No description available.

Made available in DSpace on 2014-06-11T19:28:35Z (GMT). No. of bitstreams: 0 Previous issue date: 2005-05Bitstream added on 2014-06-13T20:58:36Z : No. of bitstreams: 1 oliveira_rjg_me_guara.pdf: 1311511 bytes, checksum: 7c57fbdb099a4b3d6123bb38b37813e3 (MD5) / Universidade Estadual Paulista (UNESP) / Na indústria moderna o monitoramento da condição de operação de máquinas rotativas é essencial para se determinar o surgimento de falhas em mancais de rolamentos. Este trabalho apresenta uma técnica de análise adotada para a identificação de falhas em mancais de rolamento em seus estágios iniciais, utilizando procedimentos de análise de sinais no domínio do tempo e da freqüência, com especial atenção para a técnica do HFRT (High Frequency Resonance Technique), também conhecida como Técnica do Envelope. Este método de análise de sinais foi escolhido em razão de ser uma ferramenta apropriada para identificar falhas em mancais de rolamentos na sua fase inicial. A teoria das técnicas foi discutida e os passos para a implementação computacional foram apresentados. As rotinas foram implementadas através da linguagem de programação MATLAB e um sinal simulado representativo de um sinal coletado de um mancal de rolamento com defeito pontual na pista externa foi desenvolvido para verificar a eficácia dos métodos implementados. Os experimentos foram desenvolvidos utilizando-se uma bancada de testes aplicada para testar mancais de rolamento com defeitos pontuais produzidos em laboratório. A aquisição dos dados foi desenvolvida com instrumentação comercial. Os resultados obtidos mostraram ser efetivos para identificar falhas em rolamentos para os dados simulados e dados experimentais. / In the modern industries, the condition monitoring of the rotational machinery operation is important to evidence the beginning of the fails in bearings. This work presents a technique of analysis applied to identify fails in bearing during the initial phases, using techniques of signal analysis in time and frequency domain with special attention for the High Frequency Resonance Technique, also called envelope technique. This method for signal analysis was chosen because is an appropriated tool to identify fails in bearings during initial phases. The theory for the techniques was discussed and the steps for the computational implementation were showed. The routines were implemented through MATLAB programming language and it was prepared a representative signal of a bearing with a single point defect in the outer race in order to verify the capability of the method implemented in the routine. The experiments were performed using a experimental test rig applied to test bearings with single point defects performed in laboratory. The data acquisition were performed with commercial instrumentation. The results obtained shown to be effective to identify fails in bearings for both numerically simulated data and experimental data.

<p>Manufacturing today is increasingly competitive and every organization around the world is looking to decrease costs. Maintenance costs generated an average of 28 percent of total manufacturing cost at the Fiat Chrysler Indiana Transmission Plant One in 2018, states Rex White, Head Maintenance Planner at Fiat Chrysler (2018). Maintenance is a supportive expense that does not generate a profit, which makes maintenance an attractive expense to decrease. The cost for components and skilled labor are expensive; however, the downtime is exponentially a larger threat to production cost. One most feared scenarios within a manufacturing facility is that one machine takes down several as it backs up the entire production process.</p><p>The three major types of maintenance are reactive, preventive, and predictive. The research project focused on applying the principles of predictive maintenance to the Fiat Chrysler facilities in Indiana. The report explains the techniques and principles of applying the technology currently available to reduce downtime and maintenance cost. The predictive maintenance procedures and saving are compared with reactive and preventive methods to determine a value of return. The report will examine the benefits of using the Internet of Things technology to create autonomous self-diagnosing smart machines. The predictive maintenance plan in this research illustration will introduce health check equipment used to implement longer lasting machine components. In conclusion, the project developed out an entire predictive maintenance plan to reduce downtime and maintenance costs.<br></p><p></p><br>

Orientador: Mauro Hugo Mathias / Banca: José Elias Tomazini / Banca: Francisco Carlos Parquet Bizarria / Resumo: Na indústria moderna o monitoramento da condição de operação de máquinas rotativas é essencial para se determinar o surgimento de falhas em mancais de rolamentos. Este trabalho apresenta uma técnica de análise adotada para a identificação de falhas em mancais de rolamento em seus estágios iniciais, utilizando procedimentos de análise de sinais no domínio do tempo e da freqüência, com especial atenção para a técnica do HFRT (High Frequency Resonance Technique), também conhecida como Técnica do Envelope. Este método de análise de sinais foi escolhido em razão de ser uma ferramenta apropriada para identificar falhas em mancais de rolamentos na sua fase inicial. A teoria das técnicas foi discutida e os passos para a implementação computacional foram apresentados. As rotinas foram implementadas através da linguagem de programação MATLAB e um sinal simulado representativo de um sinal coletado de um mancal de rolamento com defeito pontual na pista externa foi desenvolvido para verificar a eficácia dos métodos implementados. Os experimentos foram desenvolvidos utilizando-se uma bancada de testes aplicada para testar mancais de rolamento com defeitos pontuais produzidos em laboratório. A aquisição dos dados foi desenvolvida com instrumentação comercial. Os resultados obtidos mostraram ser efetivos para identificar falhas em rolamentos para os dados simulados e dados experimentais. / Abstract: In the modern industries, the condition monitoring of the rotational machinery operation is important to evidence the beginning of the fails in bearings. This work presents a technique of analysis applied to identify fails in bearing during the initial phases, using techniques of signal analysis in time and frequency domain with special attention for the High Frequency Resonance Technique, also called envelope technique. This method for signal analysis was chosen because is an appropriated tool to identify fails in bearings during initial phases. The theory for the techniques was discussed and the steps for the computational implementation were showed. The routines were implemented through MATLAB programming language and it was prepared a representative signal of a bearing with a single point defect in the outer race in order to verify the capability of the method implemented in the routine. The experiments were performed using a experimental test rig applied to test bearings with single point defects performed in laboratory. The data acquisition were performed with commercial instrumentation. The results obtained shown to be effective to identify fails in bearings for both numerically simulated data and experimental data. / Mestre

