hh.sePublications
Change search
Link to record
Permanent link

Direct link
BETA
Publications (10 of 14) Show all publications
Sheikholharam Mashhadi, P., Nowaczyk, S. & Pashami, S. (2020). Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life. Applied Sciences, 10(1), Article ID 69.
Open this publication in new window or tab >>Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life
2020 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. 10, no 1, article id 69Article in journal (Refereed) Published
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/). 

Place, publisher, year, edition, pages
Basel: MDPI, 2020
Keywords
predictive maintenance, remaining useful life, recurrent neural networks, LSTM, Stacked Ensemble
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-41314 (URN)10.3390/app10010069 (DOI)
Projects
HEALTH-VINNOVA
Funder
Vinnova
Available from: 2019-12-29 Created: 2019-12-29 Last updated: 2020-01-21Bibliographically approved
Dahl, O., Johansson, F., Khoshkangini, R., Pashami, S., Nowaczyk, S. & Pihl, C. (2020). Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning. In: : . Paper presented at The 3rd International Conference on Information Management and Processing (ICIMP 2020), Portsmouth, United Kingdom, June 11-13, 2020.
Open this publication in new window or tab >>Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters - Using Clustering and Rule-Based Machine Learning
Show others...
2020 (English)Conference paper, Published 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 LVD in order to evaluate whether they align with vehicle configurations, so-called GTA parameters. GMMs are employed to cluster and classify various vehicle behaviors from the LVD. 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.

Keywords
Machine Learning, Clustering, Usage Behaviors, Association Rule Mining, Gaussian Mixture Models.
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-41214 (URN)
Conference
The 3rd International Conference on Information Management and Processing (ICIMP 2020), Portsmouth, United Kingdom, June 11-13, 2020
Available from: 2019-12-10 Created: 2019-12-10 Last updated: 2020-02-17
Cooney, M., Ong, L., Pashami, S., Järpe, E. & Ashfaq, A. (2019). Avoiding Improper Treatment of Dementia Patients by Care Robots. In: : . Paper presented at The Dark Side of Human-Robot Interaction: Ethical Considerations and Community Guidelines for the Field of HRI. HRI Workshop, Daegu, South Korea, March 11, 2019.
Open this publication in new window or tab >>Avoiding Improper Treatment of Dementia Patients by Care Robots
Show others...
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The phrase “most cruel and revolting crimes” has been used to describe some poor historical treatment of vulnerable impaired persons by precisely those who should have had the responsibility of protecting and helping them. We believe we might be poised to see history repeat itself, as increasingly humanlike aware robots become capable of engaging in behavior which we would consider immoral in a human–either unknowingly or deliberately. In the current paper we focus in particular on exploring some potential dangers affecting persons with dementia (PWD), which could arise from insufficient software or external factors, and describe a proposed solution involving rich causal models and accountability measures: Specifically, the Consequences of Needs-driven Dementia-compromised Behaviour model (C-NDB) could be adapted to be used with conversation topic detection, causal networks and multi-criteria decision making, alongside reports, audits, and deterrents. Our aim is that the considerations raised could help inform the design of care robots intended to support well-being in PWD.

Keywords
care robot, therapy robot, dementia, ethics
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-39448 (URN)
Conference
The Dark Side of Human-Robot Interaction: Ethical Considerations and Community Guidelines for the Field of HRI. HRI Workshop, Daegu, South Korea, March 11, 2019
Funder
Knowledge Foundation, 20140220
Note

Funder: EU REMIND project (H2020-MSCA-RISE No 734355)

Available from: 2019-05-22 Created: 2019-05-22 Last updated: 2020-02-17
Said, A., Parra, D., Bae, J. & Pashami, S. (2019). IDM-WSDM 2019: Workshop on Interactive Data Mining. In: WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data. Paper presented at The Twelfth ACM International Conference on Web Search and Data (WSDM 2019), Melbourne, Australia, February 11-15, 2019 (pp. 846-847). New York, NY: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>IDM-WSDM 2019: Workshop on Interactive Data Mining
2019 (English)In: WSDM '19: Proceedings of the Twelfth ACM International Conference on Web Search and Data, New York, NY: Association for Computing Machinery (ACM), 2019, p. 846-847Conference paper, Oral presentation with published abstract (Refereed)
Abstract [en]

The first Workshop on Interactive Data Mining is held in Melbourne, Australia, on February 15, 2019 and is co-located with 12th ACM International Conference on Web Search and Data Mining (WSDM 2019). The goal of this workshop is to share and discuss research and projects that focus on interaction with and interactivity of data mining systems. The program includes invited speaker, presentation of research papers, and a discussion session.

