of air-conditioning systems has a special significance due to the effects on health, well-being and productivity of the occupants of the building.

Ett transformatorfel kan få stora ekonomiska konsekvenser och påverka ett elkraftsystems driftsäkerhet. Genom att övervaka en transformators tillstånd kan haverier och avbrott undvikas. Tillståndsövervakning möjliggör även att förebyggande underhåll kan planeras effektivt. Det här arbetet har undersökt tillgängliga diagnostikmetoder och övervakningstekniker för tillståndsbedömning av transformatorer.  Tillgänglig felstatistik för transformatorer har studerats ingående för att förstå vilka fel som kan uppstå i en transformator och i vilken grad de förekommer. Eftersom felen har olika konsekvenser för transformatorns fortsatta drift har dessa undersökts. Befintliga metoder för att upptäcka fel har dokumenterats. Detta har utförts genom att studera tillgänglig litteratur och via intervjuer med företag som tillverkar övervakningsutrustning för transformatorer. Övervakningsmetoderna har jämförts med avseende på kostnad, livslängd och noggrannhet. Utifrån dessa analyser har en rekommendation tagits fram över vilka övervakningsmetoder som är mest lämpade för Svenska Kraftnät. Det finns verktyg för att kvantifiera resultaten från övervakningsmetoder till lättolkad information. Ett sådant är hälsoindexet, som komprimerar data från olika mätningar, till ett enskilt värde som representerar en komponents tillstånd. En fallstudie har utförts där tre transformatorers tillstånd under perioden 2002–2015 har undersökts med ett befintligt hälsoindex. Resultatet visar att hälsoindex kan utnyttjas för att indikera transformatorernas tillstånd. Nackdelen med hälsoindexet som användes är att indexet är fortsatt högt oavsett kvaliteten på isolationsmaterialet. Därför föreslås en alternativ modell för en hälsoindexberäkning, i vilken pappersåldringen beaktas i större utsträckning. / A transformer fault could affect the reliability of a power system and have serious economic consequences. Therefore there is an increasing demand among electrical utilities to assess the actual condition of their transformers and hence detect faults before they occur. Condition monitoring has the potential to reduce operating costs and improve the reliability of operation. The aim of this master thesis has been to investigate available diagnostic measurement techniques that can assess the condition of power transformers. Transformer failure statistics has been studied in order to understand what different faults that can occur and to which extent they appear. The consequences of different faults have been investigated. Available measurement techniques were coupled with what faults they can detect. The diagnostic measurement techniques have also been compared in regard to cost, lifetime and accuracy. The comparison resulted in a recommendation over which measurement techniques that are most suitable for the Swedish transmission system operator, Svenska Kraftnät. There is a need among electrical utilities of economic and technical justifications for optimal investment and maintenance decisions. One available asset management technique that enables this is the health index. A health index is a tool that combines available measurement data from operating observations, field inspections and laboratory testing into an objective and quantitative index. The index provides information about the overall condition of an asset. A case study has been conducted where an available health index was used to estimate the condition of three transformers. The results indicate that the health index presents an efficient way to represent the condition of transformers. The disadvantage of the used index is that it implies a good condition even when the quality of the transformer insulation paper is poor. An alternative health index is therefore proposed where the paper insulation quality has a larger impact on the index.

Intresset för effektivisering av underhåll på järnvägen i Sverige växer, järnvägsinfrastrukturen utsätts för högre belastning på grund av den ökade trafiken vilket innebär mer slitage och kortare tidsfönster för inspektion och underhåll. Digitalisering av underhåll på järnvägen bidrar med att säkerställa kontinuerlig drift, effektivt underhåll, planering och minskar olyckor. Realtidsöverföring av data är teknik som är centralt för att mäta tillståndet på järnvägen. Tekniken har visats effektivisera det förebyggande underhållet av järnvägen med enheter som mäter tillståndet på räls och lok i realtid, tekniken benämns som tillståndsbaserade underhållssystem. Syftet med denna studie är att identifiera de problemfaktorer som påverkar användningen av tillståndsbaserat underhåll med utgångspunkt från inblandade aktörer. Studiens använder sig av aktörs-nätverksteorin för att identifiera hur problemfaktorerna förhåller sig till inblandade aktörer och hur användningen av tillståndsbaserat underhåll påverkas av faktorerna. Data samlades in via semistrukturerade intervjuer och tolkades med en innehållsanalys. Resultatet indikerar att komplexitet, samverkan, datahantering, juridiska aspekter, ekonomiska aspekter, underhållsaspekter och kompetensnivå är kategorier över de identifierade problemfaktorer som kan bidra med problem kring användningen av tillståndsbaserat underhåll. / Interest in efficientmaintenance on the railway in Sweden is growing, the railway infrastructure is exposed to higher loads due to the increased traffic, which leads to increased tear on the railway infrastructure and shorter time windows for inspection and maintenance. Digitalization of maintenance on the railway helps to ensure continuous operation, efficient maintenance, planning and reduces accidents. Real-time data transmission is technology that is central to measuring the condition of the railway. The technology has been proven to improve the preventive maintenance of the railway with units that measure the condition of rails and locomotives in real time, the technology is referred to as condition-based maintenance. The purpose of this study is to identify the problem factors that have an influencing effect on the use of condition-based maintenance systems based on the actors involved. The study uses the Actor Network Theory to identify how the problem factors relate to the actors involved and how the use of condition-based maintenance is affected by the factors. Data were collected via semi-structured interviews and interpreted with a content analysis. The results indicate that complexity, collaboration, data management, legal aspects, financial aspects, maintenance aspects and competence level are categories over factors that can contribute to problems around the use of condition-based maintenance.

