Cost-Benefit Analysis for Justifying Investment in Predictive Maintenance

Naresh Iyer, GE Research ABSTRACT: The operational safety, cost and criticality of complex, industrial assets like aircraft engines, gas/wind turbines, locomotives, nuclear reactors have historically led to the design of conservative sustainment and maintenance strategies for these assets. However, such strategies: are often wasteful due to remaining component-life that is discarded, they curtail revenue by reducing effective availability of assets, and lead to high O&M (operations and maintenance) costs due to unnecessary time, effort and cost expended. In cases like the nuclear industry, a key barrier to adoption as well as future growth is the extremely high capital and O&M costs. Additionally, the dire demands of climate change are more regularly driving the need to employ many of these industrial assets within paradigms that are more efficient, frugal, and less wasteful. Predictive analytics, and maintenance strategies devised using them, have been identified to be a key capability for addressing some of these challenges. When designed correctly, preventive maintenance (PM) strategies make use of sensing, monitoring, and predictive analytics to reliably assess condition of asset-components in real-time, thus enabling cost-efficient, sustainment workflows in support of the asset, that optimally trade off sustainment costs, waste, and revenue, while retaining same levels of operational safety. However, the additional investments required to stand up the PM paradigm can itself become a barrier to the adoption and deployment of PM strategies in the industry. Like most investments, an assessment of the return on investment (RoI) is often a critical factor that can serve towards helping make them. However, a cost-benefit analysis of a PM paradigm for a complex machinery with thousands of components can be a non-trivial activity – the information resolution required to assess conclusive benefits might often not be available or uncertain, while a very coarse analysis might make it hard for the operator to believe in the outcome. Multiple factors of uncertainty arise from not knowing how much of the relevant phenomena is observable with the sensors in place, not knowing the true entitlement of the predictive analytics driving the PM, and how effective the analytics will need to be, in terms of their accuracy and reliability, to drive decisions that are overall cost-reducing. At a secondary level, errors caused by predictive analytics lead to newer costs from suboptimal decisions, and thus one needs to account for those as well. In this talk, we introduce a cost-benefit framework we developed at GE to support strategy development for PM related to the new BWRX300 nuclear reactor. The framework is generalizable to enable similar cost-impact evaluations for PM evaluation scenarios specific to other industries. Most importantly, it allows one to assess the limits of cost-impact (entitlement) from usage of predictive maintenance, while additionally deriving requirements for PHM algorithm performance, which traditionally have not been rigorously connected to high level metrics. The goal is to drive such analysis to help estimate likely cost reductions from deploying PM and to derive required performance targets, both in terms of accuracy and reliability of the predictive analytics, for the cost reductions to manifest.This work was funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under GEMINA program Award Number DE-AR0001290.