Asset Performance Management
Assembly Line
Industrial Asset Performance Management has a data problem
Historically, the solution for solving Asset Performance Management use cases has been to invest in siloed systems (e.g., ERP systems), niche solutions (e.g., IoT Applications), or run multi-year “lighthouse” projects with little-to-no ROI to show after months or years of data wrangling and deployment effort. If it takes two years to deploy an APM application at your lighthouse facility, do you really have 100 years to wait until the same solution is deployed across the remaining 50 sites?
When people are spending 90% of their time searching, preparing, and governing data, there’s little time left to invest in gaining better, data-driven insights. Without a coordinated and collaborative data management strategy, APM initiatives will continue to fall short of their ROI expectations.
Eliminate blind spots with asset monitoring across the plant
With technology-driven insights that don’t put pressure on headcount, plant managers can look to scale asset monitoring goals beyond critical assets to see machine health for all plant assets.
How to Use Data in a Predictive Maintenance Strategy
Free-Text and label correction engines are a solution to clean up missing or inconsistent work order and parts order data. Pattern recognition algorithms can replace missing items such as funding center codes. They also fix work order (WO) descriptions to match the work actually performed. This can often yield a 15% shift in root cause binning over non-corrected WO and parts data.
With programmable logic controller-generated threshold alarms (like an alarm that is generated when a single sensor exceeds a static value), “nuisance” alarms are often generated and then ignored. These false alarms quickly degrade the culture of an operating staff as their focus is shifted away from finding the underlying problem that is causing the alarm. In time, these distractions threaten the health of the equipment, as teams focus on making the alarm stop rather than addressing the issue.
Industry 4.0 and the pursuit of resiliency
There are two parts to the Zero D story. Visual inspection and asset performance management (APM). Visual inspection uses computer vision models focused on quality inspection. APM uses machine learning models based on time series data to determine health of assets and probable failures in the future. Toyota is using Maximo Visual Inspection, and now they are also using the Maximo Asset Performance Management (APM) suite. They tested Maximo APM on some of their machinery that does liquid cooling and found that was another problem area for them. By implementing the software into this pilot, they are now able to monitor the asset health 24×7 and predict probability of failure in the future.