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Using Prognostics for Extending Maintenance-Free Operation - A Case Study using Inductive Sensors for Condition Based Maintenance of a Wave Solder Machine
Keywords: Preventive Maintenance, Prognostics, Manufacturing
One of the most important factors for the industries to become a market leader requires near zero downtime performance for the manufacturing equipment. Most machine maintenance is either reactive type, where the equipment is either fixed or replaced after it fails; or blindly proactive where a certain level of performance degradation is assumed though there is no kind of malfunctioning input from the machine. For the latter case maintenance is scheduled on a fixed time interval even if the servicing of the machine is actually required or not. Figure 1 illustrates an overview of the different types of maintenance that are classified into preventive and reactive types of maintenance. The preventive type can be further classified into condition based and predetermined types of maintenance. Out of all different types of maintenance, condition based maintenance is the focal point of our investigation. This condition based maintenance is a PHM methodology to forecast maintenance-free operation cycles by avoiding scheduled maintenance based on actual application conditions. Figure 1. Overview of the different types of maintenance When humans are in charge of detecting deviations from the expected performance – health of mechanical equipment, it is very often observed that the degradation of the system appears to be invisible to the human sensing capability. A conservative approach to avoid this “sudden” failure would have been to schedule a maintenance based on a strict fixed time interval during which the probability of failure is very small or in other words, close to zero. Reality is that in fact machines usually go through a measurable process of degradation before they fail. The system health can be monitored by measuring the critical condition parameters which can prove to be a good tool to indicate the equipment physical condition. If this data can be effectively reduced, modeled, and continually analyzed it is actually possible to go ahead of the fixed interval preventive maintenance to smart prognostics (condition based maintenance) in which the likelihood of failure is predicted based on the remaining life models derived from the extracted real time health data that will be captured with the help of specific sensor(s). Our investigation will use of the fundamental knowledge of prognostics and health management (PHM) as it pertains to extending the maintenance-free operation period applied to a particular case of a “Wave Solder (WS) machine”. Figure 2 shows a schematic of our proposed approach for the current investigation on Wave Solder machine. Figure 2. Schematic diagram showing the PHM approach for extending the maintenance-free operation period of a Wave Solder machine.
Pedro O. Quintero, Student
University of Maryland at College Park
Hyattsville, Maryland
USA


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