As technology keeps evolving in the manufacturing industry, businesses introduce new processes to reduce costs, increase performance, and boost efficiency. The current industrial revolution dubbed Industry 4.0 rests on advanced technologies such as artificial intelligence (AI), machine learning (ML), the Internet of Things (IoT), and others that all work together to redefine existing manufacturing standards.
When these technologies are put together, they provide all kinds of insights that improve management, production, and maintenance. Predictive maintenance is one of the most revolutionary advancements that offer significant benefits compared to traditional maintenance techniques. Let's see how it works and what benefits it provides.
Predictive Maintenance Explained
Predictive maintenance is a modern maintenance strategy that uses real-time operational data to predict when an asset or piece of machinery needs repairs before breaking down completely. It results from modern technologies connected to a single AI-powered system that monitors all assets and determines when they need maintenance. The use of AI in the industry is spreading by the day as more and more companies adopt AI solutions.
The system gets information from tiny IoT sensors placed all over the production plant. These sensors generate operational data and details such as vibrations, temperature, production, etc. The system can quickly pick up warning signs of faulty machinery and inform the floor manager that it needs maintenance. Predictive maintenance is multiple times more efficient than traditional reactive and planned maintenance techniques because it's followed by short downtimes and a significant reduction in repair costs.
The Goal of Predictive Maintenance
Predictive maintenance is conducted and executed by AI to improve productivity and efficiency. For example, since the AI can predict when a machine will break down, it can plan maintenance efforts where they are needed, effectively reducing long-term repair costs. The goal is to reduce the frequency of maintenance efforts and repair only those assets that stopped working as intended.
The second goal of predictive maintenance is to reduce unexpected breakdowns and maintenance downtimes. Compared to traditional maintenance methods such as reactive and planned maintenance, preventive maintenance is much more accurate. Moreover, this type of maintenance also reduces spare part costs, as the AI in industry can tell the manager which part needs to repair.
Predictive Maintenance VS Preventive Maintenance
The use of AI in industry is changing most manufacturing processes completely. The same can be said about predictive maintenance. However, many people confuse predictive maintenance with preventive maintenance, which is a different type of maintenance. While they share the same goals, predictive maintenance is better at identifying problems.
Preventive maintenance is performed at certain times. For example, if a machine tends to break down after 10,000 hours, preventative maintenance is completed when the machine reaches the 9,000-hour mark. All maintenance efforts are on a set schedule. Even if the machines are still working without any issues, the maintenance is performed.
On the other hand, predictive maintenance uses advanced technologies to assess the system and pinpoint the machines that require repairs. All of that is possible only because of IoT sensors and a centralized management system with AI capabilities. The system simply identifies the problem and alerts the repair crew before a machine breaks down. Also, preventive maintenance comes with higher labor costs, while predictive maintenance requires an initial investment that pays off over time.
Benefits of Predictive Maintenance
We've touched on the benefits of predictive maintenance above, but let's look at them in more detail. One thing is for sure, the use of AI in industry has led to the development of accurate maintenance predictions that help optimize manufacturing on various levels. Here are some of the benefits of predictive maintenance:
Reduced Maintenance Costs
Since predictive maintenance uses real-world data to identify faulty machinery, it takes all the guesswork out of the equation. Instead of hiring a repair crew to take everything apart and put it back together, the AI-driven system can quickly identify which parts need to be replaced before it causes further damage. That leads to a significant reduction in repair time and downtime as well.
No Surprise Malfunctions
The use of AI in industry is finding an application in many different areas, as it can learn from any data type. Once it knows how an operation work, it can identify areas that need improvement. This approach removes the need for reactive maintenance repairs, as the managers will always know which machine is not working correctly before it breaks down.
Maximized Equipment Lifespan
Planned or preventive maintenance often includes replacing parts that still work to prevent them from breaking down in the future simply. But unfortunately, that means that manufacturers lose value on operating equipment. AI in industry can also help increase the lifespan of all machines and parts through close performance monitoring. That way, you can get the most out of every aspect without worrying that it will stop working.
Increased Revenues
Even though predictive maintenance requires a significant investment upfront, it can help you save a lot of money by protecting your equipment and ensuring that it's always in perfect working condition.
Types of Predictive Maintenance
Predictive maintenance can be applied to different processes. The goal always stays the same, but there are multiple types of predictive maintenance, so let's go over them in more detail:
Vibration Analysis
Vibration analysis is a method that monitors changes in a machine's vibrations. The AI solution sets normal vibration levels and alerts the managers only if a machine deviates from the regular levels.
Acoustic Monitoring
AI in industry has reached a point where it can use sound to identify malfunctions. Like an experienced worker, the AI can predict which machine has a problem by listening to its inner works. However, this method is impossible if the surrounding area is loud and full of background noise.
Infrared Technology
AI systems can also predict which machines are faulty by checking their temperature. If the operating conditions are not ideal, the system can identify hot spots in electronic equipment, determine the fuses under stress, pinpoint faulty electrical circuits, etc.
Oil Analysis
Oil analysis is a process where machine oil is extracted from a machine and tested for wear particles, the presence of water, and viscosity. This type of predictive maintenance is usually used in the transportation industry.
Motor Circuit Analysis
Motor circuit analysis is a method used across multiple industries, including the automotive and the marine industry. It involves monitoring a motor's stator and rotor to detect ground faults or contamination.
Example of Predictive Maintenance
Here's an example of how predictive maintenance works.
Let's say that you own a coal preparation plant. The centrifugal pump motor is one of the essential pieces of equipment. Unfortunately, it's a large machine that performs heavy rotations, so it's hard to identify a problem. The best approach is monitoring its vibrations and looking for changes as the machine is turned on.
After the machine is fitted with vibration sensors that establish a regular baseline measurement, the sensors pick up a spike in acceleration. The system quickly informs the repair crew about the change. The maintenance team schedules an inspection, and the technician soon identifies the problem in the form of a loose ball bearing. The repair takes only a few minutes, and the machine goes back to work.
The system is always connected to the internet, allowing team members to monitor vibrations in the future remotely. The system informs the repair crew to perform the needed repairs whenever a change is detected.
Conclusion
Predictive maintenance is a truly revolutionary maintenance method, but it's not for everyone. The use of AI in industry is still reserved only for the largest enterprises, as the technology and implementation are still pricey.
However, as the technology becomes widely available, it will become more affordable. It will soon find its way into small and medium-sized businesses looking to reduce operating costs and increase overall profits.
