157 research outputs found

    A hybrid prognostics approach for motorized spindle-tool holder remaining useful life prediction

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    The quality and efficiency of high-speed machining are restricted by the matching performance of the motorized spindle-tool holder. In high speed cutting process, the mating surface is subjected to alternating torque, repeated clamping wear and centrifugal force, which results in serious degradation of mating performance. Therefore, for the purpose of the optimum maintenance time, periodic evaluation and prediction of remaining useful life (RUL) should be carried out. Firstly, the mapping model between the current of the motorized spindle and matching performance was extracted, and the degradation characteristics of spindle-tool holder were emphatically analyzed. After the original current is de-noised by an adaptive threshold function, the extent of degradation was identified by the amplitudes of wavelet packet entropy. A hybrid prognostics combining Relevance Vector Machine (RVM) i.e. AI-model with power regression i.e. statistical model was proposed to predict the RUL. Finally, the proposed scheme was verified based on a motorized spindle reliability test platform. The experimental results show that the current signal processing method based on wavelet packet and entropy can reflect the change of the degradation characteristics sensitively. Compared with other two similar models, the hybrid model proposed can accurately predict the RUL. This model is suitable for complex and high reliability equipment when Condition Monitoring (CM) data is scarcer

    Rotating machine prognostics using system-level models

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    The prognostics of rotating machines is crucial for the reliable and safe operation as well as maximizing usage time. Many reliability studies focus on component-level prognostics. However, in many cases, the desired information is the residual life of the system, rather than the lifetimes of its constituent components. This review paper focuses on system-level prognostic techniques that can be applied to rotating machinery. These approaches use multi-dimensional condition monitoring data collected from different parts of the system of interest to predict the remaining useful life at the system level. The working principles, merits and drawbacks as well as field of applications of these techniques are summarized

    Turbofan Engine Behaviour Forecasting using Flight Data and Machine Learning Methods

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    The modern gas turbine engine widely used for aircraft propulsion is a complex integrated system which undergoes deterioration during operation due to the degradation of its gas path components. This dissertation outlines the importance of Engine Condition Monitoring (ECM) for a more efficient maintenance planning. Different ML approaches are compared with the application of predicting engine behaviour aiming at finding the optimal time for engine removal. The selected models were OLS, ARIMA, NeuralProphet, and Cond-LSTM. Long operating and maintenance history of two mature CF6-80C2 turbofan engines were used for the analysis, which allowed for the identification of the impact of different factors on engine performance. These factors were also considered when training the ML models, which resulted in models capable of performing prediction under specified operation and flight conditions. The Machine Learning (ML) models provided forecasting of the Exhaust Gas Temperature (EGT) parameter at take-off phase. Cond-LSTM is shown to be a reliable tool for forecasting engine EGT with a Mean Absolute Error (MAE) of 7.64?, allowing for gradual performance deterioration under specific operation type. In addition, forecasting engine performance parameters has shown to be useful for identifying the optimal time for performing important maintenance action, such as engine gas path cleaning. This thesis has shown that engine removal forecast can be more precise by using sophisticated trend monitoring and advanced ML methods.O moderno motor de turbina a gás amplamente utilizado para propulsão de aeronaves é um sistema integrado complexo que sofre deterioração durante a operação devido à degradação de seus componentes do percurso do gás. Esta dissertação destaca a importância da monitorização da condição do motor para um planejamento de manutenção mais eficiente. Diferentes abordagens de Machine Learning (ML) são comparadas visando a aplicação de previsão do comportamento do motor com o objetivo de encontrar o momento ideal para a remoção do motor. Os modelos selecionados foram OLS, ARIMA, NeuralProphet e Cond-LSTM. O longo histórico de operação e manutenção de dois motores turbofan CF6-80C2 maduros foi usado para a análise, o que permitiu a identificação do impacto de diferentes fatores no desempenho do motor. Esses fatores também foram considerados no treinamento dos modelos de ML, o que resultou em modelos capazes de realizar a previsão em operação e condições de voo especificadas. Os modelos ML forneceram previsão do parâmetro Exhaust Gas Temperature (EGT) na fase de decolagem. O Cond-LSTM demonstrou ser uma ferramenta confiável para previsão do EGT do motor com um erro absoluto médio de 7,64 ?, permitindo a deterioração gradual do desempenho sob um tipo específico de operação. Além disso, a previsão dos parâmetros de desempenho do motor tem se mostrado útil para identificar o momento ideal para realizar ações de manutenção importantes, como a limpeza do percurso do gás do motor. Esta tese mostrou que a previsão de remoção do motor pode ser mais precisa usando um monitoramento sofisticado de tendências e métodos avançados de ML

    Data-driven prognostics for critical electronic assemblies and electromechanical components

