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Xin, Z., Xudong, T. & Fan, Y. (2025). Detecting changes in air composition based on speed of sound. Applied Acoustics, 229, 1-6, Article ID 110393.
Open this publication in new window or tab >>Detecting changes in air composition based on speed of sound
2025 (English)In: Applied Acoustics, ISSN 0003-682X, E-ISSN 1872-910X, Vol. 229, p. 1-6, article id 110393Article in journal (Refereed) In press
Abstract [en]

For detecting changes in air composition, the traditional method based on measuring the speed of sound lacks selectivity for different gaseous species and is easily influenced by environmental effects such as pressure, humidity, and temperature. Additionally, this method is difficult to be used for the quantitative analysis of air mixed with an unknown gas. In this paper, a data-driven model is developed for detecting changes in air composition from a qualitative perspective. By comparing the measured speed of sound with that theoretically calculated using the virial expansion for real air, the precise differences are used as data to construct a distance matrix, then the most typical speed difference is identified in order to calculate the z-score, from which the one-sided p-value (which is the probability of the z-score from a normal distribution) is calculated to detect a change in air composition at a given significance level. Experimental results show that the proposed data-driven model can accurately locate the time of change and determine the change intervals for air composition variations, and it has better accuracy and a lower value of RFT, almost equal to zero, compared with methods such as quartiles, standard deviation, interquartile range, and Bayesian detection and thus can be applied to domestic and industrial sensors for air monitoring, gas detection, and gas pollution alarms. © 2024 Elsevier Ltd

Place, publisher, year, edition, pages
Oxford: Elsevier, 2025
Keywords
Data-driven model, P-value, Precise equation, Speed of sound
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:hh:diva-54970 (URN)10.1016/j.apacoust.2024.110393 (DOI)2-s2.0-85208766751 (Scopus ID)
Note

This work was supported by the National Key R&D Program of China (2022YFB3204303), the National Natural Science Foundation of China (No. 11934009).

Available from: 2024-11-26 Created: 2024-11-26 Last updated: 2024-11-26Bibliographically approved
Fan, Y., Nowaczyk, S., Wang, Z. & Pashami, S. (2024). Evaluating Multi-task Curriculum Learning for Forecasting Energy Consumption in Electric Heavy-duty Vehicles. In: Nowaczyk S.; Spiliopoulou M.; Ragni M.; Fink O. (Ed.), Proceedings of Workshop on Embracing Human-Aware AI in Industry 5.0 (HAII5.0 2024): . Paper presented at 2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024, Santiago de Compostela, Spain, 19 October, 2024. Aachen: Rheinisch-Westfaelische Technische Hochschule Aachen, 3765
Open this publication in new window or tab >>Evaluating Multi-task Curriculum Learning for Forecasting Energy Consumption in Electric Heavy-duty Vehicles
2024 (English)In: Proceedings of Workshop on Embracing Human-Aware AI in Industry 5.0 (HAII5.0 2024) / [ed] Nowaczyk S.; Spiliopoulou M.; Ragni M.; Fink O., Aachen: Rheinisch-Westfaelische Technische Hochschule Aachen , 2024, Vol. 3765Conference paper, Published paper (Refereed)
Abstract [en]

Accurate energy consumption prediction is crucial for optimising the operation of electric commercial heavy-duty vehicles, particularly for efficient route planning, refining charging strategies, and ensuring optimal truck configuration for specific tasks. This study investigates the application of multi-task curriculum learning to enhance machine learning models for forecasting the energy consumption of various onboard systems in electric vehicles. Multi-task learning, unlike traditional training approaches, leverages auxiliary tasks to provide additional training signals, which has been shown to enhance predictive performance in many domains. By further incorporating curriculum learning, where simpler tasks are learned before progressing to more complex ones, neural network training becomes more efficient and effective. We evaluate the suitability of these methodologies in the context of electric vehicle energy forecasting, examining whether the combination of multi-task learning and curriculum learning enhances algorithm generalisation, even with limited training data. We primarily focus on understanding the efficacy of different curriculum learning strategies, including sequential learning and progressive continual learning, using complex, real-world industrial data. Our research further explores a set of auxiliary tasks designed to facilitate the learning process by targeting key consumption characteristics projected into future time frames. The findings illustrate the potential of multi-task curriculum learning to advance energy consumption forecasting, significantly contributing to the optimisation of electric heavy-duty vehicle operations. This work offers a novel perspective on integrating advanced machine learning techniques to enhance energy efficiency in the exciting field of electromobility. © 2024 Copyright for this paper by its authors.

