Making Factories Smarter Through Machine Learning
1 . INTRODUCTION
With the advent of the 4th Industrial Revolution , referred to as Industrie 4.0 ( I4.0 ) and the Industrial Internet of Things ( IIoT ), machines and systems have become more intelligent and more connected . This connectivity has enabled data from the operational domain , the Operational Technology ( OT ), to become more accessible to the information technology ( IT ) domain .
The continual growth of machine intelligence and proliferation of sensors generating non-stop data has created a tremendous body of information . While data may have been acquired before , with some degree of analysis performed , the data was not being analyzed and understood to the extent possible today , especially in terms of real time analytics and related command and control .
This wealth of actionable information provides the key to unlocking higher efficiency and increasing reliability of these machines and systems through predictive , connected and precise operation . To remain competitive , companies are looking to evolve their analytics approaches and decipher the true meaning in the data . This data provides insight into their system operations enabling reduction in overall operating and maintenance costs .
One particular means to accomplish this reduction in costs is to maximize assets by minimizing downtime . Using sophisticated data analytics , combining the wealth of sensor data and other asset-related lifecycle parameters , companies are shifting their focus on “ Preventative ” maintenance with greater emphasis on “ Predictive ” maintenance . Using this approach , companies can begin to keep their assets operating more efficiently , more reliably , with longer periods of uptime , without taking their assets offline , avoiding costly downtime for regularly scheduled maintenance . By increasing uptime , with the ambitious goal of no unscheduled downtime , companies can keep their assets operational until the data analytics indicate a specific asset or part of an asset is approaching need for maintenance , thereby avoiding potential costly unplanned system failure .
This article highlights a specific company that is not only aware of the value of the IIoT but is truly recognizing value from unlocking the meaning of the data . The predictive maintenance presented in this article is representative of actions being taken as a result of IIoT and I4.0 . To become more competitive by leveraging the convergence of the IT and the OT in a secure , safe and connected manner , smart factories see this as one of the key ways to increase productivity and operate more efficiently through higher availability and increased machine and asset utilization .
IIC Journal of Innovation - 30 -