Condition-Monitoring Trends for 2024
As modern-day manufacturing practices continue to evolve, it’s increasingly driven by advanced methodologies and technologies. Where concepts like reliability and efficiency are concerned, condition monitoring is the prevailing trend. The ability to home in on specific variables and markers of equipment performance to detect anomalies has made it possible to prevent problems before they arise.
As more and more manufacturers shift perspective to preventive and proactive maintenance strategies, condition monitoring is creating a future where downtime is minimized and productivity is maximized. And the technologies for condition monitoring are only improving.
IIoT technology is enabling powerful condition monitoring
The advent of IIoT (industrial Internet of Things) technology has become a cornerstone of condition monitoring. By interconnecting sensors, devices, and machinery, IIoT facilitates an unprecedented level of data collection and analysis. This continuous flow of information allows for real-time insights into equipment health, which enable quicker decision-making and more efficient operations.
IIoT sensors can detect heat thresholds in an electric motor and alert operators to deviations beyond control levels before the equipment overheats or burns out. By acting before the problem escalates, manufacturers can prevent prolonged downtime, improve equipment management, and safeguard workers. This use case — and countless others — are possible only through condition monitoring.
2024’s most anticipated trends in condition monitoring
As we usher in another year of manufacturing innovation, here’s a look at the most anticipated condition-monitoring trends that will rise to prominence in factories working to improve reliability:
1. AI/ML for preventive and predictive maintenance
Artificial intelligence (AI) and machine learning (ML) are at the forefront of the predictive-maintenance revolution. These technologies analyze historical and real-time data to forecast equipment failures, allowing maintenance to be scheduled at the most opportune times. In the energy sector, AI algorithms are already predicting turbine maintenance needs, reducing downtime, and increasing energy production efficiency.
2. Edge computing for rapid alerting
Edge computing brings data processing closer to the source of data generation. In condition monitoring, this means quicker response times to potential issues. By processing data on-site rather than in a distant data center, edge computing enables immediate alerts when anomalies are detected. In critical environments like chemical plants, this rapid alerting can prevent catastrophic events.
3. Enhanced IIoT integrations
The seamless integration of IIoT devices is leading to automated adjustments and calibration in machinery. This automation ensures machines are always operating at optimal settings, reducing the likelihood of problems caused by out-of-tolerance settings. In precision manufacturing, automated calibration keeps tools within stringent tolerances to reduce unnecessary wear and tear that might cause premature failures.
4. Operator-driven reliability
Despite the rise of automation, the human element remains vital. Operator-driven reliability emphasizes the importance of skilled operators in conjunction with advanced monitoring tools. By equipping operators with the right technology and training, manufacturers are seeing a marked improvement in equipment handling and decision-making.
Better condition monitoring means better reliability
These up-and-coming trends in condition monitoring illustrate a clear trajectory toward more intelligent, more efficient, and more operator-friendly monitoring solutions. They mark a leap in technological capability and catalyze a shift in how we approach maintenance and operation — a shift bound to redefine benchmarks for efficacy, reliability, and predictability.
