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  • 1. Order onlineBuy this publication >>
    Voronov, Sergii
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Machine Learning Models for Predictive Maintenance2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The amount of goods produced and transported around the world each year increases and heavy-duty trucks are an important link in the logistic chain. To guarantee reliable delivery a high degree of availability is required, i.e., avoid standing by the road unable to continue the transport mission. Unplanned stops by the road do not only cost due to the delay in delivery, but can also lead to damaged cargo. Vehicle downtime can be reduced by replacing components based on statistics of previous failures. However, such an approach is both expensive due to the required frequent visits to a workshop and inefficient as many components from the vehicles in the fleet are still operational. A prognostic method, allowing for vehicle individualized maintenance plans, therefore poses a significant potential in the automotive field. The prognostic method estimates component degradation and remaining useful life based on recorded data and how the vehicle has been operated.

    Lead-acid batteries is a part of the electrical power system in a heavy-duty truck, primarily responsible for powering the starter motor but also powering auxiliary units, e.g., cabin heating and kitchen equipment, which makes the battery a vital component for vehicle availability. Developing physical models of battery degradation is a difficult process which requires access to battery health sensing that is not available in the given study as well a detailed knowledge of battery chemistry.

    An alternative approach, considered in this work, is data-driven methods based on large amounts of logged data describing vehicle operation conditions. In the use-case studied, recorded data is not closely related to battery health which makes battery prognostic challenging. Data is collected during infrequent and non-equidistant visits to a workshop and there are complex dependencies between variables in the data. The main aim of this work has been to develop a framework and methods for estimating lifetime of lead-acid batteries using data-driven methods for condition-based maintenance. The methodology is general and can be applicable for prognostics of other components.

    A main contribution of the thesis is development of machine learning models for predictive maintenance, estimating conditional reliability functions, using Random Survival Forests (RSF) and recurrent neural networks (RNN). An important property of the data is that for a specific vehicle there may be multiple data readouts, but also one single data readout which makes predictive modeling challenging and dealing with this situation is discussed for both RSF and neural networks models. Data quality is important when building data-driven models, and here the data is imbalanced since there are few battery failures relative to the number of vehicles. Further, the data includes many uninformative variables and among those that are informative, there are complex dependencies and correlation. Methods for selecting which data features to use in the model in this situation is also a key contribution. When a point estimation of the conditional reliability functions is available, it is of interest to know how uncertain the estimate is as it allows to take quality of the prediction into account when deciding on maintenance actions. A theory for estimating the variance of the RSF predictor is another contribution in the thesis. To conclude, the results show that Long Short-Term Memory networks, which is a type of RNN, is the most suitable for the vehicle operational data and give the best performance among methods evaluated in the thesis.

    List of papers
    1. Heavy-duty truck battery failure prognostics using random survival forests
    Open this publication in new window or tab >>Heavy-duty truck battery failure prognostics using random survival forests
    2016 (English)In: IFAC PAPERSONLINE, ELSEVIER SCIENCE BV , 2016, Vol. 49, no 11, p. 562-569Conference paper, Published paper (Refereed)
    Abstract [en]

    Predicting lead-acid battery failure is important for heavy-duty trucks to avoid unplanned stops by the road. There are large amount of data from trucks in operation, however, data is not closely related to battery health which makes battery prognostic challenging. A new method for identifying important variables for battery failure prognosis using random survival forests is proposed. Important variables are identified and the results of the proposed method are compared to existing variable selection methods. This approach is applied to generate a prognosis model for lead-acid battery failure in trucks and the results are analyzed. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

    Place, publisher, year, edition, pages
    ELSEVIER SCIENCE BV, 2016
    Keywords
    Battery failure prognosis; Random survival forests; Variable selection
    National Category
    Transport Systems and Logistics
    Identifiers
    urn:nbn:se:liu:diva-132240 (URN)10.1016/j.ifacol.2016.08.082 (DOI)000383464400082 ()
    Conference
    8th IFAC Symposium on Advances in Automotive Control (AAC)
    Available from: 2016-10-24 Created: 2016-10-21 Last updated: 2020-01-24
    2. Variable selection for heavy-duty vehicle battery failure prognostics using random survival forests
    Open this publication in new window or tab >>Variable selection for heavy-duty vehicle battery failure prognostics using random survival forests
    2016 (English)In: PHME 2016 Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain July 5–8, 2016 / [ed] Ioana Eballard and Anibal Bregon, 2016, p. 649-659Conference paper, Published paper (Refereed)
    Abstract [en]

    Prognostics and health management is a useful tool for more flexible maintenance planning and increased system reliability. The application in this study is lead-acid battery failure prognosis for heavy-duty trucks which is important to avoid unplanned stops by the road. There are large amounts of data available, logged from trucks in operation. However, datais not closely related to battery health which makes battery prognostic challenging. When developing a data-driven prognostics model and the number of available variables is large,variable selection is an important task, since including non-informative variables in the model have a negative impact on prognosis performance. Two features of the dataset has been identified, 1) few informative variables, and 2) highly correlated variables in the dataset. The main contribution is a novel method for identifying important variables, taking these two properties into account, using Random Survival Forests to estimate prognostics models. The result of the proposed method is compared to existing variable selection methods,and applied to a real-world automotive dataset. Prognostic models with all and reduced set of variables are generated and differences between the model predictions are discussed, and favorable properties of the proposed approach are highlighted.

