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Fan, Y. (2020). Wisdom of the Crowd for Fault Detection and Prognosis. (Doctoral dissertation). Halmstad: Halmstad University Press
Open this publication in new window or tab >>Wisdom of the Crowd for Fault Detection and Prognosis
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Monitoring and maintaining the equipment to ensure its reliability and availability is vital to industrial operations. With the rapid development and growth of interconnected devices, the Internet of Things promotes digitization of industrial assets, to be sensed and controlled across existing networks, enabling access to a vast amount of sensor data that can be used for condition monitoring. However, the traditional way of gaining knowledge and wisdom, by the expert, for designing condition monitoring methods is unfeasible for fully utilizing and digesting this enormous amount of information. It does not scale well to complex systems with a huge amount of components and subsystems. Therefore, a more automated approach that relies on human experts to a lesser degree, being capable of discovering interesting patterns, generating models for estimating the health status of the equipment, supporting maintenance scheduling, and can scale up to many equipment and its subsystems, will provide great benefits for the industry. 

This thesis demonstrates how to utilize the concept of "Wisdom of the Crowd", i.e. a group of similar individuals, for fault detection and prognosis. The approach is built based on an unsupervised deviation detection method, Consensus Self-Organizing Models (COSMO). The method assumes that the majority of a crowd is healthy; individual deviates from the majority are considered as potentially faulty. The COSMO method encodes sensor data into models, and the distances between individual samples and the crowd are measured in the model space. This information, regarding how different an individual performs compared to its peers, is utilized as an indicator for estimating the health status of the equipment. The generality of the COSMO method is demonstrated with three condition monitoring case studies: i) fault detection and failure prediction for a commercial fleet of city buses, ii) prognosis for a fleet of turbofan engines and iii) finding cracks in metallic material. In addition, the flexibility of the COSMO method is demonstrated with: i) being capable of incorporating domain knowledge on specializing relevant expert features; ii) able to detect multiple types of faults with a generic data- representation, i.e. Echo State Network; iii) incorporating expert feedback on adapting reference group candidate under an active learning setting. Last but not least, this thesis demonstrated that the remaining useful life of the equipment can be estimated from the distance to a crowd of peers. 

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2020. p. 87
Series
Halmstad University Dissertations ; 67
National Category
Information Systems
Identifiers
urn:nbn:se:hh:diva-41367 (URN)978-91-88749-43-7 (ISBN)978-91-88749-42-0 (ISBN)
Public defence
2020-01-31, J102 Wigforss, Kristian IV:s väg 3, Halmstad, 13:00 (English)
Opponent
Supervisors
Available from: 2020-01-14 Created: 2020-01-10 Last updated: 2020-01-14Bibliographically approved
David, J., Mostowski, W., Aramrattna, M., Fan, Y., Varshosaz, M., Karlsson, P., . . . Andersson, E. (2019). Design and Development of a Hexacopter for the Search and Rescue of a Lost Drone. In: : . Paper presented at IROS 2019 - Workshop on Challenges in Vision-based Drones Navigation, Macau, China, November 8, 2019.
Open this publication in new window or tab >>Design and Development of a Hexacopter for the Search and Rescue of a Lost Drone
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Search and rescue with an autonomous robot is an attractive and challenging task within the research community. This paper presents the development of an autonomous hexacopter that is designed for retrieving a lost object, like a drone, from a vast-open space, like a desert area. Navigating its path with a proposed coverage path planning strategy, the hexacopter can efficiently search for a lost target and locate it using an image-based object detection algorithm. Moreover, after the target is located, our hexacopter can grasp it with a customised gripper and transport it back to a destined location. It is also capable of avoiding static obstacles and dynamic objects. The proposed system was realised in simulations before implementing it in a real hardware setup, i.e. assembly of the drone, crafting of the gripper, software implementation and testing under real-world scenarios. The designed hexacopter won the best UAV design award at the CPS-VO 2018 Competition held in Arizona, USA.

Keywords
drones, UAV, competition, search and rescue
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-40830 (URN)
Conference
IROS 2019 - Workshop on Challenges in Vision-based Drones Navigation, Macau, China, November 8, 2019
Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2019-11-05
Teng, X., Zhang, X., Fan, Y. & Zhang, D. (2019). Evaluation of Cracks in Metallic Material Using a Self-Organized Data-Driven Model of Acoustic Echo-Signal. Applied Sciences: APPS, 9(1), Article ID 95.
Open this publication in new window or tab >>Evaluation of Cracks in Metallic Material Using a Self-Organized Data-Driven Model of Acoustic Echo-Signal
2019 (English)In: Applied Sciences: APPS, ISSN 1454-5101, E-ISSN 1454-5101, Vol. 9, no 1, article id 95Article in journal (Refereed) Published
Abstract [en]

