One of Bosch Rexroth’s newest developments is the ABPAC power unit, which is both modular and configurable. The modular design of the ABPAC is enhanced by a selfcontained Condition Monitoring System (CMS), which can also be used to retrofit existing designs. This dissertation shows how Industry 4.0-Technology provides special advantages for the diverse user profiles. Today, Hydraulic Power Units have either scheduled intervals for preventive maintenance or are repaired in case of component failures. Preventive maintenance concepts, until now, did not fully utilize the entire life expectancy of the components, causing higher maintenance costs and prolonged downtimes. Risk of unscheduled downtime forces the customer to stock an array of spare parts leading to higher inventory costs or in the event a spare is not readily available, the customer may encounter long delivery times and extended downtime. Bearing this in mind, we’ve conceived the idea of a self-contained intelligent Condition Monitoring System including a predictive maintenance concept, which is explained in the following.

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.

Le roulement est l'un des composants les plus importants des machines tournantes. Néanmoins, dans des conditions normales d'utilisation, il est soumis à de la fatigue de roulement qui peut conduire à un défaut d'écaillage. Dans ce travail, nous présentons un suivi de la fatigue d'un roulement de butée grâce à un banc d'essais dédié. L'analyse vibratoire est une méthode qui permet de caractériser et de localiser les défauts dans les roulements. Des mesures successives de ces niveaux de vibrations donnent des indications quant à l'évolution de la sévérité des défauts. Le suivi de cette évolution est fait grâce à un indicateur statistique, la valeur RMS (Root Mean Square) qui peut être corrélée avec la taille d'un écaillage de roulement. L'approche suit le fonctionnement du roulement de butée jusqu'à la dégradation avec une acquisition on line des états vibratoires sous forme de signaux temporels. A l'aide du traitement de signal, on obtient les valeurs des amplitudes vibratoires qui caractérisent l'état vibratoire du roulement. Par conséquent, ces valeurs nous permettent de tracer les courbes de fatigue. Au cours de notre travail expérimental, cette opération est appliquée à un lot de butées à billes pour lesquelles nous avons obtenu des courbes semblables où la tendance de l'évolution suit un modèle mathématique à partir de la détection de l'apparition de la première écaille. Le résultat de ce travail contribuera à prédire la durée de vie résiduelle avant la panne. / The bearing is one of the most important components of rotating machines. Nevertheless, in normal conditions of use, it is subject to fatigue which creates a defect called a rolling fatigue spalling. In this work, we present a follow-up of the thrust bearing fatigue on a test bench. Vibration analysis is a method used to characterize the defect. In order to obtain the fatigue curve more adjusted, we have studied the vibration level according to statistical indicators: the Root Mean Square value (RMS value), which is one of the best indicators to show the evolution of the bearing degradation. The approach follows the working of the bearing until the degradation with an on line acquisition of vibration statements in form of time signals. With the signal treatment, we obtain the values of the vibration amplitudes which characterize the vibration state of the bearing. Consequently, these values allow us to plot the fatigue curves. During our experimental work, this operation is applied for a batch of thrust bearings for which we have obtained similar fatigue curves where the evolution trend follows a mathematical model from the detection of the onset of the first spall. The result of this work will contribute to predict the working residual time before failure.

Ever since the first industrial revolution industries have had the goal to increase their production. With new technology such as CPS, AI and IoT industries today are going through the fourth industrial revolution denoted as industry 4.0. The new technology not only revolutionises production, but also maintenance, making predictive maintenance possible. Predictive maintenance seeks to predict when failure would occur, instead of having scheduled maintenance or maintenance after failure already occurred. In this report a convolutional neural network (CNN) will analyse data from an ultrasound machine scanning a weld joint. The data from the ultrasound machine will be transformed by the short time Fourier transform in order to create an image for the CNN. Since the data from the ultrasound is not complete, simulated data will be created and investigated as another option for training the network. The results are promising, however the lack of data makes it hard to show any concrete proof.

In automotive industry there is a growing need for predicting the failure of a component, to achieve the cost saving and customer satisfaction. As failure in a component leads to the work breakdown for the customer. This paper describes an effort in making a prediction failure monitoring model for NOx sensor in trucks. It is a component that used to measure the level of nitrogen oxide emission from the truck. The NOx sensor has chosen because its failure leads to the slowdown of engine efficiency and it is fragile and costly to replace. The data from a good and contaminated NOx sensor which is collated from the test rigs is used the input to the model. This work in this paper shows approach of complementing the Deep Learning models with Machine Learning algorithm to achieve the results. In this work LSTMs are used to detect the gain in NOx sensor and Encoder-Decoder LSTM is used to predict the variables. On top of it Multiple Linear Regression model is used to achieve the end results. The performance of the monitoring model is promising. The approach described in this paper is a general model and not specific to this component, but also can be used for other sensors too as it has a universal kind of approach.

Made available in DSpace on 2014-06-11T19:28:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2006-08-03Bitstream added on 2014-06-13T19:58:09Z : No. of bitstreams: 1 santosjr_jf_me_guara.pdf: 1211785 bytes, checksum: c98805cec7f1bd9e53920e8e6cf06c59 (MD5) / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / 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. / 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.

No description available.

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.