Using Modelling and Simulation as an Alternative to Big Data
RRAMAC Connected Systems will present on big data, machine learning and simulation based approaches to predictive maintenance at the 2019 AI Manufacturing Conference on Wednesday, August 28 at 1:35pm. As a provider of Industrial Internet of Things (IoT) solutions including predictive maintenance, RRAMAC Connected Systems is sharing their expertise on the advantages and disadvantages to the array of predictive maintenance approaches.
Big data and machine learning is very good at finding correlations between large numbers of sensors and equipment breakdowns or quality issues. This is incredibly valuable if you have a very complex machine where the reasons for a failure are difficult to identify.
The big data and machine learning approach to predictive maintenance looks like this:
- Install lots of sensors to learn as much as possible about your equipment
- Collect massive amounts of data for a long period of time
- Run computer algorithms that correlate the sensor data to machine failures
- Use these results to predict future failures
The up-front costs of this approach is high, the return on investment is slow, and few companies are willing to suffer through the time and expense of numerous failures in order for the machine learning algorithm to learn the leading indicators. A faster and more cost effective approach is to use a simulation model for your equipment. Chances are you are not the first company trying to avoid downtime on a pumping system, assembly line, extruder, etc. , so there is no need to wait for a machine learning algorithm to derive the correlations between the sensor data and the eventually failures.
The Simulation Approach to Predictive Maintenance
Simulation models combine modelling algorithms with specific application knowledge in order to predict failures or quality defects. This approach requires fewer sensors and can be fully implemented in weeks rather than months or years. The predicted failures are calculated from day 1 with increasing accuracy over time. The lower cost and fast results provide an extremely compelling return on investment for industrial equipment manufacturers as well as the end users.
This AI Manufacturing Session by RRAMAC Connected Systems will cover:
- Approaches to predictive maintenance (machine learning, simulation models, and rule based models)
- Advantages, disadvantages, and ROI for each approach
- Case studies
- Recommendations for getting started
- And more
AI Manufacturing 2019 will be held August 28-29 in Rosemont, Illinois. The conference is designed for CEOs, CTOs, and plant managers interested in integrating/expanding Machine Learning, AI and IoT technology into their automation or manufacturing systems. Presentations and panel discussions will discuss return on investments, case studies, strategies for integration, technology and manufacturing trends. The conference will provide opportunities to network with AI and Machine Learning experts, as well as manufacturers who are already using Smart Manufacturing solutions. Register to attend and view the program agenda on the AI Manufacturing Conference website. To learn more about the Industrial IoT solutions RRAMAC Connected Systems provides, contact us today.