Place, publisher, year, edition, pages
Aachen: Rheinisch-Westfaelische Technische Hochschule Aachen, 2024
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 3765
Keywords
Curriculum Learning, Electric Vehicles, Energy Consumption Forecasting, Multi-task Learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-54807 (URN)2-s2.0-85206261149 (Scopus ID)
Conference
2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024, Santiago de Compostela, Spain, 19 October, 2024
Note

12 sidor

Available from: 2024-11-06 Created: 2024-11-06 Last updated: 2024-11-06Bibliographically approved
Fan, Y., Altarabichi, M. G., Pashami, S., Sheikholharam Mashhadi, P. & Nowaczyk, S. (2024). Invariant Feature Selection for Battery State of Health Estimation in Heterogeneous Hybrid Electric Bus Fleets. In: Nowaczyk S.; Spiliopoulou M.; Ragni M.; Fink O. (Ed.), Proceedings of Workshop on Embracing Human-Aware AI in Industry 5.0 (HAII5.0 2024): . Paper presented at 2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024, Santiago de Compostela, Spain, 19 October, 2024. Aachen: Rheinisch-Westfaelische Technische Hochschule Aachen, 3765
Open this publication in new window or tab >>Invariant Feature Selection for Battery State of Health Estimation in Heterogeneous Hybrid Electric Bus Fleets
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2024 (English)In: Proceedings of Workshop on Embracing Human-Aware AI in Industry 5.0 (HAII5.0 2024) / [ed] Nowaczyk S.; Spiliopoulou M.; Ragni M.; Fink O., Aachen: Rheinisch-Westfaelische Technische Hochschule Aachen , 2024, Vol. 3765Conference paper, Published paper (Refereed)
Abstract [en]

Batteries are a safety-critical and the most expensive component for electric buses (EBs). Monitoring their condition, or the state of health (SoH), is crucial for ensuring the reliability of EB operation. However, EBs come in many models and variants, including different mechanical configurations, and deploy to operate under various conditions. Developing new degradation models for each combination of settings and faults quickly becomes challenging due to the unavailability of data for novel conditions and the low evidence for less popular vehicle populations. Therefore, building machine learning models that can generalize to new and unseen settings becomes a vital challenge for practical deployment. This study aims to develop and evaluate feature selection methods for robust machine learning models that allow estimating the SoH of batteries across various settings of EB configuration and usage. Building on our previous work, we propose two approaches, a genetic algorithm for domain invariant features (GADIF) and causal discovery for selecting invariant features (CDIF). Both aim to select features that are invariant across multiple domains. While GADIF utilizes a specific fitness function encompassing both task performance and domain shift, the CDIF identifies pairwise causal relations between features and selects the common causes of the target variable across domains. Experimental results confirm that selecting only invariant features leads to a better generalization of machine learning models to unseen domains. The contribution of this work comprises the two novel invariant feature selection methods, their evaluation on real-world EBs data, and a comparison against state-of-the-art invariant feature selection methods. Moreover, we analyze how the selected features vary under different settings. © 2024 Copyright for this paper by its authors.

Place, publisher, year, edition, pages
Aachen: Rheinisch-Westfaelische Technische Hochschule Aachen, 2024
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 3765
Keywords
Casual Discovery, Genetic Algorithm, Invariant Feature Selection, State of Health Estimation, Transfer Learning
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-54808 (URN)2-s2.0-85206258591 (Scopus ID)
Conference
2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024, Santiago de Compostela, Spain, 19 October, 2024
Note

19 sidor

Available from: 2024-11-06 Created: 2024-11-06 Last updated: 2024-11-06Bibliographically approved
Alabdallah, A., Rögnvaldsson, T., Fan, Y., Pashami, S. & Ohlsson, M. (2023). Discovering Premature Replacements in Predictive Maintenance Time-to-Event Data. In: Takehisa Yairi; Samir Khan; Seiji Tsutsumi (Ed.), Proceedings of the Asia Pacific Conference of the PHM Society 2023: . Paper presented at 4th Asia Pacific Conference of the Prognostics and Health Management, Tokyo, Japan, September 11-14, 2023. New York: The Prognostics and Health Management Society, 4
Open this publication in new window or tab >>Discovering Premature Replacements in Predictive Maintenance Time-to-Event Data
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2023 (English)In: Proceedings of the Asia Pacific Conference of the PHM Society 2023 / [ed] Takehisa Yairi; Samir Khan; Seiji Tsutsumi, New York: The Prognostics and Health Management Society , 2023, Vol. 4Conference paper, Published paper (Refereed)
Abstract [en]

