A review of predictive monitoring approaches and algorithms for material handling systems

Material handling systems (MHS) include a wide range of systems and equipment that are used for handling, movement, storage and control of materials during manufacturing. MHS can represent a significant portion of the cost to make a product and there can be significant cost savings in manufacturing processes if unexpected failures of such systems can be avoided and maintenance costs lowered. This paper first surveys the common maintenance practices of MHS including three typical warehouse MHS e.g. automatic picking system (APS), goods to destination (GDS), and Erector. The survey from end users shows that the majority of the companies does not keep the record of their past failures or improve their maintenance practices after major failures occur. Even the end users with more advanced maintenance programs use more reactive maintenance approaches, as opposed to preventive maintenance approaches which have the potential to lower the chances of unexpected failures and costly repairs. The present paper also reviews the past works in predictive monitoring of MHS categorized as machine level, component level, and production level. Although there has not been a large body of research published around this topic, the existing works demonstrate the suitability of applying Prognostics and Health Management techniques for improving the reliability of such machines and reducing their downtime. The opportunity for MHS to move towards a condition-based maintenance approach is still a few years away, and many end-users of this equipment would have to first adopt a good preventive and reliably centered maintenance philosophy. Once that occurs, the OEM’s would be the most likely candidates for further developing and implementing the research work and methods for MHS predictive monitoring.