In today's economy, manufacturers strive for the highest level of equipment productivity possible, while achieving the lowest operational costs. Fortunately, open and comprehensive industrial innovation platforms (IIoT) allow us to incorporate decades of reliability and condition monitoring domain expertise along with equipment connectivity, enterprise systems connectivity, people connectivity, expert systems, machine learning, augmented reality and more.
Predictive Maintenance promises the larger returns on investment (ROI) opportunities of a digital transformation. The gap between where manufacturers are today and where new industrial innovation platforms can take us stems from the historic inability to connect systems and people together with all data sources in a single location; thus, giving way to higher visibility, machine learning, and ultimately improved insight into the best next steps and strategy that improve equipment productivity.
However, the laws of physics bounding the ability of our production equipment are not changed by IIoT applications platforms. Equipment and processes continue to fail as physical, chemical, and environmental parameters wear and change. Our equipment and processes fail for known reasons. We can map the failure modes of our equipment and processes to machinery, operating process and environmental parameters. We then detect changes in these parameters that lead to equipment and process failure. We then predict a failure, and work to prevent the failure before it occurs. We are on our way to jump start our digital transformation for predictive maintenance.
This presentation introduces a typical production environment and equipment to illustrate failure modes of equipment and process. It illustrates parameters our IIoT platform can monitor, and the decades of domain expertise can build on for immediate ROI while setting the stage for machine learning and augmented reality to join in as our digital transformation and the tools we use mature over time. Example case studies are offered to illustrate the failure mode approach.
