Abstract The use of a modern technological system requires a good engineering approach, optimized operations, and proper maintenance in order to keep the system in an optimal state. Predictive maintenance focuses on the organization of maintenance actions according to the actual health state of the system, aiming at giving a precise indication of when a maintenance intervention will be necessary. Predictive maintenance is normally implemented by means of specialized computational systems that incorporate one of several models to fulfil diagnostics and prognostics tasks. As complexity of technological systems increases over time, single-model approaches hardly fulfil all functions and objectives for predictive maintenance systems. It is increasingly common to find research studies that combine different models in multi-model approaches to overcome complexity of predictive maintenance tasks, considering the advantages and disadvantages of each single model and trying to combine the best of them. These multi-model approaches have not been extensively addressed by previous review studies on predictive maintenance. Besides, many of the possible combinations for multi-model approaches remain unexplored in predictive maintenance applications; this offers a vast field of opportunities when architecting new predictive maintenance systems. This systematic survey aims at presenting the current trends in diagnostics and prognostics giving special attention to multi-model approaches and summarizing the current challenges and research opportunities.

中文翻译：

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迈向预测性维护的多模型方法：关于诊断和预测的系统文献调查

摘要 现代技术系统的使用需要良好的工程方法、优化的操作和适当的维护，以保持系统处于最佳状态。预测性维护侧重于根据系统的实际健康状态组织维护操作，旨在准确指示何时需要进行维护干预。预测性维护通常通过专门的计算系统来实现，这些系统结合了几种模型之一来完成诊断和预测任务。随着技术系统的复杂性随着时间的推移而增加，单一模型方法很难满足预测性维护系统的所有功能和目标。在多模型方法中结合不同模型来克服预测性维护任务的复杂性、考虑每个单一模型的优缺点并尝试将它们中的优点结合起来的研究越来越普遍。先前关于预测性维护的审查研究尚未广泛解决这些多模型方法。此外，在预测性维护应用中，多模型方法的许多可能组合仍未得到探索；这在构建新的预测性维护系统时提供了广阔的机会。这项系统调查旨在展示诊断和预后的当前趋势，特别关注多模型方法并总结当前的挑战和研究机遇。