by Emily Newton
Predictive maintenance has long received recognition as a critical strategy for minimizing downtime, reducing maintenance costs and maximizing the life span of industrial equipment. A relatively new offset — AI predictive maintenance — introduces entirely new possibilities.
Outside of a few minor tasks, conventional maintenance tends to be reactionary. In other words, teams carry it out after an event or problem has occurred. Predictive maintenance is much more proactive, which means underperforming equipment receives service well before a failure.
Artificial intelligence (AI) integration has revolutionized the field in recent years, making predictive maintenance smarter, more accurate and less costly than ever. By harnessing the power of AI, organizations can unlock valuable insights from vast amounts of data, enabling proactive maintenance actions and preventing catastrophic equipment failures.
What Is AI Predictive Maintenance?
Predictive maintenance is when technicians, engineers, and workers utilize proactive care to prevent equipment from malfunctioning or failing. They may even use care to mitigate performance reductions that happen over time. This is the opposite of conventional or corrective maintenance, and often leverages automation software and robust data to ensure everything is carried out appropriately.
For example, monitoring tools might detect when a machine’s performance is declining or experiencing abnormal behavior. Technicians receive the alert and can administer the appropriate care to improve operations.
AI predictive maintenance is a relatively new spin on that process which introduces machine learning and AI-driven monitoring solutions. By definition, these technologies are faster and smarter at detecting anomalous signs, primarily because of how much data they access and ingest over time. Moreover, industry analysts believe AI-based predictive maintenance offers significant cost savings, boosting availability for services by as much as 20%, and reducing inspection costs by 25% and annual maintenance fees by up to ten percent.
Continuous Learning and Improvement
One of the key strengths of AI is its ability to learn and improve over time. By analyzing historical data, AI algorithms can uncover hidden patterns, correlations and failure modes human experts might have overlooked. This continuous learning process allows AI systems to refine their predictive models, resulting in increasingly accurate predictions and reduced false positives.
Additionally, organizations can gain valuable insights into equipment usage, environmental factors, and operational conditions as more data is collected and analyzed, further refining their maintenance strategies and optimizing asset management practices. Simply put, AI predictive maintenance will grow to be smarter and more effective the more it’s in use and the more operations adopt it.
Enhanced Fault Detection and Diagnosis
One of the primary advantages of AI in predictive maintenance is its ability to identify subtle patterns and anomalies in data that may indicate the onset of equipment failure. Traditional approaches to care rely on predetermined maintenance schedules or simple rule-based systems, leading to unnecessary maintenance actions and wasted resources.
On the other hand, AI can process enormous volumes of sensor data, historical records and real-time information, enabling the detection of early warning signs of equipment deterioration. It can also identify various aspects of operations to measure and react to performance. Once a machine begins to slow to the point where it impacts productivity, there’s enough data to understand what’s happening and why. In this way, AI reduces the troubleshooting aspect of maintenance, too.
Moreover, AI systems can learn from past maintenance data and continuously improve their fault detection and diagnosis capabilities, resulting in more accurate predictions and reduced false alarms. This is imperative, considering many traditional maintenance tasks rely on telltale signs of failure, like watching for leaks, unusual noise, vibrations or discolorations. While these signs can certainly be helpful and technicians should always be on the lookout for them, it’s best to act before they appear.
Condition-Based Maintenance Optimization
AI-driven predictive maintenance allows organizations to transition from preventive care to condition-based maintenance (CBM) — a relatively new approach. Rather than performing maintenance tasks based on fixed schedules, CBM relies on the real-time condition of the equipment. By integrating AI algorithms with IoT sensors and monitoring devices, organizations can gather a wealth of data on equipment health, performance and environmental factors.
This data can be leveraged to identify patterns and correlations, enabling the prediction of optimal maintenance windows. As a result, team members can perform maintenance precisely when necessary, minimize downtime, and reduce unnecessary repairs or replacements, leading to significant cost savings.
This prevents the overuse of resources from conducting regular maintenance routines like oil or fluid changes, or parts replacements. It’s all handled precisely when the equipment or systems need service. It also significantly reduces the potential of over-servicing or administering too much maintenance.
Improved Asset Performance Management
AI-based predictive maintenance also offers insights into the overall performance and health of assets across an entire facility or organization. By leveraging AI, organizations can establish a comprehensive asset performance management system that provides a holistic view of equipment conditions, failure trends and maintenance history. This enables the identification of systemic issues and the development of proactive strategies to mitigate potential failures.
It can also help pinpoint areas of the operation where improvements are vital. Suppose you can see from a top-down perspective that specific equipment or systems require more care than others. You’re now in a position to seek more effective alternatives — or, you can find a way to phase out unwanted processes completely.
By addressing these underlying problems, organizations can optimize asset performance, extend equipment lifespans, and reduce the need for costly emergency repairs or replacements. If and when the time arises, they can also pinpoint the operational processes and systems that need addressing, whether removing them, phasing them out, or finding an entirely new solution.
Optimal Resource Allocation
AI-based predictive maintenance allows organizations to optimize resource allocation, ensuring maintenance efforts aim toward the most critical equipment and components. Some may need more care than others and some are mission-critical, so their servicing should always be a higher priority. By prioritizing maintenance actions based on data-driven insights, organizations can allocate their limited resources more effectively, focusing on high-risk assets that are more likely to fail.
A targeted approach helps reduce maintenance costs by eliminating unnecessary interventions on low-risk equipment. Furthermore, AI can assist in inventory management, ensuring the correct spare parts and tools are available when necessary to minimize inventory costs and reduce downtime. The opposite is also true — it reduces an overabundance of certain items or components in facility inventories. Components that are never used can collect dust for years, taking up valuable warehouse space and wasting resources — like the initial cost to acquire them.
AI Predictive Maintenance: Crucial to Future Operations
AI has emerged as a game-changer in predictive maintenance, making it smarter, more accurate and less costly for organizations. The benefits of AI-driven predictive maintenance include minimized downtime, reduced maintenance costs and optimized asset performance.
The future of predictive maintenance lies in the virtual hands of AI. It will help to unlock new possibilities and revolutionize asset management practices while achieving never-before-seen levels of efficiency.