    Keywords
    variable selection, random survival forest, battery failure prognostics
    National Category
    Other Electrical Engineering, Electronic Engineering, Information Engineering
    Identifiers
    urn:nbn:se:liu:diva-131788 (URN)978-1-936263-21-9 (ISBN)
    Conference
    Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, July 5-8, 2016
    Available from: 2016-10-06 Created: 2016-10-06 Last updated: 2020-01-24Bibliographically approved
    3. Data-Driven Battery Lifetime Prediction and Confidence Estimation for Heavy-Duty Trucks
    Open this publication in new window or tab >>Data-Driven Battery Lifetime Prediction and Confidence Estimation for Heavy-Duty Trucks
    2018 (English)In: IEEE Transactions on Reliability, ISSN 0018-9529, E-ISSN 1558-1721, Vol. 67, no 2, p. 623-639Article in journal (Refereed) Published
    Abstract [en]

    Maintenance planning is important in the automotive industry as it allows fleet owners or regular customers to avoid unexpected failures of the components. One cause of unplanned stops of heavy-duty trucks is failure in the lead-acid starter battery. High availability of the vehicles can be achieved by changing the battery frequently, but such an approach is expensive both due to the frequent visits to a workshop and also due to the component cost. Here, a data-driven method based on random survival forest (RSF) is proposed for predicting the reliability of the batteries. The dataset available for the study, covering more than 50 000 trucks, has two important properties. First, it does not contain measurements related directly to the battery health; second, there are no time series of measurements for every vehicle. In this paper, the RSF method is used to predict the reliability function for a particular vehicle using data from the fleet of vehicles given that only one set of measurements per vehicle is available. A theory for confidence bands for the RSF method is developed, which is an extension of an existing technique for variance estimation in the random forest method. Adding confidence bands to the RSF method gives an opportunity for an engineer to evaluate the confidence of the model prediction. Some aspects of the confidence bands are considered: their asymptotic behavior and usefulness in model selection. A problem of including time-related variables is addressed in this paper with the argument that why it is a good choice not to add them into the model. Metrics for performance evaluation are suggested, which show that the model can be used to schedule and optimize the cost of the battery replacement. The approach is illustrated extensively using the real-life truck data case study.

    Place, publisher, year, edition, pages
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2018
    Keywords
    Battery lifetime prognostics; data-driven prediction; flexible maintenance; infinitesimal jackknife (IJ) confidence bands; reliability
    National Category
    Other Computer and Information Science
    Identifiers
    urn:nbn:se:liu:diva-149358 (URN)10.1109/TR.2018.2803798 (DOI)000433911000015 ()
    Note

    Funding Agencies|Scania CV; FFI (Vehicle Strategic Research and Innovation)

    Available from: 2018-07-02 Created: 2018-07-02 Last updated: 2020-01-24
    4. Lead-acid battery maintenance using multilayer perceptron models
    Open this publication in new window or tab >>Lead-acid battery maintenance using multilayer perceptron models
    2018 (English)In: 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), 2018, p. 1-8Conference paper, Published paper (Refereed)
    Abstract [en]

    Predictive maintenance of components has the potential to significantly reduce costs for maintenance and to reduce unexpected failures. Failure prognostics for heavy-duty truck lead-acid batteries is considered with a multilayer perceptron (MLP) predictive model. Data used in the study contains information about how approximately 46,000 vehicles have been operated starting from the delivery date until the date when they come to the workshop. The model estimates a reliability and lifetime probability function for a vehicle entering a workshop. First, this work demonstrates how heterogeneous data is handled, then the architectures of the MLP models are discussed. Main contributions are a battery maintenance planning method and predictive performance evaluation based on reliability and lifetime functions, a new model for reliability function when its true shape is unknown, the improved objective function for training MLP models, and handling of imbalanced data and comparison of performance of different neural network architectures. Evaluation shows significant improvements of the model compared to more simple, time-based maintenance plans.

    Keywords
    lead acid batteries, neural nets, preventive maintenance, reliability, lead-acid battery maintenance, MLP models, neural network architectures, reliability function, lifetime functions, predictive performance evaluation, battery maintenance planning method, heterogeneous data, lifetime probability function, multilayer perceptron predictive model, heavy-duty truck lead-acid batteries, failure prognostics, predictive maintenance, simple time-based maintenance plans, improved objective function, Batteries, Histograms, Maintenance engineering, Data models, Neural networks, Conferences
    National Category
    Transport Systems and Logistics
    Identifiers
    urn:nbn:se:liu:diva-163289 (URN)10.1109/ICPHM.2018.8448472 (DOI)
    Conference
    2018 IEEE International Conference on Prognostics and Health Management (ICPHM)
    Available from: 2020-01-24 Created: 2020-01-24 Last updated: 2020-01-24Bibliographically approved
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  • 2.
    Voronov, Sergii
    et al.
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Frisk, Erik
    Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
    Krysander, Mattias
    Linköping University, Department of Electrical Engineering, Computer Engineering. Linköping University, Faculty of Science & Engineering.
    Lead-acid battery maintenance using multilayer perceptron models2018In: 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), 2018, p. 1-8Conference paper (Refereed)
    Abstract [en]

    Predictive maintenance of components has the potential to significantly reduce costs for maintenance and to reduce unexpected failures. Failure prognostics for heavy-duty truck lead-acid batteries is considered with a multilayer perceptron (MLP) predictive model. Data used in the study contains information about how approximately 46,000 vehicles have been operated starting from the delivery date until the date when they come to the workshop. The model estimates a reliability and lifetime probability function for a vehicle entering a workshop. First, this work demonstrates how heterogeneous data is handled, then the architectures of the MLP models are discussed. Main contributions are a battery maintenance planning method and predictive performance evaluation based on reliability and lifetime functions, a new model for reliability function when its true shape is unknown, the improved objective function for training MLP models, and handling of imbalanced data and comparison of performance of different neural network architectures. Evaluation shows significant improvements of the model compared to more simple, time-based maintenance plans.

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