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  • 51.
    Pashami, Sepideh
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Holst, Anders
    RISE SICS, Stockholm, Sweden.
    Bae, Juhee
    School of Informatics, University of Skövde, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Causal discovery using clusters from observational data2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Many methods have been proposed over the years for distinguishing causes from effects using observational data only, and new ones are continuously being developed – deducing causal relationships is difficult enough that we do not hope to ever get the perfect one. Instead, we progress by creating powerful heuristics, capable of capturing more and more of the hints that are present in real data.

    One type of such hints, quite surprisingly rarely explicitly addressed by existing methods, is in-homogeneities in the data. Clusters are a very typical occurrence that should be taken into account, and exploited, in the process of identifying causes and effects. In this paper, we discuss the potential benefits, and explore the hints that clusters in the data can provide for causal discovery. We propose a new method, and show, using both artificial and real data, that accounting for clusters in the data leads to more accurate learning of causal structures.

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  • 52.
    Pirasteh, Parivash
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Löwenadler, Magnus
    Aftermarket Solutions Department, Volvo Trucks, Gothenburg, Sweden.
    Thunberg, Klas
    Service Market Products, Volvo Buses, Gothenburg, Sweden.
    Ydreskog, Henrik
    Aftermarket Solutions Department, Volvo Trucks, Gothenburg, Sweden.
    Berck, Peter
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain2019Ingår i: Proceedings of the Workshop on Interactive Data Mining, New York, NY: Association for Computing Machinery (ACM), 2019, artikel-id 4Konferensbidrag (Refereegranskat)
    Abstract [en]

    Predicting future maintenance needs of equipment can be addressed in a variety of ways. Methods based on machine learning approaches provide an interesting platform for mining large data sets to find patterns that might correlate with a given fault. In this paper, we approach predictive maintenance as a classification problem and use Random Forest to separate data readouts within a particular time window into those corresponding to faulty and non-faulty component categories. We utilize diagnostic trouble codes (DTCs) as an example of event-based data, and propose four categories of features that can be derived from DTCs as a predictive maintenance framework. We test the approach using large-scale data from a fleet of heavy duty trucks, and show that DTCs can be used within our framework as indicators of imminent failures in different components.

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  • 53.
    Prytz, Rune
    et al.
    Volvo Technology, Göteborg, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Towards relation discovery for diagnostics2011Ingår i: Proceedings of the First International Workshop on Data Mining for Service and Maintenance, New York, NY: Association for Computing Machinery (ACM), 2011, s. 23-27Konferensbidrag (Refereegranskat)
    Abstract [en]

    It is difficult to implement predictive maintenance in the automotive industry as it looks today, since the sensor capabilities and engineering effort available for diagnostic purposes is limited. It is, in practice, impossible to develop diagnostic algorithms capable of detecting many different kinds of faults that would be applicable to a wide range of vehicle configurations and usage patterns. However, it is now becoming feasible to obtain and analyse on-board data on vehicles as they are being used. It makes automatic data-mining methods an attractive alternative, since they are capable of adapting themselves to specific vehicle configurations and usage. In order to be useful, though, such methods need to be able to detect interesting relations between a large number of available signals. This paper presents an unsupervised method for discovering useful relations between measured signals in a Volvo truck, both during normal operations and when a fault has occurred. The interesting relationships are found in a two-step procedure. In the first step, we identify a set of “good” models, by establishing an MSE threshold over the complete data set. In the second step, we estimate model parameters over time, in order to capture the dynamic behaviour of the system. We use two different approaches here, the LASSO method and the Recursive Least Squares filter. The usefulness of obtained relations is then evaluated using supervised learning to separate different classes of faults.