Place, publisher, year, edition, pages
New York, NY: Association for Computing Machinery (ACM), 2019
Keywords
Data mining, Interactive classification and clustering, Human-in-the-loop, Visual modeling, Interactive dashboards
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-41472 (URN)10.1145/3289600.3291376 (DOI)000482120400120 ()2-s2.0-85061736320 (Scopus ID)978-1-4503-5940-5 (ISBN)
Conference
The Twelfth ACM International Conference on Web Search and Data (WSDM 2019), Melbourne, Australia, February 11-15, 2019
Available from: 2020-01-31 Created: 2020-01-31 Last updated: 2020-02-17Bibliographically approved
Holst, A., Pashami, S. & Bae, J. (2019). Incremental causal discovery and visualization. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019. Association for Computing Machinery, Inc
Open this publication in new window or tab >>Incremental causal discovery and visualization
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery, Inc , 2019Conference paper, Published paper (Refereed)
Abstract [en]

Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. © 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM.

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2019
Keywords
Causal Discovery, Correlation, Incremental Visualization, Correlation methods, Data mining, Visualization, Causal graph, Causal relations, Discovery algorithm, Incremental discoveries, Novel visualizations, Data visualization
National Category
Probability Theory and Statistics Media Engineering Computer Sciences
Identifiers
urn:nbn:se:hh:diva-41536 (URN)10.1145/3304079.3310287 (DOI)2-s2.0-85069768142 (Scopus ID)9781450362962 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Available from: 2020-02-04 Created: 2020-02-04 Last updated: 2020-02-04Bibliographically approved
Pirasteh, P., Nowaczyk, S., Pashami, S., Löwenadler, M., Thunberg, K., Ydreskog, H. & Berck, P. (2019). Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain. In: Proceedings of the Workshop on Interactive Data Mining: . Paper presented at WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia, 11-15 February, 2019. New York, NY: Association for Computing Machinery (ACM), Article ID 4.
Open this publication in new window or tab >>Interactive feature extraction for diagnostic trouble codes in predictive maintenance: A case study from automotive domain
Show others...
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, New York, NY: Association for Computing Machinery (ACM), 2019, article id 4Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
New York, NY: Association for Computing Machinery (ACM), 2019
Keywords
Predictive maintenance, failure detection, diagnostic trouble codes, feature extraction
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-40184 (URN)10.1145/3304079.3310288 (DOI)2-s2.0-85069771384 (Scopus ID)978-1-4503-6296-2 (ISBN)
Conference
WSDM 2019: The 12th ACM International Conference on Web Search and Data Mining, Melbourne, VIC, Australia, 11-15 February, 2019
Available from: 2019-07-07 Created: 2019-07-07 Last updated: 2020-02-03Bibliographically approved
Chen, K., Pashami, S., Fan, Y. & Nowaczyk, S. (2019). Predicting Air Compressor Failures Using Long Short Term Memory Networks. In: Paulo Moura Oliveira, Paulo Novais, Luís Paulo Reis (Ed.), Progress in Artificial Intelligence: 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Vila Real, Portugal, September 3–6, 2019, Proceedings, Part I. Paper presented at 19th EPIA Conference on Artificial Intelligence (EPIA 2019), Vila Real, Portugal, September 3–6, 2019 (pp. 596-609). Cham: Springer
Open this publication in new window or tab >>Predicting Air Compressor Failures Using Long Short Term Memory Networks
2019 (English)In: Progress in Artificial Intelligence: 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Vila Real, Portugal, September 3–6, 2019, Proceedings, Part I / [ed] Paulo Moura Oliveira, Paulo Novais, Luís Paulo Reis, Cham: Springer, 2019, p. 596-609Conference paper, Published paper (Refereed)
Abstract [en]

