[{"quality_controlled":"1","issue":"10","year":"2021","date_created":"2021-09-27T07:07:58Z","title":"A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions","publication":"Machines","abstract":[{"lang":"eng","text":"<jats:p>While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case.</jats:p>"}],"keyword":["prognostics","RUL predictions","particle filter","uncertainty consideration","Multi-Model-Particle Filter","model-based approach","rubber-metal-elements","predictive maintenance"],"language":[{"iso":"eng"}],"publication_status":"published","publication_identifier":{"issn":["2075-1702"]},"citation":{"bibtex":"@article{Bender_2021, title={A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions}, volume={9}, DOI={<a href=\"https://doi.org/10.3390/machines9100210\">10.3390/machines9100210</a>}, number={10210}, journal={Machines}, author={Bender, Amelie}, year={2021} }","mla":"Bender, Amelie. “A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions.” <i>Machines</i>, vol. 9, no. 10, 210, 2021, doi:<a href=\"https://doi.org/10.3390/machines9100210\">10.3390/machines9100210</a>.","short":"A. Bender, Machines 9 (2021).","apa":"Bender, A. (2021). A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions. <i>Machines</i>, <i>9</i>(10), Article 210. <a href=\"https://doi.org/10.3390/machines9100210\">https://doi.org/10.3390/machines9100210</a>","ieee":"A. Bender, “A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions,” <i>Machines</i>, vol. 9, no. 10, Art. no. 210, 2021, doi: <a href=\"https://doi.org/10.3390/machines9100210\">10.3390/machines9100210</a>.","chicago":"Bender, Amelie. “A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions.” <i>Machines</i> 9, no. 10 (2021). <a href=\"https://doi.org/10.3390/machines9100210\">https://doi.org/10.3390/machines9100210</a>.","ama":"Bender A. A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions. <i>Machines</i>. 2021;9(10). doi:<a href=\"https://doi.org/10.3390/machines9100210\">10.3390/machines9100210</a>"},"intvolume":"         9","date_updated":"2022-11-03T11:42:46Z","oa":"1","author":[{"last_name":"Bender","full_name":"Bender, Amelie","id":"54290","first_name":"Amelie"}],"volume":9,"main_file_link":[{"open_access":"1","url":"https://www.mdpi.com/2075-1702/9/10/210"}],"doi":"10.3390/machines9100210","type":"journal_article","status":"public","_id":"25046","user_id":"54290","department":[{"_id":"151"}],"article_number":"210","article_type":"original"},{"year":"2014","page":"1-6","citation":{"chicago":"Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” In <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>, 1–6, 2014. <a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">https://doi.org/10.1109/ICPHM.2014.7036406</a>.","ieee":"J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle filter with model parameter adaptation,” in <i>Prognostics and Health Management (PHM), 2014 IEEE Conference on</i>, 2014, pp. 1–6.","ama":"Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter with model parameter adaptation. In: <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>. ; 2014:1-6. doi:<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>","short":"J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management (PHM), 2014 IEEE Conference On, 2014, pp. 1–6.","mla":"Kimotho, James Kuria, et al. “PEM Fuel Cell Prognostics Using Particle Filter with Model Parameter Adaptation.” <i>Prognostics and Health Management (PHM), 2014 IEEE Conference On</i>, 2014, pp. 1–6, doi:<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>.","bibtex":"@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics using particle filter with model parameter adaptation}, DOI={<a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">10.1109/ICPHM.2014.7036406</a>}, booktitle={Prognostics and Health Management (PHM), 2014 IEEE Conference on}, author={Kimotho, James Kuria  and Meyer, Tobias and Sextro, Walter}, year={2014}, pages={1–6} }","apa":"Kimotho, J. K., Meyer, T., &#38; Sextro, W. (2014). PEM fuel cell prognostics using particle filter with model parameter adaptation. In <i>Prognostics and Health Management (PHM), 2014 IEEE Conference on</i> (pp. 1–6). <a href=\"https://doi.org/10.1109/ICPHM.2014.7036406\">https://doi.org/10.1109/ICPHM.2014.7036406</a>"},"date_updated":"2019-05-20T13:12:27Z","date_created":"2019-05-20T13:11:02Z","author":[{"first_name":"James Kuria ","full_name":"Kimotho, James Kuria ","last_name":"Kimotho"},{"first_name":"Tobias","full_name":"Meyer, Tobias","last_name":"Meyer"},{"first_name":"Walter","id":"21220","full_name":"Sextro, Walter","last_name":"Sextro"}],"title":"PEM fuel cell prognostics using particle filter with model parameter adaptation","doi":"10.1109/ICPHM.2014.7036406","publication":"Prognostics and Health Management (PHM), 2014 IEEE Conference on","type":"conference","abstract":[{"lang":"eng","text":"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."