  • 54.
    Prytz, Rune
    et al.
    Volvo Group Trucks Technology, Advanced Technology & Research, Göteborg, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Analysis of Truck Compressor Failures Based on Logged Vehicle Data2013Ingår i: / [ed] Hamid Reza Arabnia, CSREA Press, 2013Konferensbidrag (Refereegranskat)
    Abstract [en]

    In multiple industries, including automotive one, predictive maintenance is becoming more and more important, especially since the focus shifts from product to service-based operation. It requires, among other, being able to provide customers with uptime guarantees. It is natural to investigate the use of data mining techniques, especially since the same shift of focus, as well as technological advancements in the telecommunication solutions, makes long-term data collection more widespread.

    In this paper we describe our experiences in predicting compressor faults using data that is logged on-board Volvo trucks. We discuss unique challenges that are posed by the specifics of the automotive domain. We show that predictive maintenance is possible and can result in significant cost savings, despite the relatively low amount of data available. We also discuss some of the problems we have encountered by employing out-of-the-box machine learning solutions, and identify areas where our task diverges from common assumptions underlying the majority of data mining research.

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  • 55.
    Prytz, Rune
    et al.
    Volvo Group Trucks Technology, Gothenburg, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data2015Ingår i: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 41, s. 139-150Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Methods and results are presented for applying supervised machine learning techniques to the task of predicting the need for repairs of air compressors in commercial trucks and buses. Prediction models are derived from logged on-board data that are downloaded during workshop visits and have been collected over three years on large number of vehicles. A number of issues are identified with the data sources, many of which originate from the fact that the data sources were not designed for data mining. Nevertheless, exploiting this available data is very important for the automotive industry as means to quickly introduce predictive maintenance solutions. It is shown on a large data set from heavy duty trucks in normal operation how this can be done and generate a profit.

    Random forest is used as the classifier algorithm, together with two methods for feature selection whose results are compared to a human expert. The machine learning based features outperform the human expert features, which supports the idea to use data mining to improve maintenance operations in this domain. © 2015 Elsevier Ltd.

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  • 56.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Prytz, Rune
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Wisdom of Crowds for Intelligent Monitoring of Vehicle FleetsManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    An approach is presented and experimentally demonstrated where consensus among distributed self-organized agents is used for intelligent monitoring of mobile cyberphysical systems (in this case vehicles). The demonstration is done on test data from a 30 month long field test with a city bus fleet under real operating conditions. The self-organized models operate on-board the systems, like embedded agents, communicate their states over a wireless communication link, and their states are compared off-line to find systems that deviate from the consensus. In this way is the group (the fleet) of systems used to detect errors that actually occur. This can be used to build up a knowledge base that can be accumulated over the life-time of the systems.

  • 57.
    Rögnvaldsson, Thorsteinn
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Byttner, Stefan
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Prytz, Rune
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Svensson, Magnus
    Volvo Group Trucks Technology, Göteborg, Sweden.
    Self-monitoring for maintenance of vehicle fleets2018Ingår i: Data mining and knowledge discovery, ISSN 1384-5810, E-ISSN 1573-756X, Vol. 32, nr 2, s. 344-384Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An approach for intelligent monitoring of mobile cyberphysical systems is described, based on consensus among distributed self-organised agents. Its usefulness is experimentally demonstrated over a long-time case study in an example domain: a fleet of city buses. The proposed solution combines several techniques, allowing for life-long learning under computational and communication constraints. The presented work is a step towards autonomous knowledge discovery in a domain where data volumes are increasing, the complexity of systems is growing, and dedicating human experts to build fault detection and diagnostic models for all possible faults is not economically viable. The embedded, self-organised agents operate on-board the cyberphysical systems, modelling their states and communicating them wirelessly to a back-office application. Those models are subsequently compared against each other to find systems which deviate from the consensus. In this way the group (e.g. a fleet of vehicles) is used to provide a standard, or to describe normal behaviour, together with its expected variability under particular operating conditions. The intention is to detect faults without the need for human experts to anticipate them beforehand. This can be used to build up a knowledge base that accumulates over the life-time of the systems. The approach is demonstrated using data collected during regular operation of a city bus fleet over the period of almost four years. © 2017 The Author(s)

