0444754 20240103210154.820150625235959.9 8 s. C cav_un_epca*0444753978-1-5108-0712-9Oxford, UKThe British Institute of Non-Destructive Testing2015 condition monitoring probabilistic logic binomial opinion multimodally distributed signal Gaussian mixture cav_un_auth*0212695 Ettler P. CZ cav_un_auth*0212696 Puchr I. CZ cav_un_auth*0101119 Jirsa Ladislav Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101175 Pavelková Lenka Adaptivní systémy Department of Adaptive Systems AS AS UTIA-B Department of Adaptive Systems Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2015/AS/jirsa-0444754.pdf 7D12004 GA MŠk cav_un_auth*0291242 Condition monitoring of complex systems starts from an inspection of single measured signals. Results of health evaluation for each signal then enter the monitoring framework which uses a special type of probabilistic logic to assess condition of the whole inspected system while respecting its internal structure. A distribution of the involved signal can be multimodal in general. Therefore, a Gaussian mixture is more suitable for its approximation than a single theoretical distribution. The mixture estimated from a sufficiently long record of a particular signal can be used not only for its prediction but also for evaluation of the signal health. However, finding the values in a low probability region does not necessarily indicate bad condition of the signal – it can be interpreted as an increase in the information uncertainty instead. This approach is being tested on industrial data within the running project which aims to develop the novel probabilistic condition monitoring system. cav_un_auth*0317468 The 12th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2015/MFPT 2015) Oxford 09.06.2015-11.06.2015 GB 2016 BC 4 PR RVO:67985556 http://hdl.handle.net/11104/0247505 1 cav_un_auth*0317469 COM COMPUREG Plzeň, s.r.o. CZ S 2015 cav_un_epca*0444753 The 12th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies (CM 2015/MFPT 2015) 978-1-5108-0712-9 Oxford, UK The British Institute of Non-Destructive Testing 2015