With design control from the tag to the software platform, AiRISTA has control of the end-to-end
solution. Many of the tags have optional sensors for temperature, humidity, motion/vibration, direction of motion, shock, and light. In addition to location, these tags collect information to drive a wide range of use cases based on conditions.
Finding a machine’s fault before it becomes critical is what AiRISTA’s condition monitoring does.
AiRISTA’s condition monitoring solution uses sensors that can detect parameters such as temperature, humidity or vibration to perform predictive maintenance on equipment.
What Is Predictive Maintenance?
Predictive maintenance is a type of monitoring system that aims to reduce the cost and loss of productivity involved with a traditional reactive maintenance solution. With this type of condition based maintenance, you can use data analysis to ensure your equipment is in peak asset form.
Whereas reactive maintenance fixes equipment after it breaks down, predictive maintenance enables you to see when a machine is at risk of failure. Resolving this after it breaks often involves large amounts of unplanned downtime and tends to be more expensive than performing predictive maintenance. This makes the latter a much more cost-efficient alternative to the former.
Potential Uses For Industrial Condition Monitoring
With design control from the tag to the software platform, AiRISTA has a full end-to-end solution. Many of the tags have an optional sensor for temperature, humidity, motion/vibration, direction of motion, shock, and light. In addition to location, these tags collect information to drive a wide range of use cases based on conditions.
The Internet of Things (IoT) is making millions of previously dumb devices visible to oversight and management. Applying an RTLS tag to an object will not only reveal its location in real time, but environmental conditions as well. You can generate alerts based on movement, light, and even direction of travel.
Reducing Accident Rates of Fork Trucks
To move carpet rolls from one process to the next, a carpet manufacturer uses a fork truck with a 21-foot boom. The introduction of this boom made navigation difficult and led to accidents, but most weren’t reported.
To drive greater accountability, manufacturers placed tags with accelerometers on the trucks. When a shock event occurred, the tags would automatically record the incident’s time and location. Businesses programmed this equipment over the air to account for selectivity levels to reduce false positives.
Predictive Maintenance of Conveyors
A broken conveyor system can bring production to a halt. Traditionally, a conveyor on the verge of failure creates noises and vibrations that seasoned staff can detect and repair before failure occurs. But with the aging workforce and high contractor turnover rate, this tribal knowledge is lost.
In its place, manufacturers place tags with a temperature and motion sensor on motors and bearings. Administrators then program thresholds into the tag over the air. The tag generates alerts when parameters exceed the provided thresholds. This is predictive maintenance in action.
Frequently Asked Questions On
Can you use a sensor to detect issues during oil analysis?
Oil analysis is a form of condition monitoring but requires special equipment and professional data analysis. Businesses commonly outsource this type of maintenance task because oil analysis can’t be performed with sensors alone.
How do condition monitoring systems reduce downtime?
Using different condition monitoring techniques, this type of preventive maintenance can check machine condition and prevent costly asset failure. In addition, administrators can use data collected from tasks such as vibration monitoring to ensure equipment is at peak asset performance.
Can you use condition data to improve equipment performance?
Yes. Condition data isn’t just to keep a machine from failing, it can also be used to ensure the equipment is operating at optimal efficiency.
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