]> 2026-04-04T17:14:07+02:00 Publications:Wisdom of the Crowd for Fault Detection and Prognosis 500 en <p>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. </p><p>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. </p> http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:1384812 PhD Thesis Halmstad University Dissertations ; 67 Wisdom of the Crowd for Fault Detection and Prognosis 2020 2020-01-14T21:23:12Z 2458863.3911111 Wisdom of the Crowd for Fault Detection and Prognosis 500 0 plainlist list 1 [[:Publications:Wisdom of the Crowd for Fault Detection and Prognosis]] Wisdom of the Crowd for Fault Detection and Prognosis# QUERYed08a521cd85463c9eed64879e4ecb0f 500 [2,"smw-datavalue-property-restricted-annotation-use","Publisher"] Wisdom of the Crowd for Fault Detection and Prognosis# ERR6c155b5d70b69ddb590b4f2e73389c2a 500 [2,"smw-datavalue-wikipage-property-invalid-title","Supervisors","R\u00f6gnvaldsson, Thorsteinn (denni) (H\u00f6gskolan i Halmstad (2804), Akademin f\u00f6r informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), ;;CAISR Centrum f\u00f6r till\u00e4mpade intelligenta system (IS-lab) (13650))Nowaczyk, S\u0142awomir (slanow) (H\u00f6gskolan i Halmstad (2804), Akademin f\u00f6r informationsteknologi (16904), Halmstad Embedded and Intelligent Systems Research (EIS) (3938), ;;CAISR Centrum f\u00f6r till\u00e4mpade intelligenta system (IS-lab) (13650))"] Wisdom of the Crowd for Fault Detection and Prognosis# ERR7bf7a32fd5dfdd1bdbd08a2360b6c72f 500 [2,"smw-datavalue-property-restricted-annotation-use","Author"] Wisdom of the Crowd for Fault Detection and Prognosis# ERRe24f17e090c98448f3de99d03182b7b6