With the advent of Artificial intelligence and the Industrial Internet of Things (IoT), manufacturing businesses are reimagining their technological landscape by leveraging the power of data to predict the future and prevent problems from arising. Maintenance is one of the key areas that industries must look into to increase production value and reduce expenditures. According to a McKinsey research report, predictive maintenance can reduce machine downtime costs by 30-50% and increase machine longevity by 20-40%.
Thus, no doubt, transforming your maintenance process by making the best use of data will provide optimum operational efficiency. While most manufacturers have already implemented some form of predictive or preventive maintenance, the rise of AI and machine learning has provided an unprecedented scope to process massive amounts of data with speed and accuracy to usher in a new era of productivity.
You might be thinking: How do I implement an AI solution into my current maintenance process and optimize it? Let’s take a look at the common types of maintenance and what role AI plays in each of them to answer your question.
The Role of AI in Different Types of Maintenance
Total Productive Maintenance (TPM)
TPM is a holistic approach taken to maintain the critical assets of the production process to prevent machinery breakdowns, avoid unplanned downtimes, increase productivity and improve safety. This method was developed in the 1960s when enterprises maintained historical data timetables to conduct timely maintenance. This method employs the concept of planned maintenance to improve overall equipment efficiency (OEE) to avoid sudden machinery breakdowns and improve asset health and the plant’s overall productivity.
Role of AI in Autonomous TPM
Autonomous maintenance (AM) was one of the core features of TPM, which holds machine operators responsible for machine upkeep instead of employing technicians for machinery repair and maintenance. But, traditionally, AM was difficult to implement because the operators did not have complete historical machine knowledge and lacked the foresight to predict any machine breakdowns.
Businesses can leverage the capabilities of AI-driven tools to make autonomous maintenance easier. Implementing an effective AI solution with easy-to-access dashboards will provide real-time historical data insights to the machine operators and enable them to conduct timely servicing. Thus, by automating the entire process, businesses can ensure the operators have the right tool for regular maintenance needs and free up technicians to focus on other critical tasks such as machine reliability and larger adjustments.
Planned Preventive Maintenance (PPM)
PPM can be described as planned maintenance driven by time or an event that necessitates maintenance and repair. Thus, the maintenance or repair is scheduled when the machines are still fully operational to prevent unplanned downtime and optimize the lifespan of the equipment. This method may be effective, but it has some drawbacks, such as you run the risk of over-maintaining or under-maintaining your assets as it is based on routine checkup guidelines, which are not improvised according to specific machinery requirements.
Role of AI in PPM
PPM is mostly driven by time-based or event-based data. For instance, if a certain amount of time has passed or you have reached the mileage bandwidth for your car, routine servicing needs to be performed as suggested by historical data. However, most maintenance technologies only transport this data, but by incorporating an AI solution that uses machine learning algorithms, you can aggregate the data into real-time analytics and make use of it faster. This is what matters- sending the data is just the first step. What actions you perform next based on the insights is more important.
Predictive Maintenance (PM)
This condition-based maintenance approach optimizes the maintenance cadence and maximizes equipment availability. For example, a car will provide an alert when its engine is overheated outside of the planned preventive maintenance schedule. This type of maintenance is warranted when your machinery is still operational but is running a potential risk of failure.
Role of AI in PM
Leveraging AI and predictive maintenance software, it is easier to access various historical maintenance records, sensor data, and weather data and translate data into meaningful insights while avoiding data overloads. Machine learning models can help you extract unstructured data quickly and use it best. In addition, AI and predictive analytics software upgrade your existing maintenance process and ensure your employees have the right knowledge and tools to maintain the peak performance of your mission-critical assets.
Quality AI-Powered Industrial IoT Maintenance Solution from Tredence
As digitization continues to dominate the market, adopting new technologies is no longer a luxury but a necessity to reduce downtime, improve safety, increase productivity and remain competitive. Tredence industrial IoT platform uses AI and predictive analytics to predict failures before they occur, allowing manufacturers to manage problems before they arise. In addition, we analyze your budget and business requirements to prevent unplanned downtime, lower costs, or expedite repairs to provide you with customizable software solutions that optimize your preventive maintenance needs.