Degradation model constructed with the aid of dynamic Bayesian networks
This paper develops a generic degradation model based on Dynamic Bayesian Networks (DBN) which predicts the condition of a technical system. Besides handling bi-directional reasoning, a major benefit of this degradation model using a DBN is its ability to adequately model stochastic processes as wel...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2017-01-01
|
| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2017.1395786 |
| _version_ | 1797715451359264768 |
|---|---|
| author | Anselm Lorenzoni Michael Kempf Oliver Mannuß |
| author_facet | Anselm Lorenzoni Michael Kempf Oliver Mannuß |
| author_sort | Anselm Lorenzoni |
| collection | DOAJ |
| description | This paper develops a generic degradation model based on Dynamic Bayesian Networks (DBN) which predicts the condition of a technical system. Besides handling bi-directional reasoning, a major benefit of this degradation model using a DBN is its ability to adequately model stochastic processes as well as Markov chains. We will assume that the behavior of the degradation can be represented as a P–F-curve (also called degradation or life curve). The model developed here is able to combine information from expert knowledge, any kind of sensor and operating data as well as information from the machine operator. Using the Bayesian approach, uncertain knowledge can be handled appropriately. Thus it is even possible to take into account the environment and stress under which the component or system is operating. Hence, it is possible to detect potential failures at an early stage and initiate appropriate remedy and repair strategies prior to catastrophic failure. |
| first_indexed | 2024-03-12T08:07:00Z |
| format | Article |
| id | doaj.art-87e9895a8efc4e5f984f2f645509d236 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T08:07:00Z |
| publishDate | 2017-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-87e9895a8efc4e5f984f2f645509d2362023-09-02T19:25:09ZengTaylor & Francis GroupCogent Engineering2331-19162017-01-014110.1080/23311916.2017.13957861395786Degradation model constructed with the aid of dynamic Bayesian networksAnselm Lorenzoni0Michael Kempf1Oliver Mannuß2Fraunhofer Institute for Manufacturing Engineering and Automation (IPA)Fraunhofer Institute for Manufacturing Engineering and Automation (IPA)Fraunhofer Institute for Manufacturing Engineering and Automation (IPA)This paper develops a generic degradation model based on Dynamic Bayesian Networks (DBN) which predicts the condition of a technical system. Besides handling bi-directional reasoning, a major benefit of this degradation model using a DBN is its ability to adequately model stochastic processes as well as Markov chains. We will assume that the behavior of the degradation can be represented as a P–F-curve (also called degradation or life curve). The model developed here is able to combine information from expert knowledge, any kind of sensor and operating data as well as information from the machine operator. Using the Bayesian approach, uncertain knowledge can be handled appropriately. Thus it is even possible to take into account the environment and stress under which the component or system is operating. Hence, it is possible to detect potential failures at an early stage and initiate appropriate remedy and repair strategies prior to catastrophic failure.http://dx.doi.org/10.1080/23311916.2017.1395786degradationdynamic bayesian networksp–f-curvestochastic process |
| spellingShingle | Anselm Lorenzoni Michael Kempf Oliver Mannuß Degradation model constructed with the aid of dynamic Bayesian networks Cogent Engineering degradation dynamic bayesian networks p–f-curve stochastic process |
| title | Degradation model constructed with the aid of dynamic Bayesian networks |
| title_full | Degradation model constructed with the aid of dynamic Bayesian networks |
| title_fullStr | Degradation model constructed with the aid of dynamic Bayesian networks |
| title_full_unstemmed | Degradation model constructed with the aid of dynamic Bayesian networks |
| title_short | Degradation model constructed with the aid of dynamic Bayesian networks |
| title_sort | degradation model constructed with the aid of dynamic bayesian networks |
| topic | degradation dynamic bayesian networks p–f-curve stochastic process |
| url | http://dx.doi.org/10.1080/23311916.2017.1395786 |
| work_keys_str_mv | AT anselmlorenzoni degradationmodelconstructedwiththeaidofdynamicbayesiannetworks AT michaelkempf degradationmodelconstructedwiththeaidofdynamicbayesiannetworks AT olivermannuß degradationmodelconstructedwiththeaidofdynamicbayesiannetworks |