Thesis Details

Thesis Title: Predictive maintenance using vibration analysis for Schlafhorst 138 winders
Thesis Author: Kevin Hill
Abstract: With increasing costs and speeds of modern textile machinery, textile manufacturers are evaluating new maintenance technologies. These technologies are designed to lower maintenance cost by predicting failures before they create problems. There are various types of predictive maintenance programs which measure some medium which is affected before the a machine begins to fail. These mediums include temperature, lubricating properties, vibration and various other measurable parameters. Of all these measured values vibration yields the most information about the type of failure which is about to occur. Currently several companies offer packages, which include computer software and some sort of data collecting device, designed to use vibration as a complete predictive maintenance program. This thesis investigates one of these system constructed by CSI (Computational Systems Incorporated) in a winding facility. Currently there is information available on how to use this equipment, but very little information on how to utilize it in setting up a predictive maintenance program, especially in a textile facility. This thesis provides a step by step procedure in setting up a program in any textile facility. The winding facility data is used to provide an example. This procedure includes a procedure to determine if vibration analysis is beneficial for a particular installation, what type of parts should be measured, data analysis, alarm level, and warning level settings, and software maintenance.