}],"status":"public","_id":"9879","department":[{"_id":"151"}],"user_id":"55222","keyword":["ageing","particle filtering (numerical methods)","proton exchange membrane fuel cells","remaining life assessment","PEM fuel cell prognostics","PHM","RUL predictions","accelerated degradation","adaptive particle filter algorithm","dynamic loading","model parameter adaptation","prognostics and health management","proton exchange membrane fuel cells","remaining useful life estimation","self-healing effect","Adaptation models","Data models","Degradation","Estimation","Fuel cells","Mathematical model","Prognostics and health management"],"language":[{"iso":"eng"}]},{"user_id":"44006","department":[{"_id":"54"}],"_id":"11943","language":[{"iso":"eng"}],"keyword":["clean speech training data","iterative methods","iterative speech enhancement","Kalman filter","Kalman filters","Kalman-LM-iterative algorithm","line spectral pair parameters","log-spectral distance","marginalized particle filter","noise level","nonlinear dynamic state speech model","particle filtering (numerical methods)","single channel speech enhancement","SNR gains","speech enhancement","speech samples"],"type":"conference","publication":"IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)","status":"public","abstract":[{"text":"A marginalized particle filter is proposed for performing single channel speech enhancement with a non-linear dynamic state model. The system consists of a particle filter for tracking line spectral pair (LSP) parameters and a Kalman filter per particle for speech enhancement. The state model for the LSPs has been learnt on clean speech training data. In our approach parameters and speech samples are processed at different time scales by assuming the parameters to be constant for small blocks of data. Further enhancement is obtained by an iteration which can be applied on these small blocks. The experiments show that similar SNR gains are obtained as with the Kalman-LM-iterative algorithm. However better values of the noise level and the log-spectral distance are achieved","lang":"eng"}],"author":[{"first_name":"Stefan","last_name":"Windmann","full_name":"Windmann, Stefan"},{"full_name":"Haeb-Umbach, Reinhold","id":"242","last_name":"Haeb-Umbach","first_name":"Reinhold"}],"date_created":"2019-07-12T05:31:15Z","volume":1,"date_updated":"2022-01-06T06:51:12Z","oa":"1","main_file_link":[{"open_access":"1","url":"https://groups.uni-paderborn.de/nt/pubs/2006/WiHa06-2.pdf"}],"doi":"10.1109/ICASSP.2006.1660058","title":"Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters","citation":{"apa":"Windmann, S., &#38; Haeb-Umbach, R. (2006). Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters. In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i> (Vol. 1, p. I). <a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">https://doi.org/10.1109/ICASSP.2006.1660058</a>","bibtex":"@inproceedings{Windmann_Haeb-Umbach_2006, title={Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters}, volume={1}, DOI={<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>}, booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2006}, pages={I} }","mla":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, vol. 1, 2006, p. I, doi:<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>.","short":"S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, p. I.","ieee":"S. Windmann and R. Haeb-Umbach, “Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters,” in <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 2006, vol. 1, p. I.","chicago":"Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” In <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 1:I, 2006. <a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">https://doi.org/10.1109/ICASSP.2006.1660058</a>.","ama":"Windmann S, Haeb-Umbach R. Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech and its Parameters. In: <i>IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>. Vol 1. ; 2006:I. doi:<a href=\"https://doi.org/10.1109/ICASSP.2006.1660058\">10.1109/ICASSP.2006.1660058</a>"},"intvolume":"         1","page":"I","year":"2006"}]