Businesses are always searching for ways to operate more efficiently, reduce costs, and improve the performance of their assets. In the manufacturing industry, this includes taking proactive steps to keep equipment operating reliably for as long as possible. Traditionally, this has entailed following a rigid preventive maintenance schedule. However, new technologies could be making regularly scheduled maintenance a thing of the past.
Artificial intelligence (AI) is helping to create efficiencies across a wide range of industries. One way it is doing this is by transforming the practice of proactive equipment maintenance. In the past, machinery components had to be maintained and swapped out on a regular basis to avoid unexpected breakdowns. However, this method can both undertreat and overtreat problems. On one hand, parts are frequently replaced well before the end of their useful lives, and on the other, unexpected problems can arise between maintenance checks. AI now offers manufacturers a cost-saving alternative. This article will explore the predictive maintenance process and how AI is being employed to enhance equipment reliability, minimize downtime, and optimize operations.
A predictive maintenance program combines equipment monitoring with AI-enabled software. First, machines are equipped with sensors that constantly collect data in real time. The data is then fed to the AI, which uses it to provide instant insights into equipment conditions and predict potential failures. Because predictive maintenance helps manufacturers spot problems before they occur, they can avoid the twin pitfalls of preventive maintenance schedules: unnecessary maintenance and missed opportunities for prevention.
Predictive maintenance gives manufacturers a new way to improve operational efficiency and reduce costs. By leveraging data and smart technology to predict asset issues at the earliest possible stage, they can move away from a calendar-based maintenance schedule and deploy their maintenance budget more strategically. As they do so, they can begin to eliminate unnecessary maintenance costs, prevent unexpected breakdowns, and extend the life of their equipment.
AI plays a fundamental role in collecting, analyzing, and interpreting the vast amounts of data needed to conduct a successful predictive maintenance program. Without it, the data supplied by equipment sensors would be of limited use. The AI-enabled software combines this data with other information like historical records and environmental factors to shed light on operational anomalies, enabling it to identify impending issues.
Predictive maintenance can be a game changer for manufacturers. However, it’s important to be aware of potential challenges before you begin implementing a new program.
First, it’s critical to get internal teams, including maintenance employees, on board with the strategy. As new technologies arise, some resistance to change is natural. Early in the process, communicate with employees about the benefits of predictive maintenance and how it can improve their jobs. Not only will maintenance workers be relieved of unnecessary tasks, but machine operators and others in the vicinity can avoid potential injuries from malfunctioning equipment.
A common phrase used in computer science is, “garbage in, garbage out.” This concept is important when deploying any AI solution. To provide accurate insights, AI programs need reliably accurate data. Ensure that the equipment sensors you’re using are up to the job.
It’s important to invest in an AI solution that’s proven to deliver accurate predictive maintenance insights in manufacturing facilities. Take the time to research solutions, and look for companies that offer free demos of their software. Understanding how the product works and the insights it can (and can’t) provide is key to understanding its potential to generate return on your investment.
Constant collection, transmission, and analysis of large quantities of data demands a substantial amount of processing power and requires strong, reliable internet connectivity. It’s important to ensure a facility is equipped to handle these demands before investing in AI technology for predictive maintenance.
When you implement a new maintenance system, you don’t want to have to get rid of the technology you already rely on. Be sure the solution you choose integrates with the systems you use, enhancing their capabilities rather than forcing you to replace systems that are working.
In complex manufacturing facilities, MSAI Cloud conducts continuous thermal monitoring and analysis, helping maintain quality control and informing predictive maintenance decisions. Thermal cameras monitor the temperature of assets like motors, conveyors, and electrical systems and feed the information to the MSAI Cloud platform. Users can set benchmarks and KPIs to identify deviations, allowing the AI to predict problems before they occur. As a result, facilities can enhance efficiency, improve productivity, and prevent both downtime and safety risks.
MSAI Cloud is capable of processing and analyzing diverse data sources, combining sensor input with historical and environmental data to identify patterns, anomalies, and potential issues that would go unnoticed by manufacturers who rely on traditional preventive maintenance scheduling. Critically, it can be seamlessly integrated with VMS and DCS platforms, allowing you to get more from your existing technology investment.
With MSAI Cloud, manufacturers can lay the groundwork for an effective predictive maintenance program. Properly implemented, MSAI Cloud can help reduce costs, extend equipment life, improve safety, and optimize allocation of resources. MultiSensor AI's mission is to develop and manufacture the most sensitive, accurate, and competitively priced infrared cameras in the world. Learn more about us and how we can support your predictive maintenance program.