Time-To-Event (TTE) modeling using survival analysis in industrial settings faces the challenge of premature replacements of machine components, which leads to bias and errors in survival prediction. Typically, TTE survival data contains information about components and if they had failed or not up to a certain time. For failed components, the time is noted, and a failure is referred to as an event. A component that has not failed is denoted as censored. In industrial settings, in contrast to medical settings, there can be considerable uncertainty in an event; a component can be replaced before it fails to prevent operation stops or because maintenance staff believe that the component is faulty. This shows up as “no fault found” in warranty studies, where a significant proportion of replaced components may appear fault-free when tested or inspected after replacement.

In this work, we propose an expectation-maximization-like method for discovering such premature replacements in survival data. The method is a two-phase iterative algorithm employing a genetic algorithm in the maximization phase to learn better event assignments on a validation set. The learned labels through iterations are accumulated and averaged to be used to initialize the following expectation phase. The assumption is that the more often the event is selected, the more likely it is to be an actual failure and not a “no fault found”.

Experiments on synthesized and simulated data show that the proposed method can correctly detect a significant percentage of premature replacement cases.

Place, publisher, year, edition, pages
New York: The Prognostics and Health Management Society, 2023
Series
Proceedings of the Asia Pacific Conference of the PHM Society, E-ISSN 2994-7219
Keywords
Survival Analysis, Predictive Maintenance, Early Replacements, Genetic Algorithms
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-52105 (URN)10.36001/phmap.2023.v4i1.3609 (DOI)
Conference
4th Asia Pacific Conference of the Prognostics and Health Management, Tokyo, Japan, September 11-14, 2023
Funder
Knowledge Foundation, 20200001
Note

Som manuscript i avhandling/As manuscript in thesis.

Available from: 2023-11-23 Created: 2023-11-23 Last updated: 2025-01-09Bibliographically approved
Fan, Y., Hamid, S. & Nowaczyk, S. (2023). Incorporating Physics-based Models into Data-Driven Approaches for Air Leak Detection in City Buses. In: Koprinska et al. (Ed.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II. Paper presented at European Conference on Machine Learning (ECML) and Principles and Practice of Knowledge Discovery in Databases (PKDD) 2022, Grenoble, France, September 19–23, 2022 (pp. 438-450). Cham: Springer
Open this publication in new window or tab >>Incorporating Physics-based Models into Data-Driven Approaches for Air Leak Detection in City Buses
2023 (English)In: Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part II / [ed] Koprinska et al., Cham: Springer, 2023, p. 438-450Conference paper, Published paper (Refereed)
Abstract [en]

In this work-in-progress paper two types of physics-based models, for accessing elastic and non-elastic air leakage processes, were evaluated and compared with conventional statistical methods to detect air leaks in city buses, via a data-driven approach. We have access to data streamed from a pressure sensor located in the air tanks of a few city buses, during their daily operations. The air tank in these buses supplies compressed air to drive various components, e.g. air brake, suspension, doors, gearbox, etc. We fitted three physics-based models only to the leakage segments extracted from the air pressure signal and used fitted model parameters as expert features for detecting air leaks. Furthermore, statistical moments of these fitted parameters, over predetermined time intervals, were compared to conventional statistical features on raw pressure values, under a classification setting in discriminating samples before and after the repair of air leak problems. The result of this exploratory study, on six air leak cases, shows that the fitted parameters of the physics-based models are useful for discriminating samples with air leak faults from the fault-free samples, which were observed right after the repair was performed to deal with the air leak problem. The comparison based on ANOVA F-score shows that the proposed features based on fitted parameters of physics-based models outrank the conventional features. It is observed that features of a non-elastic leakage model perform the best. © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