Non-linear acoustic technique is an attractive approach in evaluating early fatigue as well as cracks in material. However, its accuracy is greatly restricted by external non-linearities of ultra-sonic measurement systems. In this work, an acoustical data-driven deviation detection method, called the consensus self-organizing models (COSMO) based on statistical probability models, was introduced to study the evolution of localized crack growth. By using pitch-catch technique, frequency spectra of acoustic echoes collected from different locations of a specimen were compared, resulting in a Hellinger distance matrix to construct statistical parameters such as z-score, p-value and T-value. It is shown that statistical significance p-value of COSMO method has a strong relationship with the crack growth. Particularly, T-values, logarithm transformed p-value, increases proportionally with the growth of cracks, which thus can be applied to locate the position of cracks and monitor the deterioration of materials. © 2018 by the authors. 

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2019
Keywords
crack growth, acoustic echo, COSMO, p-value
National Category
Applied Mechanics
Identifiers
urn:nbn:se:hh:diva-39445 (URN)10.3390/app9010095 (DOI)000456579300095 ()2-s2.0-85059353615 (Scopus ID)
Note

Financiers: National Natural Science Foundation of China, QingLan Project & The Fundamental Research Funds for the Central Universities.

Available from: 2019-05-22 Created: 2019-05-22 Last updated: 2020-01-10Bibliographically approved
Calikus, E., Fan, Y., Nowaczyk, S. & Pinheiro Sant'Anna, A. (2019). Interactive-cosmo: Consensus self-organized models for fault detection with expert feedback. 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, Melbourne, Australia; 15 February, 2019 (pp. 1-9). New York: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Interactive-cosmo: Consensus self-organized models for fault detection with expert feedback
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, New York: Association for Computing Machinery (ACM), 2019, p. 1-9Conference paper, Published paper (Refereed)
Abstract [en]

Diagnosing deviations and predicting faults is an important task, especially given recent advances related to Internet of Things. However, the majority of the efforts for diagnostics are still carried out by human experts in a time-consuming and expensive manner. One promising approach towards self-monitoring systems is based on the "wisdom of the crowd" idea, where malfunctioning equipments are detected by understanding the similarities and differences in the operation of several alike systems.

A fully autonomous fault detection, however, is not possible, since not all deviations or anomalies correspond to faulty behaviors; many can be explained by atypical usage or varying external conditions. In this work, we propose a method which gradually incorporates expert-provided feedback for more accurate self-monitoring. Our idea is to support model adaptation while allowing human feedback to persist over changes in data distribution, such as concept drift. © 2019 Association for Computing Machinery.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2019
Keywords
Anomaly Detection, Self-Monitoring, Active Learning, Human-in- the-loop
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-41365 (URN)10.1145/3304079.3310289 (DOI)2-s2.0-85069779014 (Scopus ID)978-1-4503-6296-2 (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, Melbourne, Australia; 15 February, 2019
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-14Bibliographically 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
Nowaczyk, S., Pinheiro Sant'Anna, A., Calikus, E. & Fan, Y. (2018). Monitoring equipment operation through model and event discovery. In: Hujun Yin, David Camacho Paulo Novais & Antonio J. Tallón-Ballesteros (Ed.), Intelligent Data Engineering and Automated Learning – IDEAL 2018: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II. Paper presented at Intelligent Data Engineering and Automated Learning – IDEAL 2018, 19th International Conference, Madrid, Spain, November 21–23, 2018 (pp. 41-53). Cham: Springer, 11315
Open this publication in new window or tab >>Monitoring equipment operation through model and event discovery
2018 (English)In: Intelligent Data Engineering and Automated Learning – IDEAL 2018: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part II / [ed] Hujun Yin, David Camacho Paulo Novais & Antonio J. Tallón-Ballesteros, Cham: Springer, 2018, Vol. 11315, p. 41-53Conference paper, Published paper (Refereed)
Abstract [en]

Monitoring the operation of complex systems in real-time is becoming both required and enabled by current IoT solutions. Predicting faults and optimising productivity requires autonomous methods that work without extensive human supervision. One way to automatically detect deviating operation is to identify groups of peers, or similar systems, and evaluate how well each individual conforms with the group. We propose a monitoring approach that can construct knowledge more autonomously and relies on human experts to a lesser degree: without requiring the designer to think of all possible faults beforehand; able to do the best possible with signals that are already available, without the need for dedicated new sensors; scaling up to “one more system and component” and multiple variants; and finally, one that will adapt to changes over time and remain relevant throughout the lifetime of the system. © Springer Nature Switzerland AG 2018.

