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Enabling Smart Manufacturing Through Connected Sensors & Machines

The value of smart machines for manufacturing applications are real. With them, you can enjoy real-time data collection, remote monitoring, preventative maintenance, and can apply self-learning for improved production quality. The basics of getting started to building intelligence around your machines include:


Connecting Sensors

There are vast amounts of data that factory equipment can provide. Complete a walkthrough of your factory and take an audit of which critical equipment can offer data that is most important to your operations. This can include devices such as PLCs, sensors, temperature gauges, production machines, and more.

You will need connectivity devices that can extract the information from individual sensors, gauges, and machines so you can stay updated on their performance and any variances that are outside your production requirements. Oftentimes, sensor data can be extracted via I/O devices. Connecting these devices to a protocol gateway will translate the I/O to an industrial protocol that can then be transmitted through Ethernet to your SCADA/MES (Manufacturing Execution System).To view this data, you will need some sort of web console that presents sensor data in a format that is easy to understand, enabling you to analyze by cross-referencing contextual production parameters and configurations, and make informed decisions. With this information at your fingertips, you will have the power to decide whether production is optimized, equipment needs maintenance, or whether there are conditions that create performance variances, etc. The return on your investment in machine intelligence will come in the form of increased production, higher production quality, and longer equipment lifespan resulting in more revenue for your business.

For more information, use the form on this page to download our case study with KPMG.


Connecting Machines

Ethernet is the standard if you want to view data from your machines. If your factory machines do not have Ethernet connections, they should have serial connections such as  RS-232/422/485. The good news for those looking to enhance the IQ of factory machines without a large investment in expensive Ethernet-ready factory equipment, is that legacy serial equipment can still have data transmitted via Ethernet through media conversion devices. In fact, this is the preferred method for most when adopting smart factories as it offers all the benefits of a connected factory at a fraction of the cost of purchasing new machines.

To get started, identify the types of connections and protocols your existing machines use. This includes conveyors, robot arms, CNC machines, automatic loaders, etc. If they are connected to a PLC, identify the connections and protocols those PLCs have. This is so you can plan how to bridge any potential gaps between your shop floor equipment and your MES software. If they speak different protocol languages, you will need a protocol gateway to bridge the communication gap between the PLC and MES (i.e. Fieldbus to Ethernet).

Depending on the size of your factory and the number of machines, it will help to draw a basic topology of your existing setup with connection types identified. This will aid in the process of selecting the proper industrial gateways, I/O devices, and Ethernet switches necessary once you start working with an industrial networking provider. Edge Computing

Once you have figured out how to extract information from your connected sensors, machines and other equipment, an abundance of data will now be available to you. This can become a burden for a single computer to process all that data. To alleviate this issue, industrial computers are used to process localized device data before they are sent to the SCADA. With this method, data acquisition is performed at the edge of your network (sometimes at remote locations), and only the important information you need to see is sent to the control center.

Edge computing delivers tangible value in industrial IoT use cases. It can help reduce connectivity costs by sending only the information that matters instead of raw streams of sensor data, which is particularly valuable on devices that connect via LTE/cellular such as smart meters or asset trackers. Also, when dealing with the massive amount of data produced by sensors in an industrial facility or a mining operation for instance, having the ability to analyze and filter the data before sending it can lead to huge savings in network and computing resources. Edge computing also reduces latency and makes connected applications more responsive and robust, lowers dependence on the MES (Manufacturing Execution System) / MI (Manufacturing intelligence) software, and helps to better manage the massive amounts data being generated from the machines and devices.

A technology called Docker is a tool that’s based on a smart gateway (computer or router) with industrial-strength reliability, running a combination of open Linux and Docker/container. The tool is embedded within a vendor’s own proprietary application, and is being touted as an ideal solution for edge computing. The Linux open platform enables easy porting of IoT applications to the IT infrastructure, while providing multi-vendor support and programmability. Some solution providers are proposing a layer of abstraction between the OS and the applications to facilitate easy deployment and management of applications on the fog node. Powered by these features, a computing node can intelligently process large volumes of data received from the sensors and field monitors while only sending critical data or a summary of the data to the cloud. For example, a semiconductor application can share computing data with the sever by implementing an edge computer to complete protocol conversion, analysis & reaction, and status monitoring.

Avoiding device-to-cloud data round trips is critical for applications using computer vision or machine learning. For example, an Automated Optical Inspection (AOI) machine is an automated visual inspection of printed circuit boards (PCB) or LCD transistors. The manufacturer uses on-device machine vision to scan the device to test for both catastrophic failure (e.g. missing components) and quality defects (e.g. fillet size or shape/component skew). By performing image processing tasks through an edge computer, AOI reduces the amount of data bandwidth, processing, and storage required for the inspection process by minimizing the number of image files sent over the network. In addition, by adapting a more sophisticated machine learning algorithm, the accuracy of image recognition could be improved while reducing chances of false alarms and downtime.Security can also be improved with edge computing by keeping sensitive information within the device and using edge networking equipment to reinforce security. This way, device data doesn’t travel over a network and stays closer to where it was created. By reducing the amount of data in a corporate data center or cloud environment, you are minimizing what is available to intruders if a system becomes compromised.

Through edge computing, system architects have the opportunity to learn the benefits of the distributed computing power from end to end - tapping into the capabilities of field devices, gateways, and the cloud altogether. Today, edge computers are being created with increasingly sophisticated computing capabilities, bringing “future proofing” to these systems by allowing automated updates for the device software and the list of local commands it can run.

Download our guidebook Edge Computing for Factory Automation to learn how edge computers can increase the intelligence of your factory.

By building the connection of sensors and machines, your manufacturing could be smarter, more efficient, and operate for longer periods of time. We're here to help enable connectivity of your factory equipment and incorporate edge computing. Reach out to us to understand which equipment fits your application. 

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You will need connectivity devices that can extract the information from individual sensors, gauges, and machines so you can stay updated on their performance and of any variances that are outside your production requirements. Unless your factory is comprised of new machines with Ethernet connections, most industrial machines are not Ethernet ready and have serial connections typically via RS-232/422/485. The good news for those looking to enhance the IQ of factory machines without a large investment in expensive factory equipment is that legacy serial equipment can still have data transmitted via Ethernet through media conversion devices.

Once you have figured out how to extract information from your connected sensors, machines and equipment, an abundance of data will now be available to you. It can help reduce connectivity costs by sending only the information that matters instead of raw streams of sensor data, which is particularly valuable on devices that connect via LTE/cellular such as smart meters or asset trackers. Also, when dealing with the massive amount of data produced by sensors in an industrial facility or a mining operation for instance, having the ability to analyze and filter the data before sending it can lead to huge savings in network and computing resources.

Edge computing also reduces latency and makes connected applications more responsive and robust, lowers dependence on the MES (Manufacturing Execution System) / MI (Manufacturing intelligence) software, and helps to better manage the massive amounts data being generated from the machines and devices. By performing image processing tasks through an edge computer, AOI reduces the amount of data bandwidth, processing, and storage required versus sending raw streams of image files over the network. Security can also be improved with edge computing by keeping sensitive information within the device and using edge networking devices to reinforce security.

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