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This thesis is a study with focus on real estate companies for one of several sub-projects under “Stadens kontrollrum” initiative in Västerås. “Stadens kontrollrum” is a concept that brought together expertise from various fields of industry, research and government to create a platform that will aggregate data from different stakeholders and proposed services to achieve the goal of making Västerås a smart and sustainable city. Our project aims to extend “Stadens kontrollrum” platform in order to make it beneficial for real estate companies. In this case study, we applied expert driven methodology, i.e. with domain experts. A detailed literature review has been performed. We identified user requirements based on the information gathered during workshops with nine participants from real estate and utility companies; interviews with three experts from Mälarenergi. During the study, we identified that data visualisation, predictive maintenance and big data analysis for decision making are the main tools, among others, that should be applied to facilitate user needs. Based on user requirements, we have suggested an architecture of a module for the “Stadens kontrollrum” platform that includes those features. To verify feasibility of the solution, a prototype was built and evaluated with a group of four experts from Mälarenergi. The prototype is going to serve as a live demo in workshops and further discussions with the potential users later in the project. A full prototype of the solution is planned to be implemented in the next stage of the project.

abstract: A quantitative analysis of a system that has a complex reliability structure always involves considerable challenges. This dissertation mainly addresses uncertainty in- herent in complicated reliability structures that may cause unexpected and undesired results. The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages. In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules. In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression. In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2018

With a predictive model that can predict failures of a manufacturing machine, many benefits can be obtained. Unnecessary downtime and accidents can be avoided. In this study a wood chipper which has 12 replaceable knives was examined. The specific task was to create a predictive model that can predict if a knife change is needed or not. To create a predictive model, supervised machine learning was used. Decision forest was the algorithm used in this study. Data samples were collected from vibration measurements. Each sample was labeled with help of ocular inspections of the knives. Microsoft Azure learning studio was the workspace used to train all models. The data set acquired consist of 106 samples, were only 9 samples belongs to the minority class. Two strategies of training a model were used, with and without oversampling. The result for the best model without oversampling obtained 87.5% precision and 77.8% recall. The best model with oversampling achieved 79% precision and 86.7% recall. This result indicates that the trained models can be useful. However, the validity of the result has been hurt by a small data set and many uncertainness of acquiring the data set.

Submitted by ANDERSON INÁCIO JUNQUEIRA JUNIOR null (anderson.inacio@unirv.edu.br) on 2016-10-07T04:28:58Z No. of bitstreams: 1 Dissertação - Anderson Inácio.pdf: 7113990 bytes, checksum: 9e01964850a125e0dfa6f5661554c9ce (MD5) / Approved for entry into archive by Juliano Benedito Ferreira (julianoferreira@reitoria.unesp.br) on 2016-10-13T20:07:04Z (GMT) No. of bitstreams: 1 junqueirajunior_ai_me_ilha.pdf: 7113990 bytes, checksum: 9e01964850a125e0dfa6f5661554c9ce (MD5) / Made available in DSpace on 2016-10-13T20:07:04Z (GMT). No. of bitstreams: 1 junqueirajunior_ai_me_ilha.pdf: 7113990 bytes, checksum: 9e01964850a125e0dfa6f5661554c9ce (MD5) Previous issue date: 2016-08-04 / O presente trabalho apresenta um projeto e construção de um protótipo para separar partículas de desgaste em óleos lubrificantes. Devido à necessidade da confiabilidade de máquinas e equipamentos para a redução de custos fabris, as indústrias recorreram às ações preditivas de manutenção. Dentre as várias ações preditivas de manutenção pode-se citar a análise de lubrificantes. Lubrificantes são materiais colocados entre duas superfícies interativas afim de preencher as irregularidades superficiais, assim reduzindo o atrito e o desgaste. Os lubrificantes podem ser sólidos, semifluidos e fluidos. O óleo é um lubrificante líquido, partículas de desgaste presentes no óleo podem danificar componentes vitais de máquinas. Um método utilizado para analisar as partículas de desgaste presentes no óleo lubrificante é a ferrografia qualitativa. Com base em fundamentos teóricos de obras renomadas no meio cientifico, o objetivo do presente trabalho é projetar e construir um protótipo separador rotativo de partículas de baixo custo, para obter ferrogramas quantitativos de boa qualidade e comparar com os modelos convencionais encontrados no mercado. Através de cinco combinações diferentes de ímãs permanentes foi possível obter ferrogramas, sendo que as combinações 03, 04 e 05 apresentaram melhor qualidade de formação de anéis de partículas ferrosas. A combinação 04 apresentou apenas dois anéis, estes são mais fidedignos em relação aos anéis do aparelho convencional. / This paper presents a project and construction of a prototype to separate wear particles in lubricating oils. Due to the need for reliability of machines and equipment to reduce manufacturing costs, manufacturers have turned to predictive maintenance actions. Among the various predictive maintenance actions can be cited the analysis of lubricants. Lubricants are interactive material placed between two surfaces in order to fill the surface irregularities, thereby reducing friction and wear. The lubricants may be solid, slurries and fluids. The oil is a liquid lubricant, wear particles in the oil can damage critical components of machinery. A method for analyzing wear particles in the lubricating oil is qualitative ferrography. Based on theoretical foundations of renowned works in the scientific environment, the objective of this work is to project and construct a rotary separator prototype low cost particles for quantitative ferrogramas good quality and compare with conventional models available on the market. Through five different combinations of permanent magnets was possible to obtain ferrogramas, and combinations 03, 04 and 05 showed better quality training rings of ferrous particles. The combination 04 had only two rings, these are more reliable with respect to the rings of the conventional device.

