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Wisdom of the Crowd for Fault Detection and Prognosis
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3034-6630
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Description
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: urn:nbn:se:hh:diva-41367ISBN: 978-91-88749-43-7 (electronic)ISBN: 978-91-88749-42-0 (print)OAI: oai:DiVA.org:hh-41367DiVA, id: diva2:1384812
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
List of papers
1. Evaluation of Cracks in Metallic Material Using a Self-Organized Data-Driven Model of Acoustic Echo-Signal
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
2. Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet
Open this publication in new window or tab >>Incorporating Expert Knowledge into a Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet
2015 (English)In: Frontiers in Artificial Intelligence and Applications, ISSN 0922-6389, E-ISSN 1879-8314, Vol. 278, p. 58-67Article in journal (Refereed) Published
Abstract [en]

In the automotive industry, cost effective methods for predictive maintenance are increasingly in demand. The traditional approach for developing diagnostic methods on commercial vehicles is heavily based on knowledge of human experts, and thus it does not scale well to modern vehicles with many components and subsystems. In previous work we have presented a generic self-organising approach called COSMO that can detect, in an unsupervised manner, many different faults. In a study based on a commercial fleet of 19 buses operating in Kungsbacka, we have been able to predict, for example, fifty percent of the compressors that break down on the road, in many cases weeks before the failure.

In this paper we compare those results with a state of the art approach currently used in the industry, and we investigate how features suggested by experts for detecting compressor failures can be incorporated into the COSMO method. We perform several experiments, using both real and synthetic data, to identify issues that need to be considered to improve the accuracy. The final results show that the COSMO method outperforms the expert method.

Place, publisher, year, edition, pages
Amsterdam: IOS Press, 2015
Keywords
Vehicle diagnostics, Predictive maintenance, Fault detection, Receiver Operating Characteristic curve, Expert knowledge
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:hh:diva-29809 (URN)10.3233/978-1-61499-589-0-58 (DOI)2-s2.0-84963636151 (Scopus ID)
Conference
The 13th Scandinavian Conference on Artificial Intelligence (SCAI), Halmstad University, Halmstad, Sweden, 5-6 November, 2015
Projects
In4Uptime
Funder
VINNOVAKnowledge Foundation
Note

ISBN: 978-1-61499-588-3 (print) | 978-1-61499-589-0 (online)

Editor: Sławomir Nowaczyk

Available from: 2015-11-24 Created: 2015-11-24 Last updated: 2020-01-10Bibliographically approved
3. Predicting Air Compressor Failures with Echo State Networks
Open this publication in new window or tab >>Predicting Air Compressor Failures with Echo State Networks
2016 (English)In: PHME 2016: Proceedings of the Third European Conference of the Prognostics and Health Management Society 2016 / [ed] Ioana Eballard, Anibal Bregon, PHM Society , 2016, p. 568-578Conference paper, Published paper (Refereed)
Abstract [en]

Modern vehicles have increasing amounts of data streaming continuously on-board their controller area networks. These data are primarily used for controlling the vehicle and for feedback to the driver, but they can also be exploited to detect faults and predict failures. The traditional diagnostics paradigm, which relies heavily on human expert knowledge, scales poorly with the increasing amounts of data generated by highly digitised systems. The next generation of equipment monitoring and maintenance prediction solutions will therefore require a different approach, where systems can build up knowledge (semi-)autonomously and learn over the lifetime of the equipment.

A key feature in such systems is the ability to capture and encode characteristics of signals, or groups of signals, on-board vehicles using different models. Methods that do this robustly and reliably can be used to describe and compare the operation of the vehicle to previous time periods or to other similar vehicles. In this paper two models for doing this, for a single signal, are presented and compared on a case of on-road failures caused by air compressor faults in city buses. One approach is based on histograms and the other is based on echo state networks. It is shown that both methods are sensitive to the expected changes in the signal's characteristics and work well on simulated data. However, the histogram model, despite being simpler, handles the deviations in real data better than the echo state network.

