The main reason for applying IOT to manage
your assets is predictive maintenance. Rather than performing routine
calendar-based inspections and component replacement, predictive techniques
monitor equipment for pending failures and notify you when a part replacement
is required. Sensors embedded in equipment check for abnormal conditions and
trigger work orders when safe operating limits are breeched.
When a predictive maintenance strategy is working effectively, maintenance is only performed on machines when it is required, thus reducing the parts and labor costs associated with replacements. With more and more systems shipping with Internet connectivity, the concept of predictive maintenance is likely to expand exponentially in the Internet of things.
So how does data fit into the equation? Examined in our last post, while software, machine-to-machine learning and other technologies work together to analyze data from physical objects – the sensors are key to gathering the information. If software is the brains of the IIoT, sensors are the nervous system collecting continuous streams of data to be processed. Industrial systems rely on sensors for reliable, consistent and accurate data in all aspects of automation. One could even argue the IIoT is nothing without sensors to measure strain such as temperature, position, and pressure.
Specifically pertaining to maintenance, it is the data from sensors that consistently monitor machine conditions that enable predictive maintenance. Automation allows patterns to be found in the data sets that in turn, picks up on and indicates any possible error or fault before an issue actually occurs. This allows any potential error or issue to resolved early and puts corrective measures in place before failure. It also cuts down on unplanned downtimes, keeps staff safe and ensures that resources are utilized as effectively as possible.
Why Predictive Maintenance and Service?
Because the IoT solution uses real-time machine data and sophisticated analytics to determine equipment health – so you can predict and prevent failures. Asset manufacturers can dramatically improve customer service, and operators can maximise equipment uptime.