Place, publisher, year, edition, pages
Cham: Springer, 2023
Series
Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937 ; 1753
Keywords
Fault detection, Air Leaks, Elastic air leakage model, Nonelastic air leakage model, Physics-informed machine learning, Explainable Predictive Maintenance
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-48535 (URN)10.1007/978-3-031-23633-4_29 (DOI)000967761200029 ()2-s2.0-85149908480 (Scopus ID)978-3-031-23632-7 (ISBN)978-3-031-23633-4 (ISBN)
Conference
European Conference on Machine Learning (ECML) and Principles and Practice of Knowledge Discovery in Databases (PKDD) 2022, Grenoble, France, September 19–23, 2022
Funder
VinnovaKnowledge FoundationSwedish Research Council
Available from: 2022-10-26 Created: 2022-10-26 Last updated: 2023-08-11Bibliographically approved
Davari, N., Pashami, S., Veloso, B., Nowaczyk, S., Fan, Y., Mota Pereira, P., . . . Gama, J. (2022). A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set. In: Tassadit Bouadi; Elisa Fromont; Eyke Hüllermeier (Ed.), Advances in Intelligent Data Analysis XX: 20th International Symposium on Intelligent Data Analysis, IDA 2022 Rennes, France, April 20–22, 2022: Proceedings. Paper presented at IDA 2022: Advances in Intelligent Data Analysis, Rennes, France, April 20–22, 2022 (pp. 39-52). Cham: Springer
Open this publication in new window or tab >>A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set
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2022 (English)In: Advances in Intelligent Data Analysis XX: 20th International Symposium on Intelligent Data Analysis, IDA 2022 Rennes, France, April 20–22, 2022: Proceedings / [ed] Tassadit Bouadi; Elisa Fromont; Eyke Hüllermeier, Cham: Springer, 2022, p. 39-52Conference paper, Published paper (Refereed)
Abstract [en]

This study applies a data-driven anomaly detection framework based on a Long Short-Term Memory (LSTM) autoencoder network for several subsystems of a public transport bus. The proposed framework efficiently detects abnormal data, significantly reducing the false alarm rate compared to available alternatives. Using historical repair records, we demonstrate how detection of abnormal sequences in the signals can be used for predicting equipment failures. The deviations from normal operation patterns are detected by analysing the data collected from several on-board sensors (e.g., wet tank air pressure, engine speed, engine load) installed on the bus. The performance of LSTM autoencoder (LSTM-AE) is compared against the multi-layer autoencoder (mlAE) network in the same anomaly detection framework. The experimental results show that the performance indicators of the LSTM-AE network, in terms of F1 Score, Recall, and Precision, are better than those of the mlAE network. © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

Place, publisher, year, edition, pages
Cham: Springer, 2022
Series
Lecture Notes in Computer Science book series (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 13205
Keywords
Fault detection, Outliers, Time series, LSTM, Autoencoder
National Category
Signal Processing
Research subject
Health Innovation; Smart Cities and Communities
Identifiers
urn:nbn:se:hh:diva-46654 (URN)10.1007/978-3-031-01333-1_4 (DOI)000937256100004 ()2-s2.0-85128784943 (Scopus ID)978-3-031-01332-4 (ISBN)978-3-031-01333-1 (ISBN)
Conference
IDA 2022: Advances in Intelligent Data Analysis, Rennes, France, April 20–22, 2022
Projects
CHIST-ERA XPM
Funder
Swedish Research Council, 2020-00767
Note

Funding: The CHIST-ERA grant CHIST-ERA-19-XAI-012, project CHIST-ERA/0004/2019 funded by FCT - Fundação para a Ciência e Tecnologia and project 2020-00767 funded by Swedish Research Council.

Available from: 2022-04-12 Created: 2022-04-12 Last updated: 2023-08-21Bibliographically approved
Altarabichi, M. G., Fan, Y., Pashami, S., Sheikholharam Mashhadi, P. & Nowaczyk, S. (2021). Extracting Invariant Features for Predicting State of Health of Batteries in Hybrid Energy Buses. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Porto, Portugal, 6-9 Oct., 2021: . Paper presented at 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Porto, Portugal, 6-9 Oct., 2021 (pp. 1-6). IEEE
Open this publication in new window or tab >>Extracting Invariant Features for Predicting State of Health of Batteries in Hybrid Energy Buses
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2021 (English)In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Porto, Portugal, 6-9 Oct., 2021, IEEE, 2021, p. 1-6Conference paper, Published paper (Refereed)
Abstract [en]

Batteries are a safety-critical and the most expensive component for electric vehicles (EVs). To ensure the reliability of the EVs in operation, it is crucial to monitor the state of health of those batteries. Monitoring their deterioration is also relevant to the sustainability of the transport solutions, through creating an efficient strategy for utilizing the remaining capacity of the battery and its second life. Electric buses, similar to other EVs, come in many different variants, including different configurations and operating conditions. Developing new degradation models for each existing combination of settings can become challenging from different perspectives such as unavailability of failure data for novel settings, heterogeneity in data, low amount of data available for less popular configurations, and lack of sufficient engineering knowledge. Therefore, being able to automatically transfer a machine learning model to new settings is crucial. More concretely, the aim of this work is to extract features that are invariant across different settings.

