Condition monitoring in marine oil separation systems using Wavelet Packet Transform and Genetic Technique
Condition Monitoring is key to predictive maintenance and especially in the operational efficiency of the Marine Oil Separation System. These systems are crucial for environmental protection and compliance with international maritime regulations. Therefore, it is necessary to design a technique capa...
| Autores: | , , , , |
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| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universidad de La Laguna (ULL) |
| Repositorio: | RIULL. Repositorio Institucional de la Universidad de La Laguna |
| OAI Identifier: | oai:riull.ull.es:915/40163 |
| Acesso em linha: | http://riull.ull.es/xmlui/handle/915/40163 |
| Access Level: | acceso abierto |
| Palavra-chave: | wavelet packet transform genetic neuro-fuzzy condition monitoring |
| Resumo: | Condition Monitoring is key to predictive maintenance and especially in the operational efficiency of the Marine Oil Separation System. These systems are crucial for environmental protection and compliance with international maritime regulations. Therefore, it is necessary to design a technique capable of analyzing the signals from sensors and estimating the remaining useful life in order to avoid breakage or unnecessary replacement. This work presents an intelligent method with signal processing based on Wavelet Packets Transform that provides energy data from vibration measurements as characteristic parameters. These values can be related to its RUL, and they are used as inputs for the training process. In particular, a Genetic Neuro-Fuzzy system is proposed as an intelligent classification technique. Once the training process is completed, it can be concluded that a good classifier has been built, since it relates the energy state of the oil separation system with its remaining useful life, and therefore, the necessary information for efficient predictive maintenance is achieved. Furthermore, a mechanism to obtain the final set of fuzzy rules has been developed, showing the correspondence between these fuzzy rules and the neural network structure. |
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