Place, publisher, year, edition, pages
PHM Society, 2016
Keywords
predictive maintenance, fault detection, Vehicle diagnostics, reservoir model, echo state network
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:hh:diva-31644 (URN)978-1-936263-21-9 (ISBN)
Conference
Third European Conference of the Prognostics and Health Management Society 2016, Bilbao, Spain, 5-8 July, 2016
Projects
In4Uptime
Funder
VINNOVA
Available from: 2016-07-14 Created: 2016-07-14 Last updated: 2020-01-10Bibliographically approved
4. Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet
Open this publication in new window or tab >>Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet
2015 (English)In: Procedia Computer Science, ISSN 1877-0509, E-ISSN 1877-0509, Vol. 53, p. 447-456Article in journal (Refereed) Published
Abstract [en]

Managing the maintenance of a commercial vehicle fleet is an attractive application domain of ubiquitous knowledge discovery. Cost effective methods for predictive maintenance are progressively demanded in the automotive industry. The traditional diagnostic paradigm that requires human experts to define models is not scalable to today's vehicles with hundreds of computing units and thousands of control and sensor signals streaming through the on-board controller area network. A more autonomous approach must be developed. In this paper we evaluate the performance of the COSMO approach for automatic detection of air pressure related faults on a fleet of city buses. The method is both generic and robust. Histograms of a single pressure signal are collected and compared across the fleet and deviations are matched against workshop maintenance and repair records. It is shown that the method can detect several of the cases when compressors fail on the road, well before the failure. The work is based on data from a three year long field study involving 19 buses operating in and around a city on the west coast of Sweden. © The Authors. Published by Elsevier B.V.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2015
Keywords
Vehicle diagnostics, predictive maintenance, fault detection, self-organizing systems
National Category
Signal Processing Information Systems
Identifiers
urn:nbn:se:hh:diva-29240 (URN)10.1016/j.procs.2015.07.322 (DOI)000360311000051 ()2-s2.0-84939156791 (Scopus ID)
Conference
INNS Conference on Big Data, San Francisco, CA, USA, 8-10 August, 2015
Projects
In4Uptime
Funder
VINNOVA
Available from: 2015-08-19 Created: 2015-08-19 Last updated: 2020-01-10Bibliographically approved
5. Transfer learning for remaining useful life prediction based on consensus self-organizing models
Open this publication in new window or tab >>Transfer learning for remaining useful life prediction based on consensus self-organizing models
(English)Manuscript (preprint) (Other academic)
Abstract [en]

The traditional paradigm for developing machine prognostics usually relies on generalization from data acquired in experiments under controlled conditions prior to deployment of the equipment. Detecting or predicting failures and estimating machine health in this way assumes that future field data will have a very similar distribution to the experiment data. However, many complex machines operate under dynamic environmental conditions and are used in many different ways. This makes collecting comprehensive data very challenging, and the assumption that pre-deployment data and post-deployment data follow very similar distributions is unlikely to hold. Transfer Learning (TL) refers to methods for transferring knowledge learned in one setting (the source domain) to another setting (the target domain). In this work, we present a TL method for predicting Remaining Useful Life (RUL) of equipment, under the assumption that labels are available only for the source domain and not the target domain. This setting corresponds to generalizing from a limited number of run-to-failure experiments performed prior to deployment into making prognostics with data coming from deployed equipment that is being used under multiple new operating conditions and experiencing previously unseen faults. We employ a deviation detection method, Consensus Self-Organizing Models (COSMO), to create transferable features for building the RUL regression model. These features capture how different target equipment is in comparison to its peers. The efficiency of the proposed TL method is demonstrated using the NASA Turbofan Engine Degradation Simulation Data Set. Models using the COSMO transferable features show better performance than other methods on predicting RUL when the target domain is more complex than the source domain.

Keywords
Transfer Learning, Feature-Representation Transfer, Domain Adaptation, Remaining Useful Life Prediction, Consensus Self-Organising Models
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-41364 (URN)
Funder
Vinnova, 201703073
Note

Som manuskript i avhandling / As manuscript in thesis

Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-15
6. Interactive-cosmo: Consensus self-organized models for fault detection with expert feedback
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
7. Predicting Air Compressor Failures Using Long Short Term Memory Networks
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

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