Effective machine maintenance policy is a critical element of a smooth running manufacturing system. This paper evaluates a multiple-recipe predictive maintenance problem modeled using a M/G/1 queueing system. A numerical study is performed on an optimal predictive maintenance policy. A simulated job-based maintenance policy is used as a baseline for the optimal policy. We investigate the effects of varying degradation rates, holding costs, preventive maintenance times, and preventive maintenance costs. We also examine a two-recipe problem. / text

The purpose of this thesis was to compare two integration’s of sensors, into a system called Enlight, but could in theoryhave been integrated to most systems. As a pre-study, the specifications and availability of five sensors were researched.From the pre-study, Smart Edge 4.0 and Phantom EPH-V11/10 from Erbessd, were chosen and then integrated. Usability andperformance of the integrations were then compared usingcognitive dimensions and stopwatch. Phantom from Erbessdwas deemed to be more usable, and the integration of SmartEdge 4.0, had better performance.

Der Einsatz modularer Anlagen wird in der Prozessindustrie immer beliebter. Da Modulhersteller ihre Applikationen weitestgehend kapseln, um ihr Know-How zu schützen, müssen Strukturen und Applikationen entwickelt werden, die das Monitoring und die Diagnose auch im Rahmen der vorausschauenden Instandhaltung dieser Module ermöglichen. Die Implementierung solcher Funktionen in das übergeordnete Leitsystem der Gesamtanlage ist bei modularen Anlagen bislang ähnlich umständlich wie die Einbindung von Fremdkomponenten in ein Leitsystem. Um ein vollständiges modulares Anlagenkonzept erfolgreich zu etablieren, ist eine einfache Implementierung dieser Funktionalitäten somit unumgänglich. Im Rahmen dieses Beitrags wird daher untersucht, wie Strukturen und Applikationen zum Monitoring und Diagnose einfach in übergeordnete Systeme implementiert werden können. Hier wird auch betrachtet, welche dieser Informationen für Anlagenfahrer relevant, welche Funktionalitäten auf externe Systeme (z. B. eine Cloud) ausgelagert werden sollten und wie diese optimal dargestellt werden. Weiterhin wird ein Konzept zur Modularisierung von Plant Asset Management Funktionen vorgestellt, anhand dessen eine Strukturierung des NAMUR Open Architecture (NOA)-Kanals vorgenommen wird.

Integrated Vehicle Health Management (IVHM) systems use models and algorithmic techniques to process Condition-based Data (CBD) to offer prognostic information and actionable imperatives in support of Condition-based Maintenance (CBM) for the system. IVHM technology was first introduced by NASA to gather data, diagnose, detect, and predict faults, and support operational and post-maintenance activities in space vehicles. Eventually, it expanded to other vehicle types such as aircraft, ships, and land vehicles [1]. In recent years, the United States Army has been implementing a policy of CBM to transition from preventive to predictive maintenance [2]. One of the many challenges faced by the Army is the lack of accurate methods to assess ground vehicle reliability using modeling and/or simulation. This study aims at developing a Markov Chain-based algorithm that can detect anomalies and that is capable of accurately predicting the operational states of military ground vehicles. Several different Markov Chain Models (MCMs) have been developed and tested in their ability to predict the next state of a vehicle, given its current state (diagnostics and prognostics), and to examine how well a given model can detect unknown measurements (anomaly detection). A target of 90% Correct Classification (PCC) was established for all the vehicle performance data. The results suggest that it is possible to predict at a high level of accuracy the likely operational states of the military vehicles using MCMs. The anomaly detection test results revealed that MCMs can clearly distinguish a change in the performance data, that does not match the expected performance.

Predictive Maintenance is an important solution to the rising maintenance costs in the industries. With the advent of intelligent computer and availability of data, predictive maintenance is seen as a solution to predict and prevent the occurrence of the faults in the different types of machines. This thesis provides a detailed methodology to predict the occurrence of critical Diagnostic Trouble codes that are observed in a vehicle in order to take necessary maintenance actions before occurrence of the fault in automobiles using Convolutional Neural Network architecture.

The prime focus of this thesis was to develop a robust Prognostic and Diagnostic Health Management module (PDHM), capable of detecting faults, classifying faults, fault progression tracking and estimating time to failure. Priority was to obtain as much accuracy as possible with the bare minimum amount of sensors as possible. Algorithms like k-Nearest Neighbors (k-NN), Linear and Non- Linear regression and development of rule engine to identify safe operating limits were deployed. The entire solution was developed using R (v 3.5.0). The accuracy of around 98% was obtained in diagnostics. For Prognostics, our ability to predict time to failure more accurately increases with time. Some balance must be there between learning horizon and predicting horizon in order to get good predictions with reasonable time left to hit catastrophic failure. In conclusion, the PDHM module works just as desired and makes Predictive maintenance, smart replacement and crisis prediction possible ensuring the safety and security of people on board and assets.