How to take your reliability centered maintenance strategy to the next level with AI

In high-hazard industries, ensuring safety and reliability is everyone’s top priority. Reliability-centered maintenance (RCM) has been the go-to strategy for managing assets, helping organisations reduce risk whilst assuring performance.

Like most maintenance strategies, people are crucial for a successful RCM approach. However, relying on individual knowledge and opinion engineering can lead to biased decisions. When inputting and analysing vast volumes of data, human error is common.

How can we improve the quality and reduce the time to value of insights that can be gained from our maintenance data? The answer: Natural Language Processing (NLP). 

What is Natural Language Processing (NLP)?

NLP is a field of artificial intelligence (AI) that focuses on the interaction between computers and human language. It involves the use of algorithms and models that enable machines to understand and interpret language.

The rise of large language models (LLMs) such as OpenAI’s ChatGPT has significantly enhanced NLP’s ability to analyse free text. Put simply, these models examine vast amounts of data to understand context and perform complex language tasks with high accuracy.

Using NLP for failure mode analysis in RCM

The performance improvements which LLMs have given NLP applications in areas like sentiment analysis and text summarisation, significantly enhance Failure Mode and Effects Analysis (FMEA) within RCM.

Traditionally, failure mode analysis relies on structured data, such as correctly recording the failure class within a Computerised Maintenance Management System (CMMS). However, a vast amount of unstructured data - operator rounds, work order notification free text, safety observations - remains underutilised due to a perception (…or reality) of either being of poor quality or too hard to analyse at scale.

NLP in action: How Wood helps customers unlock failure insights

At Wood, we developed maintAI to take advantage of NLP’s power to extract meaningful insights from unstructured data, identifying functional failures and assigning failure modes. This enables automated classification of failure mechanisms, highlighting trends to allow us to tailor maintenance strategies based upon the actual performance of the asset(s).

By integrating NLP into FMEA, we can analyse corrective breakdowns over 90% quicker than traditional methods.  These deep insights are used to develop targeted maintenance strategies to enhance process safety and reliability.  Using maintAI, Wood delivers optimised maintenance strategies for large portfolios of assets in 6-12 weeks, instead of 12-24 months.

AI: Assisting, not replacing subject matter experts (SMEs)

Since its inception, there has been fear that AI is going to take jobs and replace human input.

I want to reiterate that this is not the case. We do not see use of AI phasing out SME input into operations.  We have to recognise, however, that SMEs are capacity constrained.  Data which could be used to assist them to make data-driven decisions is often siloed or buried in disparate systems and spreadsheets.

By automating data collection, analysis and classification, AI reduces the burden of manual data processing. It will act as their friend, not foe, doing the heavy lifting which allows SMEs to focus on data-driven decision-making. We use AI to enhance, not replace, the skills and experience of SMEs.

Rather than replacing human judgment, AI augments it by identifying patterns and correlations that might be overlooked. This people-centric approach empowers SMEs to validate AI-driven recommendations, prioritise interventions, and develop tailored maintenance strategies, ultimately improving process safety and reliability while retaining human oversight.

The future of maintenance: AI-powered excellence

By integrating AI into RCM, we are not just enhancing process safety and reliability - but releasing important capacity within our site and support teams.

maintAI’s innovative use of NLP and predictive analytics allows us to make data-driven decisions faster and more accurately, reducing risks and optimizing where our capacity-limited site teams spend their time. SMEs can work smarter, not harder, with the use of AI.

As we continue to bridge the gap between human expertise and AI capabilities, the future of maintenance optimisation looks brighter than ever.