We introduce an LSTM-based method for predicting compressor failures using aggregated sensory data, and evaluate it using historical information from over 1000 heavy duty vehicles during 2015 and 2016. The goal is to proactively identify trucks that will require maintenance in the near future, so that component replacement can be scheduled before the failure happens, translating into improved uptime. The problem is formulated as a classification task of whether a compressor failure will happen within the specified prediction horizon. A recurrent neural network using Long Short-Term Memory (LSTM) architecture is employed as the prediction model, and compared against Random Forest (RF), the solution used in industrial deployment at the moment. Experimental results show that while Random Forest slightly outperforms LSTM in terms of AUC score, the predictions of LSTM stay significantly more stable over time, showing a consistent trend from healthy to faulty class. Additionally, LSTM is also better at detecting the switch from faulty class to the healthy one after a repair. We demonstrate that this stability is important for making repair decisions, especially in questionable cases, and therefore LSTM model is likely to lead to better results in practice. © Springer Nature Switzerland AG 2019

Place, publisher, year, edition, pages
Cham: Springer, 2019
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 11804
Keywords
Fault detection, Predictive maintenance, Recurrent neural networks, Long-short term memory
National Category
Computer Systems
Identifiers
urn:nbn:se:hh:diva-41366 (URN)10.1007/978-3-030-30241-2_50 (DOI)2-s2.0-85072895300 (Scopus ID)978-3-030-30240-5 (ISBN)978-3-030-30241-2 (ISBN)
Conference
19th EPIA Conference on Artificial Intelligence (EPIA 2019), Vila Real, Portugal, September 3–6, 2019
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-14Bibliographically approved
Khoshkangini, R., Pashami, S. & Nowaczyk, S. (2019). Warranty Claim Rate Prediction using Logged Vehicle Data. Paper presented at 19th EPIA Conference on Artificial Intelligence (EPIA 2019), Vila Real, Portugal, 3-6 September, 2019. Lecture Notes in Computer Science, 11804, 663-674
Open this publication in new window or tab >>Warranty Claim Rate Prediction using Logged Vehicle Data
2019 (English)In: Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349, Vol. 11804, p. 663-674Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Heidelberg: Springer, 2019
Keywords
Warranty Claim Predictive, Machine Learning, Fault De- tection
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-41207 (URN)10.1007/978-3-030-30241-2_55 (DOI)2-s2.0-85072879357 (Scopus ID)
Conference
19th EPIA Conference on Artificial Intelligence (EPIA 2019), Vila Real, Portugal, 3-6 September, 2019
Available from: 2019-12-10 Created: 2019-12-10 Last updated: 2019-12-11Bibliographically approved
Pashami, S., Holst, A., Bae, J. & Nowaczyk, S. (2018). Causal discovery using clusters from observational data. In: : . Paper presented at FAIM'18 Workshop on CausalML, Stockholm, Sweden, July 15, 2018.
Open this publication in new window or tab >>Causal discovery using clusters from observational data
2018 (English)Conference paper, Published paper (Refereed)
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.

National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-39216 (URN)
Conference
FAIM'18 Workshop on CausalML, Stockholm, Sweden, July 15, 2018
Available from: 2019-04-09 Created: 2019-04-09 Last updated: 2019-04-11Bibliographically approved
Vaiciukynas, E., Uličný, M., Pashami, S. & Nowaczyk, S. (2018). Learning Low-Dimensional Representation of Bivariate Histogram Data. IEEE transactions on intelligent transportation systems (Print), 19(11), 3723-3735
Open this publication in new window or tab >>Learning Low-Dimensional Representation of Bivariate Histogram Data
2018 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 11, p. 3723-3735Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2018
Keywords
Task analysis, Histograms, Engines, Intelligent transportation systems, Maintenance engineering, Machine learning, Feature extraction
National Category
Other Computer and Information Science
Identifiers
urn:nbn:se:hh:diva-38252 (URN)10.1109/TITS.2018.2865103 (DOI)000449978100029 ()2-s2.0-85053294183 (Scopus ID)
Available from: 2018-11-04 Created: 2018-11-04 Last updated: 2020-02-03Bibliographically approved
Projects
Data-Driven Predictive Maintenance for Trucks [2016-03451_Vinnova]; Halmstad University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org//0000-0003-3272-4145

Search in DiVA

Show all publications