All industries aim for superior-quality products. A product is of good quality only when there is no fault or the consumer appreciates the product.
But at times, the quality of the product is degraded because of a defect in the equipment or machinery that has not been monitored or appropriately maintained. This is where the role of condition monitoring steps in and is used by most industries to understand and know the issues and problems with the equipment. There are different techniques for condition monitoring.
And now IoT condition monitoring helps enterprises reduce their manual work of detecting faults in equipment. Research shows that 64% of the unplanned downtime is because of equipment failure, which is due to improper handling and maintenance. Condition monitoring has been used by manufacturers to observe the working of the machines closely to predict the abnormalities and faults beforehand. We will now discuss how industries can effectively use IoT combined with condition monitoring.
How does IoT condition monitoring work?
Conditioning monitoring measures specific equipment parameters like temperature, vibrations, oil, and frequency of the equipment. This data is collected by the industrial machine equipped with sensors and passed on to the cloud. The software then aggregates the collected data from sensors and uses analytical tools to develop time-series sensor data into informative sights that tell about equipment health and factors.
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This solution provides the solutions and communicates them to the manufacturers in the form of graphs and charts. The data collected by the condition monitoring solution will inform us about the current state of equipment and, in turn, help in monitoring the quality of the products in production. This solution will also help in predicting the future condition of the equipment, anticipating any failure that is likely to be configured, and then finding solutions for condition-based maintenance.
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Which industries are using IoT condition monitoring or where it can be applied?
IoT Condition monitoring can be used across a wide variety of industries. Some of them are:
- In the electric power industry, condition monitoring can help power plants ensure power generation. It is used to observe the health of coal-fed turbines, wind turbines, gas turbines, nuclear power plants, etc. With the help of vibration and pulse shock sensors, the condition of turbine rotating parts and bearings can be monitored.
- In the steel industry, condition monitoring can be used to observe the state of co-rolling mills, which determines the quality of the steel. This will help the manufacturers take timely action and minimize the impact on the quality of the product.
- In the paper and pulp industry, condition monitoring is applied to monitor the condition of rolls and roll balance, which determines the quality of the paper. The data is collected by the sensors on temperature and vibration and transferred to the cloud for analysis.
- In the automotive industry, IoT condition monitoring is used to monitor the machines' condition, which allows for early detection of faults and issues.
A living example of IoT condition monitoring
IoT condition monitoring has been successfully implemented in the wind power industry, where these solutions are used for monitoring the components of wind turbines like gearboxes and rolling bearings.
The vibrators and pulse shock sensors are used to monitor the condition of the wind turbines, which are then passed on to the cloud. The data is collected and analyzed to develop sensor readings into informative insights to understand the condition of the wind turbine. The correct findings and condition of the turbine are then communicated to technicians and manufacturers.
The main aim of condition monitoring is to ensure the efficient functioning of the equipment, and that can be well achieved with simple condition monitoring.
But when combined with IoT technology, it can help save and store a large amount of data and can also monitor the condition of the equipment in isolated areas as well. They also provide a good amount of computing resources machine-learning algorithms for efficient maintenance. This helps in saving the time, money, and resources of the organisation.