How AI Is Used In Predictive Maintenance

Serious equipment failures throw your production schedules out of joint and may require expensive repairs. In the worst-case scenario, you’re looking at complete and very costly replacements. One way to prevent this is by implementing AI in predictive maintenance so you can address emerging issues proactively. By incorporating AI and machine learning (ML) into maintenance procedures, you can leverage both historical and real-time data on machine performance. This allows you to anticipate potential failures and fix them before they occur so the overall risk is mitigated. 

Used for predictive maintenance, ML and AI analysis of performance data offers actionable insights, giving you the power to predict and avoid equipment failure. This will ultimately have a positive impact on product quality and increase productivity. AI in predictive maintenance offers cost and energy savings, along with longer equipment life, increased reliability, and greater customer satisfaction. 

Predictive Or Prevent(at)ive Maintenance?

You might find some overlap between the terms predictive and preventive or preventative maintenance. All are proactive strategies implemented by service teams and essentially boil down to fixing stuff before it breaks down. 

Preventive Maintenance 

The traditional preventive approach relies on the historical accumulation of human knowledge and experience. You conduct regularly scheduled inspections and corrective actions such as repair and replacements. You consult manufacturers’ recommendations to help identify regular maintenance tasks and their frequency and set up inspection schedules. This system reduces the risk of unexpected failure, but it’s a generalised prevention approach.

Predictive Maintenance

Predictive strategies leverage advanced technology to forecast potential breakdowns using sensor arrays and data analytics. This combination produces an AI prediction of when failures are likely to occur, even if you haven’t yet detected any obvious operational issues. AI tools compare real-time machine performance with baseline data, so they can spot even a minute lessening of efficiency. The monitoring and analysis of real-time data can identify anomalies and underlying process patterns that could indicate an impending failure. Advanced analytics may also suggest what you can do about it, outside the boundaries of your normal maintenance schedules.

Predictive maintenance techniques include condition-based monitoring, where a continuous stream of data is collected by IIoT devices and sensors. It includes an array of machine performance indicators such as fluid levels, pressure, vibration and temperature, which help determine equipment status and performance. All this is analysed by ML algorithms to identify potential maintenance issues. They are also capable of enhancing production outcomes by identifying areas where specific improvements would benefit.

How You Can Use AI To Improve Maintenance

There are various ways in which you can use AI to enhance your maintenance effectiveness:

Planned Preventive Maintenance (PPM)

PPM is another term for standard scheduled maintenance that keeps your equipment running smoothly and prolongs its life. While this can be done in the traditional way, AI tools can vastly upgrade the focus of your efforts. Its advanced analytical algorithms can pinpoint and prioritise the most critical areas of maintenance to optimise your overall equipment effectiveness. It can also monitor and assess performance over time, identifying patterns which suggest the most likely occurrence of equipment failure. This enables teams to come up with targeted PPM schedules that optimise the maintenance process.

Total Productive Maintenance (TPM)

If you then make those AI tools and the accumulated maintenance data available to all staff on demand, you get TPM. This is a strategy for improving operations holistically by involving the whole business in maintenance to expand the scope of problem detection. It includes not only engineers and equipment operators but also managers and front-line staff. 

Predictive Maintenance

The natural extension of PPM and TPM is AI-based predictive maintenance. In addition to looking at past and present experiences to inform ongoing maintenance activity, predictive maintenance looks into the future. This means you can be proactive in your maintenance strategies to reduce the potential impact of issues that haven’t yet happened. You can also leverage real-time data and predictive analytics to optimise existing maintenance schedules and allocation of resources. You’ll be able to do more with fewer resources, thus reducing labour costs, enhancing service teams' productivity and improving operational efficiency. 

Benefits Of AI In Predictive Maintenance

Implementing AI in predictive maintenance offers several benefits for the industry, including improved asset reliability, reduced costs and enhanced operational efficiency. 

Manufacturing

AI reduces costly downtime by helping to predict equipment failures. Continuous production is ensured by the real-time monitoring of machinery health, where it can pick up minute fluctuations in temperature, pressure, vibration, etc.

Transportation

AI-based predictive maintenance can use its data to help with scheduling activities in transportation and logistics, improving safety and reducing delays. Road, rail, air and sea transport performance can all be optimised.

Vehicle and Fleet Management

Predicting maintenance requirements with AI can improve vehicle and fleet performance, minimising breakdowns and extending vehicle lifespan. It monitors critical parameters like tyre pressures, fluid levels and engine performance.

Electronic Systems

AI-based predictive maintenance is advantageous in managing high-tech electronic equipment like data centres and servers. Its continuous monitoring can prevent issues such as overheating that might otherwise result in system failures or data loss.

Energy and Utilities

Predictive maintenance with AI can help detect potential failures in critical assets such as transformers and turbines. This ensures that systems for generating, transmitting and distributing power remain stable and efficient.

Healthcare

AI-based predictive maintenance helps to ensure that critical medical equipment is reliable and safe. It’s used for things like ventilators and MRI machines, safeguarding equipment against unexpected failures that could have a serious impact on patient care.

Staying Ahead of The Issues With AI

AI is becoming a part of many day-to-day processes, both in the home and at work. For business owners, it’s taking the principles of Industry 4.0 to the next technological level, solving many operational issues, and informing them of our decisions. The great advantage of using AI for predictive maintenance is the optimisation of productivity. Adopting proactive maintenance strategies allows you to stay ahead of disruptive issues, reduce overall expenditure, and improve your operational efficiency. By eliminating unnecessary maintenance activities, you’ll also avoid costly unplanned downtime while extending the lifespan of equipment.