  • 58.
    Sheikholharam Mashhadi, Peyman
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life2020Ingår i: Applied Sciences, E-ISSN 2076-3417, Vol. 10, nr 1, artikel-id 69Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Predictive Maintenance (PM) is a proactive maintenance strategy that tries to minimize a system’s downtime by predicting failures before they happen. It uses data from sensors to measure the component’s state of health and make forecasts about its future degradation. However, existing PM methods typically focus on individual measurements. While it is natural to assume that a history of measurements carries more information than a single one. This paper aims at incorporating such information into PM models. In practice, especially in the automotive domain, diagnostic models have low performance, due to a large amount of noise in the data and limited sensing capability. To address this issue, this paper proposes to use a specific type of ensemble learning known as Stacked Ensemble. The idea is to aggregate predictions of multiple models—consisting of Long Short-Term Memory (LSTM) and Convolutional-LSTM—via a meta model, in order to boost performance. Stacked Ensemble model performs well when its base models are as diverse as possible. To this end, each such model is trained using a specific combination of the following three aspects: feature subsets, past dependency horizon, and model architectures. Experimental results demonstrate benefits of the proposed approach on a case study of heavy-duty truck turbochargers. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). 

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  • 59.
    Soliman, Amira
    et al.
    RISE SICS, Stockholm, Sweden.
    Girdzijauskas, Sarunas
    RISE SICS, Stockholm, Sweden.
    Bouguelia, Mohamed-Rafik
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Decentralized and Adaptive K-Means Clustering for Non-IID Data using HyperLogLog Counters2020Konferensbidrag (Refereegranskat)
    Abstract [en]

    The data shared over the Internet tends to originate from ubiquitous and autonomous sources such as mobile phones, fitness trackers, and IoT devices. Centralized and federated machine learning solutions represent the predominant way of providing smart services for users. However, moving data to central location for analysis causes not onlymany privacy concerns, but also communication overhead. Therefore, incertain situations machine learning models need to be trained in a collaborative and decentralized manner, similar to the way the data is originally generated without requiring any central authority for data or modelaggregation. This paper presents a decentralized and adaptive k-means algorithm that clusters data from multiple sources organized in peer-to-peer networks. Our algorithm allows peers to reach an approximation of the global model without sharing any raw data. Most importantly, we address the challenge of decentralized clustering with skewed non-IID data and asynchronous computations by integrating HyperLogLog counters with k-means algorithm. Furthermore, our clustering algorithm allows nodes to individually determine the number of clusters that fits their local data. Results using synthetic and real-world datasets show that our algorithm outperforms state-of-the-art decentralized k-means algorithms achieving accuracy gain that is up-to 36%.

  • 60.
    Stefanowski, Jerzy
    et al.
    Poznan University of Technology.
    Nowaczyk, Sławomir
    Lund University.
    An Experimental Study of Using Rule Induction Algorithm in Combiner Multiple Classifier2007Ingår i: International Journal of Computational Intelligence Research, ISSN 0974-1259, Vol. 3, nr 4, s. 335-342Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Multiple classifiers consist of sets of subclassifiers, whose individual predictions are combined to classify new objects. These approaches attract an interest of researchers as they can outperform single classifiers on wide range of classification problems. This paper presents an experimental study of using the rule induction algorithm MODLEM in the multiple classifier scheme called combiner, which is a specific meta learning approach to aggregate answers of component classifiers. Our experimental results show that the improvement of predictive accuracy depends on the independence of errors made by the base classifiers. Moreover, we summarise our experience with using MODLEM as component in other multiple classifiers, namely bagging and n2 classifiers.