Orientador: Aparecido Carlos Gonçalves / Resumo: O presente trabalho apresenta um projeto e construção de um protótipo para separar partículas de desgaste em óleos lubrificantes. Devido à necessidade da confiabilidade de máquinas e equipamentos para a redução de custos fabris, as indústrias recorreram às ações preditivas de manutenção. Dentre as várias ações preditivas de manutenção pode-se citar a análise de lubrificantes. Lubrificantes são materiais colocados entre duas superfícies interativas afim de preencher as irregularidades superficiais, assim reduzindo o atrito e o desgaste. Os lubrificantes podem ser sólidos, semifluidos e fluidos. O óleo é um lubrificante líquido, partículas de desgaste presentes no óleo podem danificar componentes vitais de máquinas. Um método utilizado para analisar as partículas de desgaste presentes no óleo lubrificante é a ferrografia qualitativa. Com base em fundamentos teóricos de obras renomadas no meio cientifico, o objetivo do presente trabalho é projetar e construir um protótipo separador rotativo de partículas de baixo custo, para obter ferrogramas quantitativos de boa qualidade e comparar com os modelos convencionais encontrados no mercado. Através de cinco combinações diferentes de ímãs permanentes foi possível obter ferrogramas, sendo que as combinações 03, 04 e 05 apresentaram melhor qualidade de formação de anéis de partículas ferrosas. A combinação 04 apresentou apenas dois anéis, estes são mais fidedignos em relação aos anéis do aparelho convencional. / Mestre

Orientador: Mauro Hugo Mathias / Banca: João Zangrandi Filho / Banca: Anselmo Monteiro Ilkiu / Resumo: O procedimento de análise de óleos e graxas vem sendo implementado em diferentes setores, tais como indústria de papel e celulose, usina de álcool, empresas marítimas e empresas aéreas comerciais. As técnicas de análise de óleos lubrificantes permitem o aumento da vida útil de equipamentos, a economia de custos de manutenção. Na área militar esta abordagem é usual desde a década de quarenta. No entanto os países desenvolvidos que vêm participando de grande conflitos bélicos têm explorado muito este recurso com o objetivo de minimizar os custos operacionais e aperfeiçoar o cumprimento de missões. Assim, esta tecnologia envolvida é estratégica, cabendo aos países em desenvolvimento a implementação de programas mais eficazes de forma a ter maior grau de independência, aumento da disponibilidade e confiabilidade de equipamentos de modo a economizar recursos. A proposta da pesquisa em desenvolvimento é complementar o programa de manutenção baseado em análise de óleos e graxas no sentido de explorar e aplicar o potencial destas técnicas, visando as atividades de dificuldades em serviço (problemas operacionais) e investigação de incidentes/acidentes aeronaúticos. O estudo de viabilidade deste tipo de programa poderá gerar um programa de Garantia da Qualidade que permita detectar os modos de falha nos sistemas das aeronaves de defesa. Por todas as considerações acima mencionadas, um programa de análise de óleo é imperativo numa empresa aérea comercial ou mesmo numa Força Aérea de Defesa. As possíveis desvantagens na implementação de um programa de análise de óleo são o seu custo ( logística, recursos humanos, instalações, equipamentos, etc) e o tempo que o citado programa leva para ser consolidado. / Abstract: The procedure of oil and grease analysis has been implemented in different sectors, such as industry of paper and cellulose, maritime, alcohol plants, companies and commercial airlines. The techniques of lubricate oil analysis allow the increase of the useful equipment life, the economy in maintenance costs. In the military field this boarding are usual since the decade of forty, however the developed countries that has been participated in World War II have explored much of this resource with the objective to minimize the operational costs and to optimize the missions fulfillment. Thus this involved technology is strategic, fitting to the developing countries the implementation of more efficient programs of form to have greater degree of independence, increase of the availability an equipment reliability in order to save resources. The proposal of the research carried on is to complement the program of maintenance based on oil and grease analysis and greases in order explore and to apply the potential of these techniques, aiming at to the activities of difficulties in service (operational problems) an inquiry of aeronautical incidents/accidents. The feasibility study this type of program will be able to generate a program of Quality Assurance that allows detecting the failure modes in the systems of the defense aircraft. For all considerations mentioned above, a program of oil analysis is imperative in a commercial airline or even in an Air Force of Defense. The possible disadvantages to the implementation of oil analysis program are the their cost (logistic, human resources, physical place, equipments and so on) and the time to consolidate such kind of program. / Mestre

