TY - CONF AB - Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge. AU - Kimotho, James Kuria AU - Meyer, Tobias AU - Sextro, Walter ID - 9879 KW - ageing KW - particle filtering (numerical methods) KW - proton exchange membrane fuel cells KW - remaining life assessment KW - PEM fuel cell prognostics KW - PHM KW - RUL predictions KW - accelerated degradation KW - adaptive particle filter algorithm KW - dynamic loading KW - model parameter adaptation KW - prognostics and health management KW - proton exchange membrane fuel cells KW - remaining useful life estimation KW - self-healing effect KW - Adaptation models KW - Data models KW - Degradation KW - Estimation KW - Fuel cells KW - Mathematical model KW - Prognostics and health management T2 - Prognostics and Health Management (PHM), 2014 IEEE Conference on TI - PEM fuel cell prognostics using particle filter with model parameter adaptation ER - TY - CONF AB - This contribution describes a step-wise source counting algorithm to determine the number of speakers in an offline scenario. Each speaker is identified by a variational expectation maximization (VEM) algorithm for complex Watson mixture models and therefore directly yields beamforming vectors for a subsequent speech separation process. An observation selection criterion is proposed which improves the robustness of the source counting in noise. The algorithm is compared to an alternative VEM approach with Gaussian mixture models based on directions of arrival and shown to deliver improved source counting accuracy. The article concludes by extending the offline algorithm towards a low-latency online estimation of the number of active sources from the streaming input data. AU - Drude, Lukas AU - Chinaev, Aleksej AU - Tran Vu, Dang Hai AU - Haeb-Umbach, Reinhold ID - 11753 KW - Accuracy KW - Acoustics KW - Estimation KW - Mathematical model KW - Soruce separation KW - Speech KW - Vectors KW - Bayes methods KW - Blind source separation KW - Directional statistics KW - Number of speakers KW - Speaker diarization T2 - 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014) TI - Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models ER - TY - CONF AB - Today we can identify a big gap between requirement specification and the generation of test environments. This article extends the Classification Tree Method for Embedded Systems (CTM/ES) to fill this gap by new concepts for the precise specification of stimuli for operational ranges of continuous control systems. It introduces novel means for continuous acceptance criteria definition and for functional coverage definition. AU - Krupp, Alexander AU - Müller, Wolfgang ID - 37037 KW - System testing KW - Automatic testing KW - Object oriented modeling KW - Classification tree analysis KW - Automotive engineering KW - Mathematical model KW - Embedded system KW - Control systems KW - Electronic equipment testing KW - Software testing T2 - Proceedings of DATE’10 TI - A Systematic Approach to Combined HW/SW System Test ER -