An estimated 40 to 50% of equipment currently on manufacturing floors will have to be replaced over the next decade, according to a 2015 McKinsey research issued a report. The McKinsey report also predicted that factories will be the next “fertile ground” for Internet of Things (IoT) sensors and technologies that will enable smarter, more automated machine to machine processes that facilitate operations, relay data on performance and machine “wellness” for purposes of preventive maintenance, and deliver smarter robots that can assume responsibility for functions like inspecting pieces of sheet metal for defects as well as moving them.
The news should pique the interest of electronics manufacturers that supply the sensor markets. The growth of the global sensor business between 2011and 2016 has been a robust 9.1% annually, according to Intechno Consulting, a Swiss-based industry research firm. Much of this sensor uptake is in vehicles, ships, and planes. However, a robust amount of activity is also centered in the manufacturing and process industries, with Europe, Japan and the U.S. leading the way.
As a frontrunner in the development and deployment of embedded information, communications, and technology (ICT) systems, machine and process automation, Internet of Things manufacturing, intelligent monitoring and autonomous decision-making processes in manufacturing, Germany put forth its Manufacturing 4.0 initiative in 2011, developing completely new business models and tapping the potential to optimize performance in the areas of production and logistics. Other countries are following suit.
However, if electronics companies that develop and deliver sensors to manufacturers are going to optimize their own revenue performance, they need to help manufacturers overcome some of the barriers to entry.
“There is a big gap between operational technology and information technology,” said Chris LeBeau, global IT director of Advanced Technology Services (ATS), a provider of factory automation and optimization solutions “Operationally, factories are used to seeing output from their equipment for the raw materials they process, but there historically has been very little done when it comes to monitoring machine health for purposes of timely maintenance so you can avoid downtime. Even with a CNC machine you can plug in a laptop to monitor for maintenance, but how do you get this information to an expert?”
LeBeau and others believe that IoT and modern sensor technology can help solve these problems, but that some initial hurdles must be overcome first.
For example, if you are looking to automate machine monitoring and maintenance detection, sensors and the analytics software that they feed have to be calibrated to the behavior of individual machines that they are monitoring, since each machine has its own tolerance range for normal performance. This means that the sensors must work with analytics to first understand what these individual machine tolerances are, as well as the different environmental and operating conditions that they can normally fluctuate under to know if there is a true maintenance need.
Sensors must also be correctly matched to the right machines and operating conditions, because different sensors have different tolerances to heat, vibration, and other environmental factors.
For manufacturers, matching machines and sensors can be challenging, since it is unlikely that anyone on the production floor will have the expertise to calibrate each sensor correctly–and sensor manufacturers generally preset their sensors to default settings that are optimal for the sensors but not necessarily for the machines that they monitor. Consequently, this places the tuning burden for quality of service (QoS) on the company IT department, which also lacks QoS experience.
“We provide a machine maintenance software to manufacturers that is based on maximizing maintenance performance for optimum uptime and capacity,” said LeBeau, “And one of our chief goals is delivering a solution to our clients that eliminates disruption from machine failures.”
To use solutions that ATS and others provide, sensors must be properly calibrated to the pieces of equipment that they monitor and strong internal networks must link all of them together in the factory. Networks, quality of service, and skills in automation and integration are central to the effort—and sensors with broad tolerance ranges and flexibility can speed adoption.
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