Railways are a main element of a sustainable transport policy in several countries as they are considered a safe, efficient and green mode of transportation. Owing to these advantages, there is a cumulative request for the railway industry to increase the performance, the capacity and the availability in addition to safely transport goods and people at higher speeds. To meet the demand, large adjustment of the infrastructure and improvement of maintenance process are required.  Inspection activities are essential in establishing the required maintenance, and it is periodically required to reduce unexpected failures and to prevent dangerous consequences.  Maintenance of railway catenary systems is a critical task for warranting the safety of electrical railway operation.Usually, the catenary inspection is performed manually by trained personnel. However, as in all human-based inspections characterized by slowness and lack of objectivity, might have a number of crucial disadvantages and potentially lead to dangerous consequences. With the rapid progress of artificial intelligence, it is appropriate for computer vision detection approaches to replace the traditional manual methods during inspections.  In this thesis, a strategy for monitoring the health of catenary wires is developed, which include the various steps needed to detect anomalies in this component. Moreover, a solution for detecting different types of wires in the railway catenary system was implemented, in which a deep learning framework is developed by combining the Convolutional Neural Network (CNN) and the Region Proposal Network (RPN).

Just like the rest of society, the use of digital technology has throughout the years become a more crucial part for train companies to improve performance, meet customer demands, improve operation and maintenance processes to increase efficiency and reliability. These solutions have through time developed to become part of a cluster named Digital Asset Management (DAM). A highly relevant segment of DAM is the maintenance and diagnostic segment (M&D) involving solutions such as diagnostic systems, and predictive maintenance. The purpose of this thesis was to investigate what the market of M&D looks like in Sweden given its complex market structure with approximately 40 private train owners. A multi-case study was conducted in which data was collected through eight semi-structured interviews with employees from different key companies in the Swedish railway industry. Based on the interviews, there are several challenges within the digitalization process in the maintenance and diagnostic segment of DAM. These are related to the following: data availability, data analysis and modelling, investments in knowledge and skills, ecosystem perspective and lastly, maintenance optimization. The role of legislation and its impact on the digitalization was studied. The transition to working with a full condition-based strategy is difficult due to the existing challenges. Instead, it is deemed optimal to implement a hybrid strategy consisting both of conventional/time-based maintenance and condition-based maintenance for applicable components given the current state of the industry. / Precis som resten av samhället har användningen av digital teknik under åren blivit en allt viktigare del för tågföretag för att förbättra prestanda, möta kundkrav, förbättra drifts- och underhållsprocesser för att öka effektivitet och tillförlitlighet. Dessa lösningar har med tiden utvecklats och blivit en del av ett kluster som kallas för Digital Asset Management (DAM) eller på svenska, digital tillgångsförvaltning. En central del av DAM är segmentet för underhåll och diagnostik (M&D) som involverar lösningar som diagnostiksystem och prediktivt underhåll. Syftet var att undersöka hur marknaden för M&D ser ut i Sverige med tanke på Sveriges komplexa marknadsstruktur. Data samlades in genom en flerfallstudie från åtta semistrukturerade intervjuer med anställda från olika nyckelföretag inom den svenska järnvägsbranschen. Baserat på intervjuerna att det finns flera utmaningar inom digitaliseringsprocessen inom segmentet för underhåll och diagnostik inom DAM. Utmaningarna är relaterade till följande områden: tillgänglighet av data, dataanalys och modellering, investeringar i kunskap och kompetens, ekosystemperspektiv och slutligen, optimering av underhåll. Lagstiftningens roll visade sig spela en viktig roll. Det konstaterades att övergången till att arbeta med en helt konditionsbaserad strategi är komplext. Med hänsyn till branschens nuvarande tillstånd är det i stället optimalt att implementera en ”hybridstrategi” som består av både konventionellt/tidsbaserat underhåll och konditionsbaserat underhåll för tillämpliga komponenter.

<p>  </p> <p>The integration of Industry 4.0 technologies such as cyber-physical systems, the internet of things, and artificial intelligence has revolutionized the traditional manufacturing systems, making them smart and digital. Maintenance, a critical component of manufacturing, has been incorporated with data-driven strategies such as prognostic and health management (PHM) to improve production efficiency and reliability. This is achieved by real-time sensing and AI-based modeling, which monitor the health condition of operational equipment for fault detection or failure prediction. The results generated by these models provide crucial support for decision-making processes in manufacturing, ranging from maintenance scheduling to production management.</p> <p>This research focuses on data-driven machine health models based on deep learning in manufacturing systems and explores three directions towards the practical implementation of PHM: model interpretation, model adaptability and robustness enhancement, and cost-benefit analysis of maintenance strategies. In terms of model interpretation, the RNN-LSTM-based model prediction on bearing health estimation was analyzed, and the relationship between the model input and output was investigated. The adoption of the LRP technique improved the explainability of the LSTM model beyond predictive maintenance applications. To enhance model adaptability and robustness, a Transformer-based method was developed for fault diagnosis and novel fault detection, which achieved superior performance compared to conventional fault classification AI-based models. The decision-making aspect of PHM was addressed by conducting a cost-benefit analysis on different maintenance strategies, which provided a new perspective for decision-makers in maintenance management.</p>