Place, publisher, year, edition, pages
Cham: Springer, 2018
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 11315
Keywords
Artificial intelligence, Computer science, Computers, Event discoveries, Human expert, Human supervision, Monitoring approach, Monitoring equipment, Multiple variants, Real time, Scaling-up, Real time systems
National Category
Embedded Systems
Identifiers
urn:nbn:se:hh:diva-38732 (URN)10.1007/978-3-030-03496-2_6 (DOI)2-s2.0-85057087564 (Scopus ID)9783030034955 (ISBN)978-3-030-03496-2 (ISBN)
Conference
Intelligent Data Engineering and Automated Learning – IDEAL 2018, 19th International Conference, Madrid, Spain, November 21–23, 2018
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved
Cooney, M., Pashami, S., Pinheiro Sant'Anna, A., Fan, Y. & Nowaczyk, S. (2018). Pitfalls of Affective Computing: How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks?. In: WWW '18 Companion Proceedings of the The Web Conference 2018: . Paper presented at The Web Conference 2018 (WWW '18), Lyon, France, April 23-27, 2018 (pp. 1563-1566). New York, NY: ACM Publications
Open this publication in new window or tab >>Pitfalls of Affective Computing: How can the automatic visual communication of emotions lead to harm, and what can be done to mitigate such risks?
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2018 (English)In: WWW '18 Companion Proceedings of the The Web Conference 2018, New York, NY: ACM Publications, 2018, p. 1563-1566Conference paper, Published paper (Refereed)
Abstract [en]

What would happen in a world where people could "see'' others' hidden emotions directly through some visualizing technology Would lies become uncommon and would we understand each other better Or to the contrary, would such forced honesty make it impossible for a society to exist The science fiction television show Black Mirror has exposed a number of darker scenarios in which such futuristic technologies, by blurring the lines of what is private and what is not, could also catalyze suffering. Thus, the current paper first turns an eye towards identifying some potential pitfalls in emotion visualization which could lead to psychological or physical harm, miscommunication, and disempowerment. Then, some countermeasures are proposed and discussed--including some level of control over what is visualized and provision of suitably rich emotional information comprising intentions--toward facilitating a future in which emotion visualization could contribute toward people's well-being. The scenarios presented here are not limited to web technologies, since one typically thinks about emotion recognition primarily in the context of direct contact. However, as interfaces develop beyond today's keyboard and monitor, more information becomes available also at a distance--for example, speech-to-text software could evolve to annotate any dictated text with a speaker's emotional state.

Place, publisher, year, edition, pages
New York, NY: ACM Publications, 2018
Keywords
Affective computing, emotion visualization, Black Mirror, privacy, ethics, intention recognition
National Category
Robotics
Identifiers
urn:nbn:se:hh:diva-37664 (URN)10.1145/3184558.3191611 (DOI)
Conference
The Web Conference 2018 (WWW '18), Lyon, France, April 23-27, 2018
Projects
CAISR 2010/0271
Funder
Knowledge Foundation, CAISR 2010/0271
Note

Funding: Swedish Knowledge Foundation (CAISR 2010/0271 and Sidus AIR no. 20140220)

Available from: 2018-07-25 Created: 2018-07-25 Last updated: 2019-04-12Bibliographically approved
Svensson, O., Thelin, S., Byttner, S. & Fan, Y. (2017). Indirect Tire Monitoring System - Machine Learning Approach. In: IOP Conference Series: Materials Science and Engineering: . Paper presented at 11th International Congress of Automotive and Transport Engineering: Mobility Engineering and Environment (CAR 2017), Pitesti, Romania, 8-10 November, 2017. Bristol: Institute of Physics Publishing (IOPP), 252, Article ID 012018.
Open this publication in new window or tab >>Indirect Tire Monitoring System - Machine Learning Approach
2017 (English)In: IOP Conference Series: Materials Science and Engineering, Bristol: Institute of Physics Publishing (IOPP), 2017, Vol. 252, article id 012018Conference paper, Published paper (Refereed)
Abstract [en]

The heavy vehicle industry has today no requirement to provide a tire pressure monitoring system by law. This has created issues surrounding unknown tire pressure and thread depth during active service. There is also no standardization for these kind of systems which means that different manufacturers and third party solutions work after their own principles and it can be hard to know what works for a given vehicle type. The objective is to create an indirect tire monitoring system that can generalize a method that detect both incorrect tire pressure and thread depth for different type of vehicles within a fleet without the need for additional physical sensors or vehicle specific parameters. The existing sensors that are connected communicate through CAN and are interpreted by the Drivec Bridge hardware that exist in the fleet. By using supervised machine learning a classifier was created for each axle where the main focus was the front axle which had the most issues. The classifier will classify the vehicles tires condition and will be implemented in Drivecs cloud service where it will receive its data. The resulting classifier is a random forest implemented in Python. The result from the front axle with a data set consisting of 9767 samples of buses with correct tire condition and 1909 samples of buses with incorrect tire condition it has an accuracy of 90.54% (0.96%). The data sets are created from 34 unique measurements from buses between January and May 2017. This classifier has been exported and is used inside a Node.js module created for Drivecs cloud service which is the result of the whole implementation. The developed solution is called Indirect Tire Monitoring System (ITMS) and is seen as a process. This process will predict bad classes in the cloud which will lead to warnings. The warnings are defined as incidents. They contain only the information needed and the bandwidth of the incidents are also controlled so incidents are created within an acceptable range over a period of time. These incidents will be notified through the cloud for the operator to analyze for upcoming maintenance decisions. © 2017 Published under licence by IOP Publishing Ltd.