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    The industrial digitalisation enables the adoption of robust, data-driven maintenance strategies that increase safety and reliability of critical assets such as electronics. And yet, an implementation of data-driven methods which primarily address the industrialisation of diagnostic and prognostic strategies is opposed by various, application specific challenges. This thesis collates such restricting factors encountered within the oil and gas industry, in particular for the critical electrical systems and components in upstream deep drilling tools. A fleet-level, tuned machine learning approach is presented that classifies the operational state (no-failure/ failure) of downhole tool printed circuit board assemblies. It supports maintenance decision making under varying levels of failure costs and fleet reliability scenarios. Applied within a maintenance scheme it has the potential to minimise non-productive time while increasing operational reliability. Likewise, a tailored and efficient deep learning data pipeline is proposed for a component-level forecast of the end of life of electromagnetic relays. It is evaluated using high resolution life-cycle data which has been collected as a part of this thesis. In combination with a failure analysis, the proposed method improves the prognostics capabilities compared to traditional methods which have been proposed so far in order to assess the operational health of electromagnetic relays. Two case studies underpin the need for tailored prognostic methods in order to provide viable solutions that can de-risk deep drilling operations. In consequence, the proposed approaches alleviate the pressure on current maintenance strategies which can no longer meet the stringent reliability requirements of upstream assets

    Condition-based maintenance in hydroelectric plants: A systematic literature review

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    Industrial maintenance has become an essential strategic factor for profit and productivity in industrial systems. In the modern industrial context, condition-based maintenance guides the interventions and repairs according to the machine’s health status, calculated from monitoring variables and using statistical and computational techniques. Although several literature reviews address condition-based maintenance, no study discusses the application of these techniques in the hydroelectric sector, a fundamental source of renewable energy. We conducted a systematic literature review of articles published in the area of condition-based maintenance in the last 10 years. This was followed by quantitative and thematic analyses of the most relevant categories that compose the phases of condition-based maintenance. We identified a research trend in the application of machine learning techniques, both in the diagnosis and the prognosis of the generating unit’s assets, being vibration the most frequently discussed monitoring variable. Finally, there is a vast field to be explored regarding the application of statistical models to estimate the useful life, and hybrid models based on physical models and specialists’ knowledge, of turbine-generators

    30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

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    Proceedings of COMADEM 201

    A Predictive maintenance model for heterogeneous industrial refrigeration systems

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    The automatic assessment of the degradation state of industrial refrigeration systems is becoming increasingly important and constitutes a key-role within predictive maintenance approaches. Lately, data-driven methods especially became the focus of research in this respect. As they only rely on historical data in the development phase, they offer great advantages in terms of flexibility and generalisability by circumventing the need for specific domain knowledge. While most scientific contributions employ methods emerging from the field of machine learning (ML), only very few consider their applicability amongst different heterogeneous systems. In fact, the majority of existing contributions in this field solely apply supervised ML models, which assume the availability of labelled fault data for each system respectively. However, this places restrictions on the overall applicability, as data labelling is mostly conducted by humans and therefore constitutes a non-negligible cost and time factor. Moreover, such methods assume that all considered fault types occurred in the past, a condition that may not always be guaranteed to be satisfied. Therefore, this dissertation proposes a predictive maintenance model for industrial refrigeration systems by especially addressing its transferability onto different but related heterogeneous systems. In particular, it aims at solving a sub-problem known as condition-based maintenance (CBM) to automatically assess the system’s state of degradation. To this end, the model does not only estimate how far a possible malfunction has progressed, but also determines the fault type being present. As will be described in greater detail throughout this dissertation, the proposed model also utilises techniques from the field of ML but rather bypasses the strict assumptions accompanying supervised ML. Accordingly, it assumes the data of the target system to be primarily unlabelled while a few labelled samples are expected to be retrievable from the fault-free operational state, which can be obtained at low cost. Yet, to enable the model’s intended functionality, it additionally employs data from only one fully labelled source dataset and, thus, allows the benefits of data-driven approaches towards predictive maintenance to be further exploited. After the introduction, the dissertation at hand introduces the related concepts as well as the terms and definitions and delimits this work from other fields of research. Furthermore, the scope of application is further introduced and the latest scientific work is presented. This is then followed by the explanation of the open research gap, from which the research questions are derived. The third chapter deals with the main principles of the model, including the mathematical notations and the individual concepts. It furthermore delivers an overview about the variety of problems arising in this context and presents the associated solutions from a theoretical point of view. Subsequently, the data acquisition phase is described, addressing both the data collection procedure and the outcome of the test cases. In addition, the considered fault characteristics are presented and compared with the ones obtained from the related publicly available dataset. In essence, both datasets form the basis for the model validation, as discussed in the following chapter. This chapter then further comprises the results obtained from the model, which are compared with the ones retrieved from several baseline models derived from the literature. This work then closes with a summary and the conclusions drawn from the model results. Lastly, an outlook of the presented dissertation is provide
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