A crescente implementação pela indústria de técnicas de manutenção preditiva exige cada vez mais o aprimoramento dos procedimentos capazes de fornecer informações sobre o estado de um equipamento. Dentre os procedimentos de análise existentes para máquinas rotativas, a análise de vibração é um dos mais utilizados sendo, atualmente inclusive, presente em larga parcela de setores industriais importantes no Brasil (como o setor de celulose e papel, por exemplo). Isto faz, portanto, cada vez mais importante explorar todas as possibilidades desta técnica. Este trabalho inicia uma investigação sobre as relações entre vibração (assinatura mecânica) e lubrificação de máquinas rotativas e assim, como ponto de partida deste estudo, procura avaliar o efeito da variação da viscosidade do lubrificante no sinal de vibração de caixas de engrenagem. O trabalho apresenta um grande banco de dados experimental, discute diversos métodos de processamento de sinais e apresenta uma característica do sinal de vibração que foi capaz de identificar alterações na viscosidade do óleo lubrificante no caso apresentado. / The crescent implementation, by brazilian industry, of predictive maintenance techniques demands, from vibration analyses processes, more capability for supplying information on the state of equipment. Among the existent analysis procedures for rotative machines, the vibration analysis is one of the more used, being nowadays, present in a wide portion of important industrial sections in Brazil (as the cellulose pulp and paper for instance). This makes, therefore, more and more important to explore all of the possibilities of this method. This work begins an investigation about the relation between vibration (mechanical signature) and lubrication of rotative machines. As a starting point of this study, it tries to evaluate the effect of the variation of the viscosity of the lubricant on the vibration signature of a gear box. The work presents a large experimental database, discusses several methods of signal processing and presents a characteristic of the vibration signal capable to identify alterations in the viscosity of the lubricating oil in the tested equipment.

In the industry, a lot of companies are facing the explosion of big data. With this much information stored, companies want to make sense of the data and use it to help them for better decision making, especially for future prediction. A lot of money can be saved and huge revenue can be generated with the power of big data. When building statistical learning models for prediction, companies in the industry are aiming to build models with efficiency and high accuracy. After the learning models have been developed for production, new data will be generated. With the updated data, the models have to be updated as well. Due to this nature, the model performs best today doesn’t mean it will necessarily perform the same tomorrow. Thus, it is very hard to decide which algorithm should be used to build the learning model. This paper introduces a new method that ensembles the information generated by two different classification statistical learning algorithms together as inputs for another learning model to increase the final prediction power. The dataset used in this paper is NASA’s Turbofan Engine Degradation data. There are 49 numeric features (X) and the response Y is binary with 0 indicating the engine is working properly and 1 indicating engine failure. The model’s purpose is to predict whether the engine is going to pass or fail. The dataset is divided in training set and testing set. First, training set is used twice to build support vector machine (SVM) and neural network models. Second, it used the trained SVM and neural network model taking X of the training set as input to predict Y1 and Y2. Then, it takes Y1 and Y2 as inputs to build the Penalized Logistic Regression model, which is the ensemble model here. Finally, use the testing set follow the same steps to get the final prediction result. The model accuracy is calculated using overall classification accuracy. The result shows that the ensemble model has 92% accuracy. The prediction accuracies of SVM, neural network and ensemble models are compared to prove that the ensemble model successfully captured the power of the two individual learning model.

Failures are the eminent aspect of any machine and so is true for vehicle as it is one of the sophisticated machines of today’s time. Early detection of faults and prioritized maintenance is a necessity of vehicle manufactures as it enables them to reduce maintenance cost and increase customer satisfaction. In our research, we have proposed a method for processing Logged Vehicle Data (LVD) that uses Ant-Miner algorithm which is a Ant Colony Optimization (ACO) based Algorithm. It also utilizes processes like Feature engineering, Data preprocessing. We tried to explore the effectiveness of ACO for solving classification problem in the form of fault detection and prediction of failures which would be used for predictive maintenance by manufacturers. From the seasonal and yearly model that we have created, we have used ACO to successfully predict the time of failure which is the month with highest likelihood of failure in vehicle’s components. Here, we also validated the obtained results. LVD suffers from data imbalance problem and we have implemented balancing techniques to eliminate this issue, however more effective balancing techniques along with feature engineering is required to increase accuracy in prediction.