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Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. A HMMbased fault detection method for piecewise stationary industrial processes
 Institute of Electrical and Electronics Engineers IEEE: 20th IEEE Conference on Emerging Technologies and Factory Automation, ETFA 2015. Proceedings : September 811, 2015, Luxembourg Piscataway, NJ: IEEE, 2015 ISBN: 9781467379298 ISBN: 9781467379304 S.497502 
 International Conference on Emerging Technologies and Factory Automation (ETFA) <20, 2015, Luxembourg> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer IOSB () 
Abstract
In this paper, fault detection in piecewise stationary industrial processes is investigated. Such processes can be modeled as sequences of distinct system modes in which the respective expectation values and variances of process variables do not change. In particular, piecewise stationary processes with autonomous transitions between system modes are considered in this work, i.e. processes without observable trigger events such as on/off signals. A Hidden Markov Model (HMM) is employed as underlying system model for such processes. System modes are modeled as hidden state variables with given transition probabilities. Continuous process variables are assumed to be Gaussian distributed with constant second order statistics in each system mode. A novel HMMbased fault detection method is proposed which incorporates the Viterbi algorithm into a fault detection method for hybrid industrial processes. Experimental results for the proposed fault detection method are presented for a module of the Lemgo Smart Factory.