Place, publisher, year, edition, pages
Bristol: Institute of Physics Publishing (IOPP), 2017
Series
IOP Conference Series: Materials Science and Engineering, ISSN 1757-8981, E-ISSN 1757-899X ; 252
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-35499 (URN)10.1088/1757-899X/252/1/012018 (DOI)2-s2.0-85034218557 (Scopus ID)
Conference
11th International Congress of Automotive and Transport Engineering: Mobility Engineering and Environment (CAR 2017), Pitesti, Romania, 8-10 November, 2017
Available from: 2017-11-29 Created: 2017-11-29 Last updated: 2017-12-11Bibliographically approved
Fan, Y. (2016). A Self-Organized Fault Detection Method for Vehicle Fleets. (Licentiate dissertation). Halmstad: Halmstad University Press
Open this publication in new window or tab >>A Self-Organized Fault Detection Method for Vehicle Fleets
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

A fleet of commercial heavy-duty vehicles is a very interesting application arena for fault detection and predictive maintenance. With a highly digitized electronic system and hundreds of sensors mounted on-board a modern bus, a huge amount of data is generated from daily operations.

This thesis and appended papers present a study of an autonomous framework for fault detection, using the data gathered from the regular operation of vehicles. We employed an unsupervised deviation detection method, called Consensus Self-Organising Models (COSMO), which is based on the concept of ‘wisdom of the crowd’. It assumes that the majority of the group is ‘healthy’; by comparing individual units within the group, deviations from the majority can be considered as potentially ‘faulty’. Information regarding detected anomalies can be utilized to prevent unplanned stops.

This thesis demonstrates how knowledge useful for detecting faults and predicting failures can be autonomously generated based on the COSMO method, using different generic data representations. The case study in this work focuses on vehicle air system problems of a commercial fleet of city buses. We propose an approach to evaluate the COSMO method and show that it is capable of detecting various faults and indicates upcoming air compressor failures. A comparison of the proposed method with an expert knowledge based system shows that both methods perform equally well. The thesis also analyses the usage and potential benefits of using the Echo State Network as a generic data representation for the COSMO method and demonstrates the capability of Echo State Network to capture interesting characteristics in detecting different types of faults.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2016. p. 116
Series
Halmstad University Dissertations ; 27
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-32489 (URN)978-91-87045-57-8 (ISBN)978-91-87045-56-1 (ISBN)
Presentation
2016-12-16, Halda, Kristian IV:s väg 3, 301 18 Halmstad, Halmstad, 10:00 (English)
Opponent
Supervisors
Projects
In4Uptime
Funder
VINNOVA
Available from: 2016-11-28 Created: 2016-11-25 Last updated: 2016-11-28Bibliographically approved
Teng, X., Fan, Y. & Nowaczyk, S. (2016). Evaluation of Micro-flaws in Metallic Material Based on A Self-Organized Data-driven Approach. In: 2016 IEEE International Conference on Prognostics and Health Management (ICPHM): . Paper presented at 2016 IEEE International Conference on Prognostics and Health Management, Carleton University, Ottawa, ON, Canada, June 20-22, 2016. IEEE conference proceedings
Open this publication in new window or tab >>Evaluation of Micro-flaws in Metallic Material Based on A Self-Organized Data-driven Approach
2016 (English)In: 2016 IEEE International Conference on Prognostics and Health Management (ICPHM), IEEE conference proceedings, 2016Conference paper, Poster (with or without abstract) (Refereed)
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

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016
Keywords
Non-destructive testing, ultrasonic, micro-defects
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:hh:diva-31646 (URN)10.1109/ICPHM.2016.7542868 (DOI)000390707700055 ()2-s2.0-84986003675 (Scopus ID)978-1-5090-0382-2 (ISBN)
Conference
2016 IEEE International Conference on Prognostics and Health Management, Carleton University, Ottawa, ON, Canada, June 20-22, 2016
Available from: 2016-07-14 Created: 2016-07-14 Last updated: 2017-12-12Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3034-6630

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