In this study, we propose an evolutionary method, called genetic algorithm for domain invariant features (GADIF), that selects a set of features to be used for training machine learning models, in such a way as to maximize the invariance across different settings. A Genetic Algorithm, with each chromosome being a binary vector signaling selection of features, is equipped with a specific fitness function encompassing both the task performance and domain shift. We contrast the performance, in migrating to unseen domains, of our method against a number of classical feature selection methods without any transfer learning mechanism. Moreover, in the experimental result section, we analyze how different features are selected under different settings. The results show that using invariant features leads to a better generalization of the machine learning models to an unseen domain.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
State of Health Estimation, Remaining Useful Life Prediction, Invariant Features, Lithium-ion Battery, Transfer Learning, Electric vehicles, Predictive maintenance
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-45895 (URN)10.1109/DSAA53316.2021.9564184 (DOI)000783799800049 ()2-s2.0-85126144193 (Scopus ID)
Conference
2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Porto, Portugal, 6-9 Oct., 2021
Funder
Vinnova
Note

Som manuscript i avhandling/As manuscript in thesis

Available from: 2021-11-17 Created: 2021-11-17 Last updated: 2024-01-24Bibliographically approved
Altarabichi, M. G., Fan, Y., Pashami, S., Nowaczyk, S. & Rögnvaldsson, T. (2020). Predicting state of health and end of life for batteries in hybrid energy buses. In: Baraldi, Piero; Di Maio, Francesco; Zio, Enrico (Ed.), Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference: . Paper presented at 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020, Venice, Italy, 1-5 November, 2020 (pp. 1231-1231). Singapore: Research Publishing Services
Open this publication in new window or tab >>Predicting state of health and end of life for batteries in hybrid energy buses
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2020 (English)In: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference / [ed] Baraldi, Piero; Di Maio, Francesco; Zio, Enrico, Singapore: Research Publishing Services, 2020, p. 1231-1231Conference paper, Published paper (Refereed)
Abstract [en]

There is a major ongoing transition from utilizing fossil fuel to electricity in buses for enabling a more sustainable, environmentally friendly, and connected transportation ecosystem. Batteries are expensive, up to 30% of the total cost for the vehicle (A. Fotouhi 2016), and considered safety-critical components for electric vehicles (EV). As they deteriorate over time, monitoring the health status and performing the maintenance accordingly in a proactive manner is crucial to achieving not only a safe and sustainable transportation system but also a cost-effective operation and thus a greater market satisfaction. As a widely used indicator, the State of Health (SOH) is a measurement that reflects the current capability of the battery in comparison to an ideal condition. Accurate estimation of SOH is important to evaluate the validity of the batteries for the intended application and can be utilized as a proxy to estimate the remaining useful life (RUL) and predict the end-of-life (EOL) of batteries for maintenance planning. The SOH is computed via an on-board computing device, i.e. battery management unit (BMU), which is commonly developed based on controlled experiments and many of them are physical-model based approaches that only depend on the internal parameters of the battery (B. Pattipati 2008; M. H. Lipu 2018). However, the deterioration processes of batteries in hybrid and full-electric buses depend not only on the designing parameters but also on the operating environment and usage patterns of the vehicle. Therefore, utilizing multiple data sources to estimate the health status and EOL of the batteries is of potential internet. In this study, a data-driven prognostic method is developed to estimate SOH and predict EOL for batteries in heterogeneous fleets of hybrid buses, using various types of data sources, e.g. physical configuration of the vehicle, deployment information, on-board sensor readings, and diagnostic fault codes. A set of new features was generated from the existing sensor readings by inducing artificial resets on each battery replacement. A neural network-based regression model achieved accurate estimates of battery SOH status. Another network was used to indicate the EOL of batteries and the result was evaluated using battery replacement based on the current maintenance strategy. © ESREL2020-PSAM15 Organizers. Published by Research Publishing, Singapore.

