Running a manufacturing unit is challenging due to various external and internal factors. While companies invest in the efficiency of their staff through corporate training, they also need to enhance the efficiency of machines. Therefore, production becomes static at a certain saturation point. Nevertheless, production needs to be improved due to the wear and tear of the machines.
The introduction of predictive maintenance and condition monitoring has changed the scenario drastically, as manufacturing units can now predict the maintenance and repair required for a production unit. According to the reports, the global predictive maintenance market size was 7.3 billion USD in 2022. But, the market size will be around 64.3 billion USD by 2030. So, the huge growth in market size suggests that IoT predictive maintenance and condition monitoring is the next big thing.
Businesses should partner with professional and reliable companies to integrate predictive maintenance and condition monitoring solutions. The experts suggest that predictive maintenance will undergo various changes due to emerging technologies. In the following section, you can find a guide on the upcoming trends in predictive maintenance.
1. Artificial Intelligence
PDM maintenance has started evolving with artificial intelligence. Artificial intelligence (AI), machine learning, and deep learning technologies aim to run specific industrial tasks without human intervention. The AI applications will collect data from different sources and run data analysis simultaneously. As a result, the machine will become self-sufficient to predict its maintenance and repair.
AI-driven predictive maintenance and condition monitoring will be more cost-effective for businesses. Since no humans are involved in data collection and analysis, the process will be cheaper. On the other hand, predictions for maintenance and repair will be more accurate due to machine intelligence.
2. IoT-Driven Predictive Maintenance
One can use IoT predictive maintenance in different ways to establish more efficient predictive maintenance. For example, many businesses have multiple assets, and monitoring the predictive maintenance of those assets is challenging. The job will be much easier when you have a central platform to track and watch the predictive maintenance of all machines.
The Internet of Things (IoT) collects data from different sources and interprets data to create structured analytics. For example, smart sensors collect information such as supply voltage, operating temperature, vibration, etc. IoT-driven applications collect such data and create analytics and insights. Both human operators and machine intelligence can interpret data and make maintenance decisions accordingly.
3. Advanced Inspection Technologies
Condition monitoring maintenance is an integral part of predictive maintenance. Condition monitoring tries to keep machines from breaking down so that a factory can make things without stopping. Introducing advanced inspection technologies can improve the accuracy of preventing machine failure.
Nowadays, predictive maintenance integrates robotic inspection to improve condition monitoring efficiency. Instead of humans, robots inspect and assess the machine’s condition. On the other hand, ultrasonic analysis has also become an advanced inspection technology. The ultrasonic analysis helps identify problems with a machine’s internal parts. The technology can check the condition of even the most fragile parts and give accurate data.
4. Predictive Analytics
The production units are moving from preventative maintenance to predictive maintenance. However, separating preventive maintenance and predictive maintenance often requires more work. Preventative maintenance often becomes a part of predictive maintenance, though preventative maintenance leads to a higher expense for businesses.
PDM solutions are the next big thing in predictive maintenance, as these analytics-driven solutions analyze unstructured data and convert it into structured data. Robust data analytics makes predictive maintenance systems more cost-effective by eliminating preventative maintenance. Therefore, a production unit will only invest in maintenance where it is required. Preventative maintenance proves unnecessary in various scenarios.
5. Digital Twins
The digital twin is one of the trending predictive maintenance technologies. The way we track the changes in industrial equipment could be better with the technology we have now. The digital twin can help businesses overcome such challenges by creating a virtual replica of the physical equipment. As a result, the operators can manage the machines from the virtual platforms. Monitoring the machines from the virtual platforms and scheduling maintenance will save time and hassles.
So, these are the next big things you can expect to see in the predictive maintenance industry 4.0 in the future. Overall, the next-level technologies will be more data-driven to establish automation to eliminate human intervention.