How can you implement a predictive maintenance program with IoT sensors and analytics?

Choosing the right sensor is one of the key elements as the actual physical events occur near the sensor and from here these metrics are taken forward. Transducers are closest to the origin and from there the data goes right up to the cloud crossing several boundaries and processors and therefore the right technology helps retain the critical data with precision. The metrics could be analogue or digital. The different technologies to consider will depend on accuracy, response time, physical and cosntructional aspects. Thereafter the conversion of the metrics in a suitable form is needed to transmit the metrics and further process it. In this part different technologies to consider may be 4-20ma loop, MODBUS, OPC-UA, MQTT etc.

I would use an existing known platform Leading2Lean. This has worked great for me in the past and has significantly more capability than just a CMMC.

This part relates to how the different types of sensors are connected to the processors. As there are multiple technologies and in a real installation, it can be a mix of technologies creating a hetrogeneous system, it will certainly be needed to do some sort of conversion of different sensor communication methods to a common platform. The units which do such functions are called gateways and in real installation multiple such gateways may be required for the entire sensor network communicate to the system in a commonly understandable language by the central processing system, usually an edge server.

In addition to analytics, it is very useful to design and develop aesthetic, intutive and purposeful dashboards. The responsive dashboard may be created using many popular software tools and would include critical metrics grouped as per functionality and criticality. Useful dashboards may display and report instant metrics, historical charts and reports and statistically aggregated and inter related metrics together. This would enable the users to get a quick view of instantaneous parameters as well as computed and aggregated historical data and trends. The visuals will help decision making in an intutive way.

To enact a robust predictive maintenance program with IoT sensors and analytics, continual assessment is vital. Initiate by evaluating key performance indicators (KPIs) such as Backlog, MTBF, MTTR, Machine Downtime, Maintenance Cost as a percent of Estimated Replacement Value (MC/ERV), availability, reliability, efficiency, quality, and cost savings. KPIs serve as the linchpin for maintenance teams, offering a comprehensive understanding of routine effectiveness, processes, and equipment functionality. Identifying the most relevant KPIs allows teams to optimize their focus, enhancing production efficiency by concentrating on impactful metrics.