论文标题
使用数据驱动的方法对工业旋转机进行多门诊断：二十年研究的审查
Multi-Fault Diagnosis Of Industrial Rotating Machines Using Data-Driven Approach: A Review Of Two Decades Of Research
论文作者
论文摘要
工业4.0是一个智能制造的时代。如果没有机械的使用，制造是不可能的。这些机器中的大多数包括旋转组件，称为旋转机器。工程师的重中之重是维护这些关键机器，以减少计划外的关闭并增加机械使用寿命。预测维护（PDM）是智能维护的当前趋势。 PDM中具有挑战性的任务是诊断故障类型。随着人工智能（AI）的进步，数据驱动的预测维护方法正在采用新的飞行智能制造。一些研究人员发表了与旋转机中故障诊断有关的工作，主要探索单一类型的故障。但是，缺乏对文献的合并评论，更多地关注旋转机器的多型诊断。从传感器选择，数据采集，功能提取，多传感器数据融合到系统审查中对多型诊断中使用的AI技术的系统综述，需要系统地涵盖所有方面。在这方面，本文试图通过对数据驱动的方法进行系统文献综述来实现相同的方法，以使用首选的报告项目进行系统评价和荟萃分析（PRISMA）方法，以对工业旋转机器进行多损坏诊断。 Prisma方法是用于系统评价和其他荟萃分析的组成和结构的指南。本文确定了该领域所做的基础工作，并对与工业旋转机的多损坏诊断有关的不同方面进行了比较研究。本文还确定了主要挑战，研究差距。它使用AI的最新进步提供了解决方案。
Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. Majority of these machines comprise rotating components and are called rotating machines. The engineers' top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive maintenance (PDM) is the current trend of smart maintenance. The challenging task in PDM is to diagnose the type of fault. With Artificial Intelligence (AI) advancement, data-driven approach for predictive maintenance is taking a new flight towards smart manufacturing. Several researchers have published work related to fault diagnosis in rotating machines, mainly exploring a single type of fault. However, a consolidated review of literature that focuses more on multi-fault diagnosis of rotating machines is lacking. There is a need to systematically cover all the aspects right from sensor selection, data acquisition, feature extraction, multi-sensor data fusion to the systematic review of AI techniques employed in multi-fault diagnosis. In this regard, this paper attempts to achieve the same by implementing a systematic literature review on a Data-driven approach for multi-fault diagnosis of Industrial Rotating Machines using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method. The PRISMA method is a collection of guidelines for the composition and structure of systematic reviews and other meta-analyses. This paper identifies the foundational work done in the field and gives a comparative study of different aspects related to multi-fault diagnosis of industrial rotating machines. The paper also identifies the major challenges, research gap. It gives solutions using recent advancements in AI in implementing multi-fault diagnosis, giving a strong base for future research in this field.