Place, publisher, year, edition, pages
Singapore: Research Publishing Services, 2020
Keywords
Electric vehicles, Lithium-ion Battery, Predictive Maintenance. References, Remaining Useful Life Prediction, State of Health Estimation
National Category
Other Civil Engineering
Identifiers
urn:nbn:se:hh:diva-51416 (URN)10.3850/978-981-14-8593-0_4515-cd (DOI)2-s2.0-85107295970 (Scopus ID)9789811485930 (ISBN)
Conference
30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020, Venice, Italy, 1-5 November, 2020
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2023-08-16Bibliographically approved
Altarabichi, M. G., Sheikholharam Mashhadi, P., Fan, Y., Pashami, S., Nowaczyk, S., Del Moral, P., . . . Rögnvaldsson, T. (2020). Stacking Ensembles of Heterogenous Classifiers for Fault Detection in Evolving Environments. In: Piero Baraldi; Francesco Di Maio; Enrico Zio (Ed.), Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference: . Paper presented at 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020, Venice, Italy, 1-5 November, 2020 (pp. 1068-1068). Singapore: Research Publishing Services
Open this publication in new window or tab >>Stacking Ensembles of Heterogenous Classifiers for Fault Detection in Evolving Environments
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2020 (English)In: Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference / [ed] Piero Baraldi; Francesco Di Maio; Enrico Zio, Singapore: Research Publishing Services, 2020, p. 1068-1068Conference paper, Published paper (Refereed)
Abstract [en]

Monitoring the condition, detecting faults, and modeling the degradation of industrial equipment are important challenges in Prognostics and Health Management (PHM) field. Our solution to the challenge demonstrated a multi-stage approach for detecting faults in a group of identical industrial equipment, composed of four identical interconnected components, that have been deployed to the evolving environment with changes in operational and environmental conditions. In the first stage, a stacked ensemble of heterogeneous classifiers was applied to predict the state of each component of the equipment individually. In the second stage, a low pass filter was applied to smoothen the predictions cast by stacked ensembles, utilizing temporal information of the prediction sequence. © ESREL2020-PSAM15 Organizers. Published by Research Publishing, Singapore.

Place, publisher, year, edition, pages
Singapore: Research Publishing Services, 2020
Keywords
Fault Detection, Prognostics and Health Management, Stacking Ensembles
National Category
Other Civil Engineering
Identifiers
urn:nbn:se:hh:diva-46741 (URN)10.3850/978-981-14-8593-0_5555-cd (DOI)2-s2.0-85107306479 (Scopus ID)9789811485930 (ISBN)
Conference
30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM15 2020, Venice, Italy, 1-5 November, 2020
Available from: 2022-05-16 Created: 2022-05-16 Last updated: 2023-03-07Bibliographically approved
Nowaczyk, S., Rögnvaldsson, T., Fan, Y. & Calikus, E. (2020). Towards Autonomous Knowledge Creation from Big Data in Smart Cities. In: Juan Carlos Augusto (Ed.), Handbook of Smart Cities: (pp. 1-35). Cham: Springer
Open this publication in new window or tab >>Towards Autonomous Knowledge Creation from Big Data in Smart Cities
2020 (English)In: Handbook of Smart Cities / [ed] Juan Carlos Augusto, Cham: Springer, 2020, p. 1-35Chapter in book (Other academic)
Abstract [en]

The notion of smart cities is inherently connected with the notion of Big Data. It is Big Data that allows more and more intelligence to be added to our existing urban systems. This intelligence then, at least as a goal, is used to serve the needs of the citizens better, making the everyday operations more efficient and adaptive. Many recent successes of supervised machine learning make it an auspicious tool; however, the long-term vision of smart cities clearly requires technology that goes beyond that. The data collected based on the current operation of the system does not in itself contain information about possible improvements. The next generation of smart cities undoubtedly lies with the systems that build towards autonomous and semi-autonomous “knowledge creation.” They can self-improve and adapt to changing conditions and expectations. They must handle situations that were not anticipated during their design. Such construction of knowledge can be illustrated with the Data, Information, Knowledge, and Wisdom hierarchy. It requires collecting and representing the data; creating relevant “events” from this data; generating rules that can combine information from different sources; and finally, the ability to project into the future and reason back into the past. © 2020, Springer Nature Switzerland AG

Place, publisher, year, edition, pages
Cham: Springer, 2020
National Category
Computer Systems
Identifiers
urn:nbn:se:hh:diva-43016 (URN)10.1007/978-3-030-15145-4_38-1 (DOI)978-3-030-15145-4 (ISBN)
Available from: 2020-08-30 Created: 2020-08-30 Last updated: 2020-12-09Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3034-6630

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