With technological developments that have steadily improved over the past 50 years, manufacturers can now collect historical and real-time data from segments of their operation and analyze it to anticipate problems before they happen. This predictive process can detect anomalies in a manufacturing operation, as well as possible defects in equipment and processes, and alert the maintenance crew so fixes can be made before issues result in unexpected failure and production downtime.
For this predictive maintenance process, an asset’s status, performance and “health” are tracked. The main considerations include real-time monitoring of equipment condition and performance, analysis of work orders, and criteria of maintenance, repair and operations. A maintenance team can use predictive maintenance tools and asset management systems to monitor approaching tasks and potential equipment breakdown. They can then schedule maintenance around the production schedule.
Software, operational technology and information technology are intrinsic components in designing a predictive maintenance model.
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When equipment and systems are integrated, Industrial Internet of Things (IIoT) sensors and artificial learning can connect, communicate, share, and use smart algorithms to analyze data. The sensors, industrial controls and resource planning software can capture information, decipher it and use it to identify things that need attention. Examples are sound analysis and lubrication; vibration, imbalance analyses and bearing speed; thermal imaging, airflow and cooling.
When abnormalities are detected, it can signal that a part is under stress before it actually fails. The maintenance team then has time to address the issue and avoid an unplanned halt in production.
Predictive maintenance is well suited to processes and equipment that are critical to operations and have a predictable failure mode. In a predictive strategy, maintenance is only performed on machines just before failure is likely to occur. On the other hand, in a preventive model, parts might be changed out on a set schedule based on their estimated lifecycle, regardless of usage, ultimately resulting in higher parts costs.
With the help of a product like Autodesk Fusion 360, predictive maintenance can result in:
- Faster problem resolution.
- Less downtime.
- Reduced maintenance costs.
- Improved safety by fixing problems prior to malfunction or failure.
To develop new strategies for bringing intelligence to factories and enabling preventive maintenance, Autodesk is collaborating with Georgia Tech on an initiative to connect and monitor machine tools with IIOT devices. The study includes implementing sensing devices to detect and measure machine behavior. The devices will collect and stream real-time information to online databases, where it can be analyzed and acted upon.
Eventually predictive maintenance will see lower cost techniques for condition monitoring with more personnel who are experienced in data analysis. Today, the return on investment pays off by enabling maintenance to be performed only when required, helping manufacturers cut costs, save time and maximize resources.
Manufacturers don’t have to wait until after machines go down to fix them. Being proactive with predictive maintenance can optimize workforce and production efficiency.
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