The Role of AI in Total Predictive Maintenance

Artificial Intelligence platforms play a significant role when it comes to predictive maintenance in manufacturing. We already know that predictive maintenance takes a different approach to maintenance. It relies on historical and real-time data to detect issues with an asset and repair it before the situation gets any worse.
Manufacturers can use AI in predictive maintenance to efficiently assess the condition of equipment, set maintenance schedules, and prevent unnecessary machinery breakdowns. This can result in maximized output and improved product quality while keeping maintenance costs at bay.
Manufacturers can employ AI-powered predictive maintenance services across multiple verticals, production lines, and assets for lucrative results.

Below are just a few examples of how AI is proving effective when it comes to predictive maintenance services

  • Detecting initial signs of clogging in heat exchangers by assessing temperature changes between upstream and downstream flows.
  • Assessing temperature and vibration levels to monitor conditions of commercial jet engines in flight.
  • Employing vibration sensors to ferret out patterns that can help detect weak spindles in milling machines.
  • Calling specific cars for tune-up based on vehicle performance data and information collected by sensors on them.

Data and AI in Asset Management

As we mentioned before, predictive maintenance takes a more analytical and data-driven approach to maintenance. The method relies on real-time data gathered while the asset is still in operation. This is made possible with the implementation of AI-driven technologies like IoT (Internet of Things) devices, which play a very important role in defining a predictive maintenance strategy and thus facilitating efficient asset management.

IoT devices are known to use condition-monitoring sensors to collect the necessary data. The data is then analyzed, shared, and processed with the help of advanced machine-learning technology. That being said, it is important to note that the type of information gathered will largely depend on the type of IoT device or AI tech you use.

For instance, tech like Infrared Cameras can be used by technicians to detect high temperatures in components such as electrical wiring. Acoustic Analysis, on the other hand, can be used to identify gas, liquid, or vacuum leaks before it’s too late.

Asset Management and Predictive Maintenance Strategy

Devising a strong predictive maintenance and asset management strategy has to involve a lot of planning and due diligence. In hindsight, we believe that any substantial strategy pertaining to the asset management process should involve the following steps

i) Identifying Priority Assets

The very first step in your asset management process has to be identifying assets that are critical to your business. We suggest referring to past breakdown reports and other records to detect assets with high repair costs.

ii) Setting Condition Baselines

It is important to set baselines (pertaining to asset performance and conditions) before you deploy predictive maintenance.

iii) Install Sensors, IoT, etc.

Once you’ve set baselines, you can proceed to install sensors and IoT devices that can meet those baselines.

iv) Connecting to CMMS

The IoT and sensors installed need to be connected to a CMMS tool to monitor an asset’s performance in real-time and collect data.

v) Schedule Maintenance

It is finally time to launch your predictive maintenance program with everything in place. It is wise to run tests on one or two assets before deploying the program on a wider scale.

Benefits of AI-Driven Asset Management and Predictive Maintenance

There are many benefits to employing AI for asset management. The most prominent of which are listed below.

  • Saving time on maintenance.
  • Preventing unplanned downtimes associated with mission-critical assets.
  • Drastically reduced breakdown of machines.
  • Increasing the longevity of manufacturing equipment by 20-40%.
  • Minimized stock of spare parts.
  • Expenses related to spare parts, equipment, and labor reduced considerably.
  • A safer work environment for technicians and operators.

Needless to say, AI-driven predictive maintenance can play a significant role in enhancing the efficiency of asset management. If you wish to deploy predictive maintenance to experience a streamlined asset management process, we suggest you give us at DT4o a call right away.