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  • 1.
    Dahl, Oskar
    et al.
    Halmstad University, School of Information Technology.
    Johansson, Fredrik
    Halmstad University, School of Information Technology.
    Khoshkangini, Reza
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pashami, Sepideh
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pihl, Claes
    Volvo Group, QandCS, Gothenburg, Sweden.
    Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters: Using Clustering and Rule-Based Machine Learning2020In: Proceedings of the 2020 3rd International Conference on Information Management and Management Science, IMMS 2020, New York: Association for Computing Machinery (ACM), 2020, p. 13-22Conference paper (Refereed)
    Abstract [en]

    Trucks are designed, configured and marketed for various working environments. There lies a concern whether trucks are used as intended by the manufacturer, as usage may impact the longevity, efficiency and productivity of the trucks. In this paper we propose a framework that aims to extract costumers' vehicle behaviours from Logged Vehicle Data (LVD) in order to evaluate whether they align with vehicle configurations, so-called Global Transport Application (GTA) parameters. Gaussian mixture model (GMM)s are employed to cluster and classify various vehicle behaviors from the LVD. Rule-based machine learning (RBML) was applied on the clusters to examine whether vehicle behaviors follow the GTA configuration. Particularly, we propose an approach based on studying associations that is able to extract insights on whether the trucks are used as intended. Experimental results shown that while for the vast majority of the trucks' behaviors seemingly follows their GTA configuration, there are also interesting outliers that warrant further analysis. © 2020 ACM.

  • 2.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gupta, Ankit
    Shahi, Durlabh
    Tajgardan, Mohsen
    Qom University of Technology, Qom, Iran.
    Orand, Abbas
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Forecasting Components Failures Using Ant Colony Optimization for Predictive Maintenance2021In: Proceedings of the 31st European Safety and Reliability Conference / [ed] Bruno Castanier; Marko Cepin; David Bigaud; Christophe Berenguer, Singapore: European Safety and Reliability Association, 2021, p. 2947-2954Conference paper (Refereed)
    Abstract [en]

    Failures are the eminent aspect of any sophisticated machine such as vehicles. Early detection of faults and prioritized maintenance is a necessity of vehicle manufacturers as it enables them to reduce maintenance costs, safety risks and increase customer satisfaction. In this study, we propose to use a type of Ant Colony Optimization (ACO) algorithm to diagnose vehicles faults. We explore the effectiveness of ACO for solving fault detection in the form of a classification problem, which would be used for predictive maintenance by the manufacturers. We show experimental evaluations on the real data captured from heavy-duty trucks illustrating how optimization algorithms can be used as a classification approach to forecast component failures in the context of predictive maintenance © ESREL 2021

  • 3.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology.
    Pashami, Sepideh
    Halmstad University, School of Information Technology.
    Bayesian network for failure prediction in different seasons2020In: 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020 / [ed] Baraldi P., Di Maio F., Zio E., Research Publishing Services , 2020, p. 1710-1710Conference paper (Refereed)
    Abstract [en]

    [No abstract available]

  • 4.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pashami, Sepideh
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Baysian Network for Failure Prediction in Different Seasons2020In: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference / [ed] Piero Baraldi, Francesco Di Maio and Enrico Zio, 2020, p. 1710-1710Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    In recent years, there have been many attentions in developing technologies with the aim of monitoring and predicting emerging issues such as break downs, component failures, and quality degradations e.g., R, Prytz et al. (2015), as a means to provide predictive maintenance solution in modern vehicle industries. These existing technologies exploit several fault predictions and diagnostic pipelines ranging from statistics methods to machine learning algorithms e.g., M, You et al. (2010), Y, Lei et al. (2016). However, these solutions have not particularly concentrated on the ability to predict the component failures and the cause of the failures taking into consideration vehicle usage patterns and history of failures over time in different seasons.

    This is not a trivial task since modern vehicles with their huge functionalities and dependency among their components bring out a challenge to the manufacturer to plan their maintenance strategy in this complex domain. This is truly a complex challenge since failures can be sourced and affected by multiple features, which are highly related to each other and change over time in different contexts (e.g., location, time, season).  

    Under such conditions, an advanced early prediction capability is desired, because manufacturers can exceedingly serve from early prediction of potential vehicle component failures, and more specifically the chain of the features and their dependencies which may lead to a failure over time in different seasons.  This is considered important due to the fact that different seasons may have a potential effect on certain component failures, so predicting these dependencies and the actual failure enables a higher level of maintenance for optimally planning and managing total cost and more importantly safety. 

