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-