137 research outputs found

    Predictive Maintenance for Aircraft Engine Using Machine Learning: Trends and Challenges

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    This article aims to prove that Machine Learning (ML) methods are effective for Predictive Maintenance (PdM) and to obtain other developing methods that suitable applied on PdM, especially for aircraft engine, and potential method that can apply on future research, and also compared between articles in International and Indonesia institution. Maintenance factors are important to prognostic the states of a machine. PdM is one of the factor strategies based on realtime data to diagnosis a failure of the machine through forecasting remaining useful life (RUL), especially on aircraft machine where the safety is priority due to enormous cost and human life. ML is the technique that accurately prediction through the data. Applied ML on PdM is the huge contribution for saving cost and human life guarantee of safety. This work provides the literature survey for recent research which trends and challenges on PdM of aircraft engine using ML that compared the research from international and Indonesia from 2016 to 2021. Result of this work shows that ML method, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are the best method to calculate PdM with more than 99% on rate accuracy, and low level of Indonesia institution research which focused on PdM on aircraft engine using M

    Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

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    In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

    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

    Least squares smoothed k-nearest neighbors online prediction of the remaining useful life of a NASA turbofan

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    An accurate prediction of the Remaining Useful Life (RUL) of aircraft engines plays a fundamental role in the aerospace field since it is both mission and safety critical. In fact, a reliable estimate of the RUL can effectively reduce the maintenance costs while fostering safety. This paper proposes a novel data-driven method to increase accuracy of the RUL prediction for real-time prognostic systems, considering multiple degradation mechanisms and making the model easy to implement. The proposed method exploits a novel modified k-Nearest Neighbors Interpolation (kNNI) with an a posteriori Least Square Smoothing (LSS) automatically optimized to obtain the minimum prediction error. The LSS novel formulation was also generalized and proved to be equivalent to a Cumulative and Moving Average (CMA) mixture filter, which can be easily implemented online. The method was developed and validated based on a new NASA dataset generated by the dynamic model Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) with run-to-failure data related to a small fleet of aircraft engines under realistic flight conditions. Finally, a refer- ence kNN-based method already known in the literature was compared to the novel proposed one to demonstrate the goodness of the results and the performance improvements

    Recent Advances in Anomaly Detection Methods Applied to Aviation

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    International audienceAnomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance

    Advanced Data Analytics Methodologies for Anomaly Detection in Multivariate Time Series Vehicle Operating Data

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    Early detection of faults in the vehicle operating systems is a research domain of high significance to sustain full control of the systems since anomalous behaviors usually result in performance loss for a long time before detecting them as critical failures. In other words, operating systems exhibit degradation when failure begins to occur. Indeed, multiple presences of the failures in the system performance are not only anomalous behavior signals but also show that taking maintenance actions to keep the system performance is vital. Maintaining the systems in the nominal performance for the lifetime with the lowest maintenance cost is extremely challenging and it is important to be aware of imminent failure before it arises and implement the best countermeasures to avoid extra losses. In this context, the timely anomaly detection of the performance of the operating system is worthy of investigation. Early detection of imminent anomalous behaviors of the operating system is difficult without appropriate modeling, prediction, and analysis of the time series records of the system. Data based technologies have prepared a great foundation to develop advanced methods for modeling and prediction of time series data streams. In this research, we propose novel methodologies to predict the patterns of multivariate time series operational data of the vehicle and recognize the second-wise unhealthy states. These approaches help with the early detection of abnormalities in the behavior of the vehicle based on multiple data channels whose second-wise records for different functional working groups in the operating systems of the vehicle. Furthermore, a real case study data set is used to validate the accuracy of the proposed prediction and anomaly detection methodologies

    A Review: Prognostics and Health Management in Automotive and Aerospace

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    Prognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems use real-time and historical state information of subsystems and components of the operating systems to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Every year, a substantial number of papers in this area including theory and practical applications, appear in academic journals, conference proceedings and technical reports. This paper aims to summarize and review researches, developments and recent contributions in PHM for automotive- and aerospace industries. It can also be considered as the starting point for researchers and practitioners in general to assist them through PHM implementation and help them to accomplish their work more easily.Algorithms and the Foundations of Software technolog

    Contribution to intelligent monitoring and failure prognostics of industrial systems.

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    This thesis was conducted within the framework of SMART project funded by a European program, Interreg POCTEFA. The project aims to support small and medium-sized companies to increase their competitiveness in the context of Industry 4.0 by developing intelligent monitoring tools for autonomous system health management. To do so, in this work, we propose efficient data-driven algorithms for prognostics and health management of industrial systems. The first contribution consists of the construction of a new robust health indicator that allows clearly separating different fault states of a wide range of systems’ critical components. This health indicator is also efficient when considering multiples monitoring parameters under various operating conditions. Next, the second contribution addresses the challenges posed by online diagnostics of unknown fault types in dynamic systems, particularly the detection, localization, and identification of the robot axes drifts origin when these drifts have not been learned before. For this purpose, a new online diagnostics methodology based on information fusion from direct and indirect monitoring techniques is proposed. It uses the direct monitoring way to instantaneously update the indirect monitoring model and diagnose online the origin of new faults. Finally, the last contribution deals with the prognostics issue of systems failure in a controlled industrial process that can lead to negative impacts in long-term predictions. To remedy this problem, we developed a new adaptive prognostics approach based on the combination of multiple machine learning predictions in different time horizons. The proposed approach allows capturing the degradation trend in long-term while considering the state changes in short-term caused by the controller activities, which allows improving the accuracy of prognostics results. The performances of the approaches proposed in this thesis were investigated on different real case studies representing the demonstrators of the thesis partners
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