Model-based fault localization in bottling plants |
| |
Affiliation: | 1. Technische Universität München, Food Packaging Technology, Weihenstephaner Steig 22, D-85354 Freising, Germany;2. Technische Universität München, Software & Systems Engineering, Boltzmannstr. 3, D-85748 Garching bei München, Germany;1. bioMérieux Italia S.P.A., I&S Global Design, Via di Campigliano 58, 50015 Ponte a Ema, Firenze, Italy;2. Politecnico di Bari, Department of Electric and Information Engineering, Via E. Orabona 4, 70126 Bari, Italy;1. Department of Knowledge Technologies, Jožef Stefan Institute, Jamova cesta 39, Ljubljana, Slovenia;2. Department of Systems and Control, Jožef Stefan Institute, Jamova cesta 39, Ljubljana, Slovenia;3. Jožef Stefan International Postgraduate School, Jamova cesta 39, Ljubljana, Slovenia;4. University of Nova Gorica, Vipavska 13, Nova Gorica, Slovenia;1. Machinery Systems Division, Wroclaw University of Science and Technology, Poland;2. Economics of Industry and Geoeconomics Division, Wroclaw University of Science and Technology, Poland |
| |
Abstract: | The bottling of beverages is carried out in complex plants that consist of several machines and material flows. To realize an efficient bottling process and high quality products, operators try to avoid plant downtimes. With actual non-productive times of between 10% and 60%, the operators require diagnosis tools that allow them to locate plant components that cause downtime by exploiting automatically acquired machine data.This paper presents a model-based solution for automatic fault diagnosis in bottling plants. There are currently only a few plant-specific solutions (based on statistical calculations or artificial neural networks) for automatic bottling plant diagnosis. In order to develop a customizable solution, we followed the model-based diagnosis approach which allows the automatic generation of diagnosis solutions for individual plants. The existing stochastic and discrete-event models for bottling plants are not adequate for model-based diagnosis. Therefore, we developed new first-principle models for the relevant plant components, validated them numerically, and abstracted them to qualitative diagnosis models. Based on the diagnosis engine OCC’M Raz’r, application systems for two real plants and one virtual plant (based on discrete-event simulation) were generated and evaluated. Compared to the reasons for downtime identified by experts, we obtained up to 87.1% of compliant diagnosis results. The diagnosis solution was tested by practitioners and judged as a useful tool for plant optimization. |
| |
Keywords: | Model-based fault localization Automatic fault diagnosis Consistency-based diagnosis Bottling plant Packaging line |
本文献已被 ScienceDirect 等数据库收录! |
|