Center for Energy Informatics, University of Southern Denmark, 5230 Odense, Denmark.
Ørsted, Markets & Bioenergy, Asset Risk Management, Kraftværksvej 53, 7000 Fredericia, Denmark.
Sensors (Basel). 2020 Apr 24;20(8):2425. doi: 10.3390/s20082425.
Thermal power plants are an important asset in the current energy infrastructure, delivering ancillary services, power, and heat to their respective consumers. Faults on critical components, such as large pumping systems, can lead to material damage and opportunity losses. Pumps plays an essential role in various industries and as such clever maintenance can ensure cost reductions and high availability. Prognostics and Health Management, PHM, is the study utilizing data to estimate the current and future conditions of a system. Within the field of PHM, Predictive Maintenance, PdM, has been gaining increased attention. Data-driven models can be built to estimate the remaining-useful-lifetime of complex systems that would be difficult to identify by man. With the increased attention that the Predictive Maintenance field is receiving, review papers become increasingly important to understand what research has been conducted and what challenges need to be addressed. This paper does so by initially conceptualising the PdM field. A structured overview of literature in regard to application within PdM is presented, before delving into the domain of thermal power plants and pump systems. Finally, related challenges and trends will be outlined. This paper finds that a large number of experimental data-driven models have been successfully deployed, but the PdM field would benefit from more industrial case studies. Furthermore, investigations into the scale-ability of models would benefit industries that are looking into large-scale implementations. Here, examining a method for automatic maintenance of the developed model will be of interest. This paper can be used to understand the PdM field as a broad concept but does also provide a niche understanding of the domain in focus.
火力发电厂是当前能源基础设施中的重要资产，为各自的消费者提供辅助服务、电力和热能。关键部件（如大型泵送系统）出现故障会导致物质损坏和机会损失。泵在各个行业中都起着至关重要的作用，因此，明智的维护可以降低成本并提高可用性。预测与健康管理（PHM）是利用数据来估计系统当前和未来状况的研究。在 PHM 领域中，预测性维护（PdM）越来越受到关注。可以构建数据驱动的模型来估计复杂系统的剩余使用寿命，而这些系统单凭人力是难以识别的。随着预测性维护领域受到越来越多的关注，综述论文对于了解已经进行了哪些研究以及需要解决哪些挑战变得越来越重要。本文通过初步概念化 PdM 领域来实现这一目标。本文首先呈现了有关 PdM 应用的文献综述，然后深入探讨了火力发电厂和泵系统领域。最后，概述了相关挑战和趋势。本文发现，大量基于实验数据驱动的模型已经成功部署，但 PdM 领域将受益于更多的工业案例研究。此外，对模型可扩展性的研究将使那些正在考虑大规模实施的行业受益。在这里，研究一种自动维护开发模型的方法将是很有意义的。本文可以用于了解 PdM 领域的广泛概念，但也提供了对关注领域的专业理解。
