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Evaluation of Cracks in Metallic Material Using a Self-Organized Data-Driven Model of Acoustic Echo-Signal
Key Laboratory of Modern Acoustics, Ministry of Education, Institute of Acoustics, Nanjing University, Nanjing, China & School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China.
Nanjing Manse Acoustics Technology Co. Ltd., Nanjing, China.
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
Key Laboratory of Modern Acoustics, Ministry of Education, Institute of Acoustics, Nanjing University, Nanjing, China.
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. Vol. 9, no 1, article id 95
Keywords [en]
crack growth, acoustic echo, COSMO, p-value
National Category
Applied Mechanics
Identifiers
URN: urn:nbn:se:hh:diva-39445DOI: 10.3390/app9010095ISI: 000456579300095Scopus ID: 2-s2.0-85059353615OAI: oai:DiVA.org:hh-39445DiVA, id: diva2:1317222
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
In thesis
1. Wisdom of the Crowd for Fault Detection and Prognosis
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

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