Effective and early fault detection and diagnosis techniques have tremendously enhanced over the years to ensure continuous operations of contemporary complex systems, control cost, and enhance safety in assets‐intensive industries, including oil and gas, process, and power generation. The objective of this work is to understand the development of different fault detection and diagnosis methods, their applications, and benefits to the industry. This paper presents a contemporary state‐of‐the‐art systematic literature survey focusing on a comprehensive review of the models for fault detection and their industrial applications. This study uses advanced tools from bibliometric analysis to systematically analyze over 500 peer‐reviewed articles on focus areas published since 2010. We first present an exploratory analysis and identify the influential contributions to the field, authors, and countries, among other key indicators. A network analysis is presented to unveil and visualize the clusters of the distinguishable areas using a co‐citation network analysis. Later, a detailed content analysis of the top‐100 most‐cited papers is carried out to understand the progression of fault detection and artificial intelligence–based algorithms in different industrial applications. The findings of this paper allow us to comprehend the development of reliability‐based fault analysis techniques over time, and the use of smart algorithms and their success. This work helps to make a unique contribution toward revealing the future avenues and setting up a prospective research road map for asset‐intensive industry, researchers, and policymakers.

中文翻译：

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具有工业应用的基于可靠性的故障分析模型：系统的文献综述

多年来，有效且早期的故障检测和诊断技术得到了极大的提高，以确保当代复杂系统的连续运行，控制成本并提高资产密集型行业（包括石油和天然气，过程和发电）的安全性。这项工作的目的是了解各种故障检测和诊断方法的发展，它们的应用以及对行业的好处。本文介绍了当代最先进的系统文献调查，重点是对故障检测模型及其工业应用的全面回顾。这项研究使用了来自文献计量分析的高级工具，系统分析了自2010年以来发表的500余篇关于重点领域的同行评审文章。我们首先提出一项探索性分析，并确定对领域，作者和国家的重要贡献以及其他关键指标。使用共引网络分析法进行网络分析，以揭示和可视化可区分区域的聚类。后来，对前100篇最热门论文进行了详细的内容分析，以了解故障检测和基于人工智能的算法在不同工业应用中的进展。本文的发现使我们能够理解随着时间的推移基于可靠性的故障分析技术的发展，以及智能算法的使用及其成功。这项工作有助于为揭示未来途径和为资产密集型行业，研究人员，