Predictive Diagnostics of Tube Mill and Roll-Forming Equipment
Project Lead: Case Western Reserve University
Partners: Rafter Total Mill Solutions, Banner, Atlas Steel, Tenneco
Member % Cost Share: 50%
CESMII % Cost Share: 50%
Duration: 6 Months
Mill systems are not operating efficiently due to lack of preventative maintenance and emergency downtime.
Increase efficiency by minimizing emergency downtime and creating targeted preventative maintenance through the use of a sensor network and algorithms that track the vibration signature of bearings at key mill locations to detect incipient bearing faults, diagnosis these faults and provide prognostic indicators and alerts to mill personnel that can assist in minimizing downtime.
Data-driven algorithms that enable the incipient detection, fault tracking and prognostics will make mill personnel aware of maintenance requirements to ensure that the necessary parts are available and maintenance can be schedule before a catastrophic failure to reduce impact on production and further damage to the mill. The monitoring system developed will provide reports showing potential areas of failure and recommended responses maximizing uptime; a targeted approach to preventative maintenance minimizing unnecessary part replacement and maintenance tasks.
- Complete vibration dataset collected from Rafter (industrial partner) production equipment for model development and validation
- Developed predictive model for vibration monitoring and fault detection
- Implemented fault detection algorithm on the Smart Manufacturing Innovation Platform
- Smart Mill Monitoring App for rotary equipment
The mill monitoring system will provide end users the capability to pinpoint maintenance problems and address them prior to a catastrophic failure helping to maximize uptime. The monitoring system can track the life expectancy of bearings and other consumable parts and reduce part replacements. The data from the monitor can be used to develop proactive maintenance programs and increase operational efficiency. The end users will benefit from the efficiency increases from minimizing downtime and replacement parts cost. The industry can benefit from the analytics of mill run time, motor loads and vibration of equipment to manufacture more efficient solutions in the future.
Rotating machines are essential equipment to most manufacturing process and the ability to use the SMIP for predictive diagnostics will create value for CESMII and the CESMII members.