    In this study, we build a probabilistic prediction model in a time series, on top of vehicle usage pattern, which is represented by the Live Vehicle Data (LVD). LVD logged and captured using multiple sensors located in Volvo vehicles that includes usage and specification of the vehicles aggregated in a cumulative fashion. We exploit and apply a type of supervised machine learning algorithm called Bayesian Network N, Friedman. (1997), on the engineered LVD (we applied a type of data engineering process to extract hidden patterns from LVD), which is logged through different seasons. These result a very complex network of dependency in each time stamp that indicates how a failure sourced by different features and their quantitative influences. In addition, integrating all these networks reveal how the usage can influence failure over time. Moreover, the quantitative influences allow us to extract the main chain of effect on a failure. This is strongly beneficial for the manufacturers and maintenance strategy to find out the main reason of failures, which can be extracted by vehicle usage pattern during their operation. © ESREL2020-PSAM15 Organizers

  • 5.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pashami, Sepideh
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Warranty Claim Rate Prediction using Logged Vehicle Data2019In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 11804, p. 663-674Article in journal (Refereed)
    Abstract [en]

    Early detection of anomalies, trends and emerging patterns can be exploited to reduce the number and severity of quality problems in vehicles. This is crucially important since having a good understanding of the quality of the product leads to better designs in the future, and better maintenance to solve the current issues. To this end, the integration of large amounts of data that are logged during the vehicle operation can be used to build the model of usage patterns for early prediction. In this study, we have developed a machine learning system for warranty claims by integrating available information sources: Logged Vehicle Data (LVD) and Warranty Claims (WCs). The experimental results obtained from a large data set of heavy duty trucks are used to demonstrate the effectiveness of the proposed system to predict the warranty claims. © Springer Nature Switzerland AG 2019.

  • 6.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Pini, M. S.
    Department of Information Engineering, University of Padova, Padua, Italy.
    Rossi, F.
    IBM T. J. Watson Research Center, Yorktown Heights, NY, United States.
    Constructing CP-Nets from Users Past Selection2019In: Lecture Notes in Computer Science: Volume 11919 LNAI, Springer, 2019, p. 130-142Conference paper (Refereed)
    Abstract [en]

    Although recommender systems have been significantly developed for providing customized services to users in various domains, they still have some limitations regarding the extraction of users’ conditional preferences from their past selections when they are in a dynamic context. We propose a framework to automatically extract and learn users’ conditional and qualitative preferences in a gamified system taking into consideration the players’ past behaviour, without asking any information from the players. To do that, we construct CP-nets modeling users preferences via a procedure that employs multiple Information Criterion score functions within an heuristic algorithm to learn a Bayesian network. The approach has been validated experimentally in the challenge recommendation domain in an urban mobility gamified system. © 2019, Springer Nature Switzerland AG.

  • 7.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Sheikholharam Mashhadi, Peyman
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Berck, Peter
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Gholami Shahbandi, Saeed
    Volvo Group, Connected Solutions, Gothenburg, Sweden.
    Pashami, Sepideh
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Nowaczyk, Sławomir
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Niklasson, Tobias
    Volvo Group, Q&CS, Gothenburg, Sweden.
    Early Prediction of Quality Issues in Automotive Modern Industry2020In: Information, E-ISSN 2078-2489, Vol. 11, no 7, article id 354Article in journal (Refereed)
    Abstract [en]

    Many industries today are struggling with early identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with customers' requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the Original Equipment Manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using Machine Learning (ML) to forecast the failures of a given component across the large population of units.

    In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage.

    We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently.  © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

  • 8.
    Khoshkangini, Reza
    et al.
    Department of Computer Science and Media Technology, Malmö University, Malmö, Sweden.
    Sheikholharam Mashhadi, Peyman
    Halmstad University, School of Information Technology.
    Tegnered, Daniel
    Volvo Group Connected Solutions, Gothenburg, Sweden.
    Lundström, Jens
    Halmstad University, School of Information Technology.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology.
    Predicting Vehicle Behavior Using Multi-task Ensemble Learning2023In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 212, article id 118716Article in journal (Refereed)
    Abstract [en]

    Vehicle utilization analysis is an essential tool for manufacturers to understand customer needs, improve equipment uptime, and to collect information for future vehicle and service development. Typically today, this behavioral modeling is done on high-resolution time-resolved data with features such as GPS position and fuel consumption. However, high-resolution data is costly to transfer and sensitive from a privacy perspective. Therefore, such data is typically only collected when the customer pays for extra services relying on that data. This motivated us to develop a multi-task ensemble approach to transfer knowledge from the high-resolution data and enable vehicle behavior prediction from low-resolution but high dimensional data that is aggregated over time in the vehicles.

