Why would you wait for something to break before deciding how to fix it?
This approach wastes time and money in any industry, and now is the time to optimize AI, as every company is focusing on it. The better approach is to go from this reactive attitude to a proactive one.
What does this mean? It means using the strengths of AI to maintain equipment predictively. Repairs and maintenance are fast because this technology knows what it will have to do before it does it, saving time and money. Equipment has little to no downtime, and the company continues to generate revenue.
This article describes how AI enables predictive maintenance, the benefits for manufacturers and facility managers, industry applications, and the importance of cybersecurity in these processes.
How AI Enables Predictive Maintenance
It’s useful to be aware of how AI technologies enable the powerful capabilities of predictive maintenance. Armed with this knowledge, you can use AI predictive maintenance in the best way in your organization.
So, how does AI decide what repairs and maintenance equipment will be needed? It uses various techniques, such as sensor data, which it collects from equipment, machine learning to process the data, and pattern recognition to predict the failures likely to happen.
AI needs specific datasets to achieve the above. The inputs it needs to make the highest quality predictions for maintenance and repair include:
Vibration Analysis
This data tracks oscillations in machines to identify early signs of issues like imbalance, misalignment, or bearing failure. It's commonly used in manufacturing and heavy industry, such as automotive production, where detecting abnormal vibrations can prevent major equipment breakdowns.
Temperature
Temperature data monitors the heat levels of machine parts. A rise can indicate overheating due to friction, electrical faults, or fluid loss. This is especially important in the energy sector, such as in wind turbines, where excessive heat can damage components.
Usage Cycles
Usage cycle data records how often a machine completes operations or loads, helping predict wear and plan servicing. In the aerospace industry, for example, tracking landing gear cycles ensures timely maintenance, preventing failure from repeated high-stress mechanical activity.
One of the most common ways experts use this information is when the AI system alerts a technician before a motor overheats. This process of alert saves a lot of time because overheating can lead to damage and expensive replacement. But AI can avoid this and recommend maintenance, which is cheaper.
Benefits for Manufacturers and Facility Managers
If you invest in and implement artificial intelligence to predict maintenance and repair issues before they happen, it’s good to be aware of the benefits. Knowing these benefits will help you get the most out of this technology, and if you already use it, check these points out to ensure you are using AI for predictive maintenance in the best way.
You’ll find four main benefits to this technology:
Optimized Resource Allocation
AI predictive maintenance allows businesses to allocate maintenance staff, parts, and budgets more efficiently by predicting exactly when and where attention is needed. This prevents over-servicing, minimizes downtime, and ensures resources are directed toward equipment that truly requires intervention, improving overall operational planning and cost control.
Reduced Unexpected Outages
When you use AI to analyze real-time equipment data, it’s able to identify early warning signs of failure. This foresight enables proactive maintenance, significantly lowering the risk of sudden breakdowns. Industries like manufacturing and utilities benefit greatly by avoiding costly production halts and service disruptions caused by unplanned outages.
Longer Equipment Life
AI is now capable of continuously monitoring crucial data like equipment health and usage trends, recommending timely, condition-based maintenance. This data helps prevent excessive wear or damage from neglected issues. Over time, such precision in care extends the operational lifespan of machinery, reducing capital expenditures on replacements and ensuring greater return on investment.
Workforce Efficiency
The great thing about AI is that it’s able to shift maintenance from reactive to proactive. This approach can lead to a dramatic reduction in breakdowns, allowing human technicians to focus on their regular, scheduled interventions instead of rushing to fix failures. This approach leads to lower burnout and higher well-being and productivity.
Consider these benefits. Do they apply to your organization? Are you making the best use of AI predictive maintenance? The answers will help you decide whether to invest or review your strategy to optimize this technology.
Cybersecurity and Third-Party Risk Management
One aspect of AI predictive maintenance we haven’t addressed yet is cybersecurity. With the current state and prevalence of generative AI, security is a serious issue, but it is often not mentioned by vendors. Consider all the data you enter into AI tools and imagine what would happen if a hacker stole this information. If this causes concern, it should. Now let’s consider ways to tackle this challenge.
One of the most effective ways to ensure security when using AI predictive maintenance tools is to use TPRM (third-party risk management) to secure data exchange with analytics platforms, OEMs (original equipment manufacturers), and software providers. These tools ensure that all software vendors are secure and are not collecting your data secretly.
Alongside third-party risk management tools, it’s essential to follow practices like regular audits, vendor vetting, and secure data channels to keep security up to date and effective.
Conclusion
The shift is already happening from reactive traditional approaches to maintenance and repair to AI and proactive predictions that save time and money. Consider AI for this function or be left behind your market peers.
The exciting thing to remember is that this technology is in its infancy: predictive systems will evolve with generative AI, robotics, and edge computing, so it takes no time at all to tell experts where repairs and maintenance are needed. Invest today to stay ahead of the curve and avoid downtime.