The Reliability Revolution with
Predictive Maintenance and AI

Jeff Craig, Fuss & O’Neill Manufacturing Solutions

Manufacturers worldwide face numerous challenges in their operations. Among the most critical is maintaining equipment reliability. Many organizations operate in a reactive mode, constantly firefighting issues as they occur, while others perform maintenance that either adds little value or doesn’t prevent recurring failure modes. Regardless of industry or location, reducing unplanned downtime, increasing production capacity, and ensuring high quality are essential goals. Achieving these objectives is possible with strategic, proactive maintenance approaches that transform their operations from costly reactive responses to efficient, reliable processes.

The challenge with achieving reliability often stems from barriers that hinder timely maintenance. Maintenance teams frequently voice concerns such as, “Production won’t give us the equipment,” “We don’t have time to do our PMs,” and “We don’t have enough personnel.” In response, I often ask, “What kind of Predictive Maintenance do you implement?” The most common answer is, “We don’t!” This highlights a critical gap that, if addressed, can significantly improve equipment reliability.

Predictive maintenance (PdM), like preventive maintenance, also faces barriers. Some common ones include “it’s too expensive,” “it’s too complicated,” or the misconception that preventive maintenance must be perfect before predictive maintenance can be implemented. These
perceived obstacles can prevent organizations from shifting their maintenance culture from reactive to proactive, which can result in wasted time and money that could be invested in growing the business. The truth is that PdM is more accessible and affordable than many believe, and it offers a clearer view of the current condition of equipment. Overcoming these barriers to effective maintenance can start with PdM. Remember: if you can predict equipment failure and plan and perform repairs before it happens, you have effectively prevented the failure.

This process involves researching options to choose the best starting point. Data is a key PdM tool, helping predict failures like bearing issues that can be over- or under-greased. Replacing scheduled greasing with ultrasound monitoring shifts from calendar-based to condition-based maintenance. If ultrasound shows stable decibel levels, no action is needed; if levels rise, repairs are planned. This saves time and money, offers real-time insights, and can prevent shutdowns.

With today’s rapidly advancing technology, Artificial Intelligence (AI) is also becoming a valuable asset for the maintenance of your organization. AI enhances predictive maintenance by transforming vast amounts of raw operational data into precise, actionable insights—faster than human analysis ever could. AI algorithms can continuously examine sensor readings, vibration patterns, temperature data, and operational history to identify subtle trends and anomalies that signal early signs of failure.

Unlike traditional PdM methods, which often rely on manual interpretation, AI can automatically detect potential issues and estimate remaining useful life (RUL). and recommend the most cost-effective intervention. This not only speeds up decision-making but also allows maintenance teams to focus their time where it truly matters. For example, AI-powered analytics can combine ultrasound readings, oil particulate data, and temperature trends into a single health score for each asset. When a deviation occurs, such as a slight increase in bearing temperature combined with an unusual vibration signature, the system can automatically generate a maintenance work order, order spare parts, and even suggest the best time to schedule repairs to reduce production impact. Over time, AI learns from each repair and failure event, enhancing prediction accuracy and decreasing false alarms. In short, AI removes guesswork, integrates multiple PdM technologies into a single decision platform, and enables a shift from reactive firefighting to a truly optimized, data-driven reliability culture.

Having a strong foundation is essential for building reliability and fostering a culture of success within your organization. Relying solely on past achievements is a gamble; organizations that continuously look ahead and innovate are the ones that stay ahead of the competition. Don’t let complacency hold you back — ensure your organization is future-ready today.

Side Note: Jeff Craig is the facilitiator for NWIRC’s Maintenance Masters Certification. The next cohort is starting October 21. Reach out to rkeller@nwirc.org for details or visit: nwirc.org/maintenance.