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.

Weibull reliability and maintainability analysis have been used to analyze the time between failures and time to repair data of a group of steam turbines being used in a large oil refinery. Failure history of a set of steam turbines was obtained from the Computerized Maintenance Management System of the plant. Out of 50 steam turbines in operation, 13 are identified as bad actors which have experienced ≥3 failures in five years. The Pareto analysis performed on this set of turbines further narrowed down the 10 most critical (worst performing) turbines. This group of most critical turbines is the primary target for this Weibull reliability and maintainability analysis. The Weibull reliability and maintainability analysis provides an indication of the equipment reliability and maintainability characteristics including their failure rates and repair rates. In addition to the failure and repair data, the associated maintenance cost for this group of turbines was also collected over a period of five years, and the trends in cost increase with respect to time are plotted.