    This study proposes a multi-task snapshot-stacked ensemble (MTSSE) deep neural network for vehicle behavior prediction by considering vehicles’ low-resolution operational life records. The multi-task ensemble approach utilizes the measurements to map the low-frequency vehicle usage to the vehicle behaviors defined from the high-resolution time-resolved data. Two data sources are integrated and used: high-resolution data called Dynafleet, and low-resolution so-called Logged Vehicle Data (LVD). The experimental results demonstrate the proposed approach’s effectiveness in predicting the vehicle behavior from low frequency data. With the suggested multi-task snapshot-stacked ensemble deep network, it is shown how low-resolution sensor data can highly contribute to predicting multiple vehicle behaviors simultaneously while using only one single training process. © 2022 The Author(s)

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  • 9.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology. Malmö University, Malmo, Sweden.
    Tajgardan, Mohsen
    Qom University Of Technology, Qom, Iran.
    Lundström, Jens
    Halmstad University, School of Information Technology.
    Rabbani, Mahdi
    Canadian Institute For Cybersecurity, Fredericton, Canada.
    Tegnered, Daniel
    Volvo Group, Gothenburg, Sweden.
    A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction2023In: Sensors, E-ISSN 1424-8220, Vol. 23, no 12, article id 5621Article in journal (Refereed)
    Abstract [en]

    Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system’s effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach. © 2023 by the authors.

  • 10.
    Khoshkangini, Reza
    et al.
    Halmstad University, School of Information Technology. Malmö University, Malmö, Sweden.
    Tajgardan, Mohsen
    Qom University of Technology, Qom, Iran.
    Sheikholharam Mashhadi, Peyman
    Halmstad University, School of Information Technology.
    Rögnvaldsson, Thorsteinn
    Halmstad University, School of Information Technology.
    Tegnered, Daniel
    Volvo Group Connected Solutions, Gothenburg, Sweden.
    Optimal Task Grouping Approach in Multitask Learning2024In: Neural Information Processing. ICONIP 2023 / [ed] Biao Luo; Long Cheng; Zheng-Guang Wu, Hongyi Li; Chaojie Li, Heidelberg: Springer Nature, 2024, p. 206-225Conference paper (Refereed)
    Abstract [en]

    Multi-task learning has become a powerful solution in which multiple tasks are trained together to leverage the knowledge learned from one task to improve the performance of the other tasks. However, the tasks are not always constructive on each other in the multi-task formulation and might play negatively during the training process leading to poor results. Thus, this study focuses on finding the optimal group of tasks that should be trained together for multi-task learning in an automotive context. We proposed a multi-task learning approach to model multiple vehicle long-term behaviors using low-resolution data and utilized gradient descent to efficiently discover the optimal group of tasks/vehicle behaviors that can increase the performance of the predictive models in a single training process. In this study, we also quantified the contribution of individual tasks in their groups and to the other groups’ performance. The experimental evaluation of the data collected from thousands of heavy-duty trucks shows that the proposed approach is promising. © 2024 Springer Nature

  • 11.
    Rabbani, Mahdi
    et al.
    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
    Wang, Yongli
    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
    Khoshkangini, Reza
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Jelodar, Hamed
    Faculty of Computer Science, Dalhousie University, Halifax, Nova Scotia, Canada.
    Zhao, Ruxin
    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
    Bagheri Baba Ahmadi, Sajjad
    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
    Ayobi, Seyedvalyallah
    School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China.
    A Review on Machine Learning Approaches for Network Malicious Behavior Detection in Emerging Technologies2021In: Entropy, E-ISSN 1099-4300, Vol. 23, no 5, article id 529Article, review/survey (Refereed)
    Abstract [en]

    Network anomaly detection systems (NADSs) play a significant role in every network defense system as they detect and prevent malicious activities. Therefore, this paper offers an exhaustive overview of different aspects of anomaly-based network intrusion detection systems (NIDSs). Additionally, contemporary malicious activities in network systems and the important properties of intrusion detection systems are discussed as well. The present survey explains important phases of NADSs, such as pre-processing, feature extraction and malicious behavior detection and recognition. In addition, with regard to the detection and recognition phase, recent machine learning approaches including supervised, unsupervised, new deep and ensemble learning techniques have been comprehensively discussed; moreover, some details about currently available benchmark datasets for training and evaluating machine learning techniques are provided by the researchers. In the end, potential challenges together with some future directions for machine learning-based NADSs are specified.

