1,061 research outputs found

    A critical review of online battery remaining useful lifetime prediction methods.

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    Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods

    Instantaneous failure mode remaining useful life estimation using non-uniformly sampled measurements from a reciprocating compressor valve failure

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    One of the major targets in industry is minimisation of downtime and cost, and maximisation of availability and safety, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) , which is founded on the principles of diagnostics, and prognostics, is a step towards this direction as it offers a proactive means for scheduling maintenance. Reciprocating compressors are vital components in oil and gas industry, though their maintenance cost is known to be relatively high. Compressor valves are the weakest part, being the most frequent failing component, accounting for almost half maintenance cost. To date, there has been limited information on estimating Remaining Useful Life (RUL) of reciprocating compressor in the open literature. This paper compares the prognostic performance of several methods (multiple linear regression, polynomial regression, Self-Organising Map (SOM), K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and precision, using actual valve failure data captured from an operating industrial compressor. The SOM technique is employed for the first time as a standalone tool for RUL estimation. Furthermore, two variations on estimating RUL based on SOM and KNNR respectively are proposed. Finally, an ensemble method by combining the output of all aforementioned algorithms is proposed and tested. Principal components analysis and statistical process control were implemented to create T^2 and Q metrics, which were proposed to be used as health indicators reflecting degradation processes and were employed for direct RUL estimation for the first time. It was shown that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques

    Overview of Machine Learning Methods for Lithium-Ion Battery Remaining Useful Lifetime Prediction

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    Lithium-ion batteries play an indispensable role, from portable electronic devices to electric vehicles and home storage systems. Even though they are characterized by superior performance than most other storage technologies, their lifetime is not unlimited and has to be predicted to ensure the economic viability of the battery application. Furthermore, to ensure the optimal battery system operation, the remaining useful lifetime (RUL) prediction has become an essential feature of modern battery management systems (BMSs). Thus, the prediction of RUL of Lithium-ion batteries has become a hot topic for both industry and academia. The purpose of this work is to review, classify, and compare different machine learning (ML)-based methods for the prediction of the RUL of Lithium-ion batteries. First, this article summarizes and classifies various Lithium-ion battery RUL estimation methods that have been proposed in recent years. Secondly, an innovative method was selected for evaluation and compared in terms of accuracy and complexity. DNN is more suitable for RUL prediction due to its strong independent learning ability and generalization ability. In addition, the challenges and prospects of BMS and RUL prediction research are also put forward. Finally, the development of various methods is summarized

    Reciprocating compressor prognostics of an instantaneous failure mode utilising temperature only measurements

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    Reciprocating compressors are critical components in the oil and gas sector, though their maintenance cost is known to be relatively high. Compressor valves are the weakest component, being the most frequent failure mode, accounting for almost half the maintenance cost. One of the major targets in industry is minimisation of downtime and cost, while maximising availability and safety of a machine, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) which is founded on the diagnostics and prognostics principles, is a step towards this direction as it offers a proactive means for scheduling maintenance. Despite the fact that diagnostics is an established area for reciprocating compressors, to date there is limited information in the open literature regarding prognostics, especially given the nature of failures can be instantaneous. This work presents an analysis of prognostic performance of several methods (multiple linear regression, polynomial regression, K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and variability, using actual temperature only valve failure data, an instantaneous failure mode, from an operating industrial compressor. Furthermore, a variation for Remaining Useful Life (RUL) estimation based on KNNR, along with an ensemble technique merging the results of all aforementioned methods are proposed. Prior to analysis, principal components analysis and statistical process control were employed to create !! and ! metrics, which were proposed to be used as health indicators reflecting degradation process of the valve failure mode and are proposed to be used for direct RUL estimation for the first time. Results demonstrated that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques

    Energy: A special bibliography with indexes, April 1974

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    This literature survey of special energy and energy related documents lists 1708 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1, 1968, and December 31, 1973. Citations from International Aerospace Abstracts (IAA) and Scientific and Technical Aerospace Reports (STAR) are grouped according to the following subject categories: energy systems; solar energy; primary energy sources; secondary energy sources; energy conversion; energy transport, transmission, and distribution; and energy storage. The index section includes the subject, personal author, corporate source, contract, report, and accession indexes

    The 1977 Goddard Space Flight Center Battery Workshop

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    The papers presented were derived from transcripts taken at the Tenth Annual Battery Workshop held at the Goddard Space Flight Center, November 15-17, 1977. The Workshop was attended by manufacturers, users, and government representatives interested in the latest results of testing, analysis, and development of the sealed nickel cadmium cell system. The purpose of the Workshop was to share flight and test experience, stimulate discussion on problem areas, and to review the latest technology improvements

    Energy: A continuing bibliography with indexes, issue 32

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    This bibliography lists 1316 reports, articles, and other documents introduced into the NASA scientific and technical information system from October 1, 1981 through December 31, 1981

    A Sustainable Revolution

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    The parties of the United Nations Framework Convention on Climate Change (UNFCCC) attained the Paris Agreement to tackle climate change and to strengthen the actions required for a sustainable transition towards an environmentally friendly future. This transition will involve holistic approaches and multifaceted societal shifts, requiring answers and collaboration between private, public, and academic sectors. This book gathers together contributions which study the transition towards a more sustainable future, involving and identifying the development and implications of more sustainable alternatives, in collaboration with all relevant stakeholders (e.g. communities, firms, policy makers, researchers, etc.), to achieve this transition. The approaches proposed are all concerned with a common perspective: imaging our globe with a greener picture, built upon a transversal sustainable revolution to clean up the Earth

    NASA SBIR abstracts of 1992, phase 1 projects

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    The objectives of 346 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1992 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 346, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1992 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included
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