In this document we perform a systematic review of the State-of-the-art in Predictive Maintenance (PdM) over the last five years in industrial settings such as commercial buildings, pharmaceutical facilities, or semi-conductor manufacturing. In general, data-driven methods such as those based on deep learning, exhibit higher accuracy than traditional knowledge-based systems. These systems however, are not without significant limitations. The need for large labeled data sets, a lack of generalizability to new environments (out-of-distribution generalization), and a lack of transparency at inference time are some of the obstacles to adoption in real world environments. In contrast, traditional approaches based on domain expertise in the form of rules, logic or first principles suffer from poor accuracy, many false positives and a need for ongoing expert supervision and manual tuning. While the majority of approaches in recent literature utilize some form of data-driven architecture, there are hybrid systems which also take into account domain specific knowledge. Such hybrid systems have the potential to overcome the weaknesses of either approach on its own while preserving their strengths. We propose taking the hybrid approach even further and integrating deep learning with symbolic logic, or Neuro-symbolic AI, to create more accurate, explainable, interpretable, and robust systems. We describe several neuro-symbolic architectures and examine their strengths and limitations within the PdM domain. We focus specifically on methods which involve the use of sensor data and manually crafted rules as inputs by describing concrete NeSy architectures. In short, this survey outlines the context of modern maintenance, defines key concepts, establishes a generalized framework, reviews current modeling approaches and challenges, and introduces the proposed focus on Neuro-symbolic AI (NESY).

翻译：本文对过去五年中在商业建筑、制药设施或半导体制造等工业环境中预测性维护（PdM）领域的最新进展进行了系统性综述。总体而言，基于深度学习等数据驱动方法比传统的基于知识的系统展现出更高的准确性。然而，这些系统也存在显著局限性：需要大量标注数据集、对新环境缺乏泛化能力（分布外泛化）以及在推理时缺乏透明度，这些都是其在现实环境中部署的障碍。相比之下，基于领域专业知识（以规则、逻辑或第一性原理形式）的传统方法则存在准确性低、误报率高以及需要持续专家监督和手动调整等问题。尽管近期文献中的大多数方法都采用了某种形式的数据驱动架构，但也存在同时考虑领域特定知识的混合系统。此类混合系统有潜力在保留各自优势的同时，克服单一方法的弱点。我们建议进一步推进混合方法，将深度学习与符号逻辑相结合，即神经符号人工智能，以创建更准确、可解释、可理解且鲁棒的系统。我们描述了几种神经符号架构，并分析了它们在PdM领域中的优势与局限。通过描述具体的神经符号架构，我们特别关注那些使用传感器数据和人工构建规则作为输入的方法。简而言之，本综述概述了现代维护的背景，定义了关键概念，建立了一个通用框架，回顾了当前建模方法与挑战，并介绍了所提出的神经符号人工智能研究重点。