Equipment has signals that tell you it’s nearing failure. Vibrations change. Temperatures rise alarmingly. An experienced operator may even be able to tell there’s trouble coming when a machine starts sounding different.
AI-powered predictive maintenance lets AI continuously monitor these signals, spotting the early warning signs to fix problems. And it’s easier (and more cost-effective) to implement than you may think.
Thinking Beyond Traditional Maintenance
Right now, you’re probably doing one of two things:
- Reactive maintenance: You fix things when they break, causing downtime on your machines or production line that impacts productivity.
- Preventive maintenance: You replace parts and run maintenance on a schedule, whether it’s convenient or truly needed.
Both of these approaches cost you, in money and opportunity. With AI in manufacturing, however, you have a third path: predictive maintenance. Instead of guessing when maintenance is needed (or hoping for the best), you have continuous monitoring letting you know exactly when to act.
A Simple Addition to Your Maintenance Tool Kit Could Change Everything
Many of the small and medium manufacturers that IMEC works with regularly are concerned about the implementation costs of AI. Predictive maintenance, however, is one of the simplest AI-powered areas of modern manufacturing, and can be implemented cost-effectively.
Vibration sensors act as a “pulse monitor” for your machines, letting you know if irregularities pop up in operation. They can often be retrofitted for very little cost, and may even be built into your machines already. Likewise, many machines are already collecting data on operating parameters and temperatures.
With AI-powered predictive maintenance to make sense of that data, here’s what happens:
- Monitoring: Sensors continuously monitor your equipment, feeding data to the central AI “brain.”
- Analysis: Machine learning applications then analyze it for patterns in real time.
- Learning: The system learns what “normal” is for each machine.
- Alerts: When sensor data changes, you get alerted, before failure occurs.
A worn bearing, for example, creates notable vibration patterns, often weeks before it finally fails. These sensors help you catch problems you can’t see, hear, or feel.
What could this look like in real life?
One mid-sized manufacturer who installed smart sensor monitoring on their most critical machines saw the following results within just six months:
- Less downtime: Reduced unplanned downtime by 40%
- Greater lifespan: Extended equipment life by catching problems early
- Cost savings: Saved 30% on maintenance costs by removing emergency fixes.
This tracks with the averages noted across the industry. Predictive maintenance offers the following average cost savings:
- Vs. reactive maintenance: Up to 40% cost savings and 50% reduction in downtime
- Vs. preventative maintenance: 8% to 12% in cost savings, and similar downtime reduction.
Getting Started with AI Predictive Maintenance in Manufacturing
Implementing predictive maintenance also doesn’t have to be a headache. You can start small, with one critical machine or production line.
This lets you gradually phase in predictive maintenance tools and spread the cost load over several budget periods. It also gives you the advantage of starting small with AI projects, letting you monitor your ROI and learn from your first AI implementation without impacting your full factory floor.
If that sounds right for you, here’s your action plan:
- Identify your pilot machine: Pick the one that hurts most when it’s down.
- Calculate downtime costs: Know what you’re spending on reactive maintenance.
- Research solutions: Look for systems designed for manufacturers of your size.
- Connect with experts: Get guidance on implementation and best practices.
This lets you trial and refine your predictive maintenance strategy in a contained way, and build as you see success.
Finding budget-friendly options for predictive maintenance
One tool manufacturers typically use to capture the data needed for predictive maintenance is vibration monitoring systems. Although the type of system you choose will depend on the specifications of your machinery, a basic vibration monitoring system has three key components:
- Wireless sensors: To attach to the equipment, typically using accelerometers. These retail for between $1,000 and $2,500 per average unit, with even more cost-effective systems on offer. You can see some available sensors from one manufacturer here, for basic comparison.
- Basic monitoring software: This is typically included with the system, or can be created bespoke.
- Alerts: Delivered to a central KPI dashboard, or sent to the mobile/desktop devices of key personnel.
Your choice of wireless sensor will be a big determinant in costs. It’s worth considering the lifetime costs of the sensor you choose as well as its initial price tag. One study, for example, found that a low-quality accelerometer will add roughly $59 per unit to the 20-year cost of purchase, while a higher-end unit could reduce that to as little as $14 per sensor.
No matter what solution you pick, you could get your pilot project off the ground for less than the cost of one unplanned breakdown.
Remember, manufacturing innovation doesn’t happen by accident; it will need some investment. But run these numbers, and you’ll likely see a compelling business case for sensor-based predictive monitoring:
- Calculate: Current unplanned downtime costs.
- Add: Emergency repair expenses.
- Include: Lost production and other hidden costs.
- Compare: With the annual cost of your predictive maintenance system.
Most manufacturers find the ROI happens within the first year to three years, with implementations on critical machines typically averaging 6 months to a year.
Beyond Vibration: Expanding Your Predictive Maintenance
Once you see results from vibration monitoring, you can expand into other areas:
- Temperature monitoring: Thermal sensors that catch overheating, before it causes damage.
- Oil analysis: Connect oil condition data with vibration patterns. This combination gives you the complete picture of your machine’s health.
These typically are built into your existing vibration sensor system. You can even shoot for AI systems that link to your quality control systems for even more in-depth monitoring.
Predictive Maintenance: Your AI Entry Point
Digital transformation doesn’t have to be dramatic or overwhelming, and predictive maintenance is a perfect example. If you’re ready for predictable maintenance costs and reduced emergency expenses, while keeping equipment availability high, AI-powered predictive maintenance is perfect for you.
Your machines are already telling you when they need help. It’s time to start listening. Make the switch from reactive to predictive, and start reaping operational efficiency rewards. The IMEC team is here to support you every step of the way. Feel free to reach out to us for help or guidance.
*This article was developed through the combined expertise of contributors from IMEC and Goodman Lantern.
