Risk‐based approach for managing obsolescence for automation systems in heavy industries

Abstract

Obsolescence is an important consideration in the management and engineering of heavy industrial facilities. Examples of these could include refineries, chemical plants, and other large‐scale materials processing facilities. These facilities have lifetimes of many decades but operate on software, automation hardware, and other electrical equipment that reaches obsolescence much sooner than the rotating equipment, unit operations, and other major systems of the facility. This work proposes a method to manage obsolescence of automation and electrical systems at industrial sites. This risk management method was developed by first analyzing a database of upgrade projects at several industrial sites and identifying parts that had been replaced due to obsolescence. The analysis was used to prioritize replacement of obsolescent parts based on the average operational lifespans, their criticality in operations, their manufacturer's continued production state, and other factors. Based on those results, a taxonomy of obsolescence risk, and a risk assessment plan were developed to manage the replacement of parts. The value of this proposed management strategy was validated through its application to 13 heavy industrial facilities. The results indicate a reduction of roughly 70% in reactive replacements due to obsolescence after the major upgrade and a 24% reduction in unplanned downtime due to part failure during normal operations. While this study is focused on heavy industries, the proposed method for identifying and managing component obsolescence can be applied to other industries and systems.

Exploring Filter Banks and Spike Interval Statistics of Level-Crossing ADCs for Fault Diagnosis of Rolling Element Bearings

“…The global machine condition monitoring market is projected to soar to USD 4 billion by 2027, driven by escalating demand from oil and gas, power generation, and automotive industries (MarketsandMarkets, 2023). Machine condition monitoring systems led to a remarkable 24% drop in unplanned downtime (Ault & Bradley, 2022a) and a significant 10-25% decrease in maintenance expenses (uit het Broek et al, 2021;Yeardley et al, 2022).…”

“…Zhang et al, 2021;Zuo et al, 2022) The increasing interest in "black-box" solutions can be attributed to their robustness and high performance in tackling complex and nonlinear problems (Hoang & Kang, 2019). The application of these solutions brings significant benefits to industries, including optimized maintenance activities, improved equipment uptime, reduced operating costs, and enhanced overall operational efficiency (Ault & Bradley, 2022b;uit het Broek et al, 2021;Yeardley et al, 2022). The development of advanced statistical and intelligent predictive maintenance techniques aims to optimize the diagnostic approach by leveraging recorded data.…”