  • 61.
    Stefanowski, Jerzy
    et al.
    Poznan University of Technology.
    Nowaczyk, Sławomir
    Lund University.
    On using rule induction in multiple classifiers with a combiner aggregation strategy2005Ingår i: Proceedings - 5th International Conference on Intelligent Systems Design and Applications 2005, ISDA '05, Los Alamitos: IEEE Computer Society, 2005, s. 432-437Konferensbidrag (Refereegranskat)
    Abstract [en]

    The paper is an experimental study of using the rough sets based rule induction algorithm MODLEM in the framework of multiple classifiers. Particular attention is paid to using a meta-classifier called combiner, which learns how to aggregate answers of component classifiers. The experimental results confirm that the range of classification improvement for the combiner depends on the independence of errors made by the component classifiers. Moreover, we summarize the experience with using MODLEM in other multiple classifiers, namely the bagging and n 2 classifiers. © 2005 IEEE.

  • 62.
    Teng, Xudong
    et al.
    Shanghai University of Engineering Science, Shanghai, China & Nanjing University, Nanjing, China.
    Fan, Yuantao
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), Laboratoriet för intelligenta system.
    Evaluation of Micro-flaws in Metallic Material Based on A Self-Organized Data-driven Approach2016Ingår i: 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), IEEE conference proceedings, 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Evaluating the health condition of a material that could potentially contain micro-flaws is a common and important application within the field of non-destructive testing. Examples of such micro-defects include dislocation, fatigue cracks or impurities and are often hard to detect. The ability to precisely measure their type, size and position is a prerequisite for estimating the remaining useful life of the component. One technique that was shown successful in the past is based on traditional ultrasonic testing methods. In most cases, inner micro-flaws induce slight changes of acoustic wave spectrum components. However, these changes are often difficult to detect directly, as they tend to exhibit features that are most naturally analyzed using statistical and probabilistic methods. In this paper we apply Consensus Self-Organizing Models (COSMO) method to detect micro-flaws in metallic material. This approach is essentially an unsupervised deviation detection method based on the concept of "wisdom of the crowd". This method is used to analyze the spectrum of acoustic waves received by the transducer attached on the surface of material being analyzed. We have modeled a steel board with micro-cracks and collected time-series of acoustic echo response, at different positions on material's surface. The experimental results show that the COSMO method is able to detect and locate micro-flaws. © 2016 IEEE

  • 63.
    Vaiciukynas, Evaldas
    et al.
    Department of Information Systems, Kaunas University of Technology, Kaunas, Lithuania.
    Uličný, Matej
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Learning Low-Dimensional Representation of Bivariate Histogram Data2018Ingår i: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, nr 11, s. 3723-3735Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    With an increasing amount of data in intelligent transportation systems, methods are needed to automatically extract general representations that accurately predict not only known tasks but also similar tasks that can emerge in the future. Creation of low-dimensional representations can be unsupervised or can exploit various labels in multi-task learning (when goal tasks are known) or transfer learning (when they are not) settings. Finding a general, low-dimensional representation suitable for multiple tasks is an important step toward knowledge discovery in aware intelligent transportation systems. This paper evaluates several approaches mapping high-dimensional sensor data from Volvo trucks into a low-dimensional representation that is useful for prediction. Original data are bivariate histograms, with two types--turbocharger and engine--considered. Low-dimensional representations were evaluated in a supervised fashion by mean equal error rate (EER) using a random forest classifier on a set of 27 1-vs-Rest detection tasks. Results from unsupervised learning experiments indicate that using an autoencoder to create an intermediate representation, followed by $t$-distributed stochastic neighbor embedding, is the most effective way to create low-dimensional representation of the original bivariate histogram. Individually, $t$-distributed stochastic neighbor embedding offered best results for 2-D or 3-D and classical autoencoder for 6-D or 10-D representations. Using multi-task learning, combining unsupervised and supervised objectives on all 27 available tasks, resulted in 10-D representations with a significantly lower EER compared to the original 400-D data. In transfer learning setting, with topmost diverse tasks used for representation learning, 10-D representations achieved EER comparable to the original representation.

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