  • 12.
    Rabbani, Mahdi
    et al.
    Nanjing University of Science and Technology, Nanjing, China.
    Wang, Young Li
    Nanjing University of Science and Technology, Nanjing, China.
    Khoshkangini, Reza
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Jelodar, Hamed
    Nanjing University of Science and Technology, Nanjing, China.
    Zhao, Ruxin
    Nanjing University of Science and Technology, Nanjing, China.
    Hu, Peng
    Nanjing University of Science and Technology, Nanjing, China.
    A Hybrid Machine Learning Approach for Malicious Behaviour Detection and Recognition in Cloud Computing2020In: Journal of Network and Computer Applications, ISSN 1084-8045, E-ISSN 1095-8592, Vol. 151, article id 102507Article in journal (Refereed)
    Abstract [en]

    The rapid growth of new emerging computing technologies has encouraged many organizations to outsource their data and computational requirements. Such services are expected to always provide security principles such as confidentiality, availability and integrity; therefore, a highly secure platform is one of the most important aspects of cloud-based computing environments. A considerable improvement over traditional security strategies is achieved by understanding how malware behaves over the entire behavioural space. In this paper, we propose a new approach to improve the capability of cloud service providers to model users’ behaviours. We applied a particle swarm optimization-based probabilistic neural network (PSO-PNN) for the detection and recognition process, in the first module of the recognition process, we meaningfully converted the users’ behaviours to an understandable format and then classified and recognized the malicious behaviours by using a multi-layer neural network. We took advantage of the UNSW-NB15 dataset to validate the proposed solution by characterizing different types of malicious behaviours exhibited by users. Evaluation of the experimental results shows that the proposed method is promising for use in security monitoring and recognition of malicious behaviours. © 2019 Elsevier Ltd

  • 13.
    Revanur, Vandan
    et al.
    Halmstad University.
    Ayibiowu, Ayodeji
    Halmstad University.
    Rahat, Mahmoud
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Khoshkangini, Reza
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.
    Embeddings Based Parallel Stacked Autoencoder Approach for Dimensionality Reduction and Predictive Maintenance of Vehicles2020In: IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning / [ed] Joao Gama, Sepideh Pashami, Albert Bifet, Moamar Sayed-Mouchawe, Holger Fröning, Franz Pernkopf, Gregor Schiele, Michaela Blott, Heidelberg: Springer, 2020, p. 127-141Conference paper (Refereed)
    Abstract [en]

    Predictive Maintenance (PdM) of automobiles requires the storage and analysis of large amounts of sensor data. This requirement can be challenging in deploying PdM algorithms onboard the vehicles due to limited storage and computational power on the hardware of the vehicle. Hence, this study seeks to obtain low dimensional descriptive features from high dimensional data using Representation Learning. The low dimensional representation can then be used for predicting vehicle faults, in particular a component related to the powertrain. A Parallel Stacked Autoencoder based architecture is presented with the aim of producing better representations when compared to individual Autoen-coders with focus on vehicle data. Also, Embeddings are employed on categorical Variables to aid the performance of the artificial neural networks (ANN) models. This architecture is shown to achieve excellent performance, and in close standards to the previous state-of-the-art research. Significant improvement in powertrain failure prediction is obtained along with a reduction in the size of input data using our novel deep learning ANN architecture.

    © Springer Nature Switzerland AG 2020

  • 14.
    Srihari, Monisha
    et al.
    Halmstad University, School of Information Technology.
    Gholipour, Zahra
    Halmstad University, School of Information Technology.
    Khoshkangini, Reza
    Halmstad University, School of Information Technology.
    Orand, Abbas
    Halmstad University, School of Information Technology.
    Optimization of the Hybrid Feature Learning Algorithm2022In: 2022 Swedish Artificial Intelligence Society Workshop (SAIS), IEEE, 2022Conference paper (Refereed)
    Abstract [en]

    In recent years, machine learning (ML) algorithms have been used to minimize maintenance costs and identify problems early in the automotive sector. The determination of an asset's residual useful life of a component at a specific time is known as 'remaining useful life' (RUL). The extensive evolution of data makes it challenging to analyze and interpret high-level and valuable features from the data. The issue arises in all disciplines, and the automotive industry is no exception, given the large number of sensors to consider. Existing RUL research has not given much thought to the influence of high dimensionality data on component maintenance and deterioration. The fundamental purpose of feature selection (FS) is to select a subset of features from the data without compromising model performance. This work proposes a hybrid approach to the FS problem that combines Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). When tested on public datasets, our results demonstrate a rise in regression accuracy and a reduction in the number of selected features. © 2022 IEEE.

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