The Industrial Internet of Things holds so much possibility that it can be difficult to know where to begin. Everyday production processes\u2014such as weighing\u2014can be a great place to start to prove the value of digitalization and cloud analyses in your operation.<\/p>\n
It\u2019s easy to become overwhelmed by all the digital technologies and data handling processes involved with the Industrial Internet of Things (IIoT)\u2014from transmitting data to the cloud to determining data analysis parameters to building the apps to access the data. As difficult as it all may sound, technology suppliers have been increasingly focused on simplifying these processes to enable companies of all sizes to benefit from the technology.<\/p>\n
Matt Morrissey, product manager at Siemens, suggests weighing operations are a great place to start with IIoT because it\u2019s a good way to better understand the technology and its possibilities, and reap significant savings through problem identification and process improvement.<\/p>\n
Weighing is a straightforward IIoT application because the weight of materials used in production processes is monitored using load cells\u2014 basic mechanical sensors that deflect under changes in mass and produce an electrical signal proportionate to the change in deflection. A controller or integrator collects this electrical signal and converts it into a process variable, which can be used by the control system to monitor or manage the process.<\/p>\n
\u201cIf we want to analyze this data and compare it to environmental conditions or other process variables such as level or pressure,\u201d suggests Morrissey, \u201cwe might then be able to correlate the data to look for opportunities to refine our process or gain an efficiency along the way.\u201d<\/p>\n
To provide a real world example of how this can work, Morrissey suggests considering belt scales, which are used in multiple industries to weigh material while it is being conveyed from one point to another. Morrissey likes to use belt scales as an example for complex, yet approachable IIoT data analyses because there are so many factors which can affect a belt scale\u2019s accuracy. For example, temperature effects on the belt tension or density of the material due to moisture content, to name just a couple.<\/p>\n
For his example scenario, Morrissey says to imagine you have three conveyors equipped with belt scales each moving one ton of raw material per hour into the plant. \u201cThat means three tons of raw material per hour going in,\u201d he said. \u201cAt the other end of the line you have finished goods leaving your plant comprised of just two and a half tons per hour of material. If three tons are going in, shouldn\u2019t you have three tons coming out? Where did that other half ton per hour disappear to?\u201d<\/p>\n
Morrissey notes that this scenario is a common production problem for many manufacturers. It\u2019s easy enough to regularly monitor the belt scale\u2019s readings, he says, but solving accuracy issues external to the belt scale is not so simple.<\/p>\n
\u201cOf course, you could take the belt scale readings and export them into a spreadsheet, go online and look at the weather trends for temperature and rain over the last month, chart that data along with your belt scale readings, and then manually chart the data to look for trends or impacts when temperature or rain goes up or down,\u201d Morrissey says. \u201cBut this sort of data mining and analysis can take hours, depending on the level of detail\u2014and getting some of this data may not be possible based on the data logging that is available.\u201d<\/p>\n
Using this data, it\u2019s now possible to easily see if there is a relation between the moisture content of the raw material or change in temperature vs. the accuracy of our belt scale on the input of the process vs. the output.<\/p><\/blockquote>\n
To explain how this kind of analysis is easily handled with an IIoT approach, Morrissey says to first consider that your belt scale is connected to an integrator which is connected to a PLC. The PLC controls and monitors the entire plant.<\/p>\n
The PLC takes the belt\u2019s data points\u2014along with inputs from other production-related devices\u2014and assigns them to data blocks. \u201cSo far, this is a pretty standard setup in process industries,\u201d he says. \u201cBut now you can take that information in the PLC and connect it to the cloud.\u201d<\/p>\n
MindConnect Nano to handle this kind of PLC data transmission to the cloud. Morrissey points out that both of these products feature integral security so that information cannot be accessed from the web without proper validation and encryption.<\/p>\n