Getting Condition Data From The Shop Floor to Your Software

IIoT (Industrial Internet of Things)  is becoming more mainstream, leading to more vendors implementing innovative monitoring capabilities in the new generation of sensors. These sensors are now multifunctional and provide a host of additional features such as self-monitoring.

With these intelligent sensors, it is possible to set up a system that enables continuous monitoring of the machines and production line. However, the essential requirement to use the provided data for analysis and condition monitoring for preventative and predictive maintenance is to get it from the shop floor to the MES, ERP, or other analysis software suites.

There are a variety of ways this can be done. In this post we will look at a few popular ways and methods to do so.

The most popular and straightforward implementation is using a REST API(also known as RESTful API). This has been the de facto standard in e consumer space to transport data. It allows multiple data formats to be transferred, including multimedia and JSON (Javascript Object Notation)

This has certain disadvantages like actively polling for the data, making it unsuitable for a spotty network, and having high packet loss.

MQTT(Message Queuing Telemetry Transport) eliminates the above problem. It’s very low bandwidth and works excellent on unreliable networks as it works on a publish/subscribe model. This allows the receiver to passively listen for the data from the broker. The broker only notifies when there is a change and can be configured to have a Quality of Service(QoS) to resend data if one of them loses connection. This has been used in the IoT world for a long time has become a standard for data transport, so most of software suits have this feature inbuilt.

The third option is to use OPCUA, which is the standard for M2M communication. OPCUA provides additional functionality over MQTT as it was developed with machine communication in mind. Notably, inbuilt encryption allows for secure and authenticated communication.

In summary, below is a comparison of these protocols.

A more detailed explanation can be found for these standards :




Photoelectric Sensors in the Packaging, Food, and Beverage Industry

The PFB industry requires the highest standards of quality and productivity when it comes to both their products and their equipment. In order to keep up with the rising demands to produce high quality parts quickly, many in the industry have incorporated photoelectric sensors into their lines. With their durable designs, accurate measurements and fast data output speeds, it is easy to see why. Combine the sensors’ benefits with the clean and well-lit environment of a PFB plant, and it begins to feel like this product was made specifically for the industry. There are many variants of photoelectric sensors, but the main categories are: through beam, diffuse, and retro-reflective sensors.

Through Beam

Through beam sensors come in many different shapes and sizes but the core idea stays the same. An emitter shoots LED red, red laser, infrared, or LED infrared light across an open area toward a receiver. If the receiver detects the light, the sensor determines nothing is present. If the light is not detected, this means an object has obstructed the light.


  • Object detection during production
  • Detecting liquid in transparent bottles
  • Detecting, counting, and packaging tablets

Diffuse Beam

Diffuse beam sensors operate a little differently in that the emitter and receiver are in the same housing, often very closely to one another. With this sensor, the light beam is emitted out, the light bounces off a surface, and the light returns to the receiver. The major takeaway with the diffuse beam sensor is that the object being detected is also being used as the reflecting surface.


  • Label detection
  • Monitoring the diameter of film
  • Verifying stack height on pallet

Retro-Reflective Beam

Retro-reflective sensors are similar to diffuse beam sensors in that the emitter and receiver are also contained within the same housing. But this sensor requires an additional component — a reflector. This sensor doesn’t use the object itself to reflect the light but instead uses a specified reflector that polarizes the light, eliminating the potential for false positive readings. Retro-reflective sensors are a strong alternative to through beam when there isn’t room for two separate sensor heads.


  • Transparent film detection
  • Detection of shrink-wrapped pallets
  • Detecting any reflective target

RFID Replaces Bar Codes Efficient Asset Tracking

Bar code technology has been around for many years and is a tried and true means for tracking asset and product movement, but it has its limitations. For example, a bar code reader must have an unobstructed view of the bar code to effectively scan. And the bar code label cannot be damaged, or it is then unreadable by the scanner.

In more recent years, additional RFID technologies have been more readily available for use to accomplish the same task but with fewer limitations. Using RFD, a scanner may be able to read tags that are blocked by other things and not visible to the naked eye. UHF RFID can scan multiple tags at the same time in a single scan, whereas most bar codes need to be scanned individually. This, therefore, increases efficiency and reduces the time required to perform the scans.

Then, of course, there is the human factor. RFID can help eliminate mistakes caused by human error. Most bar code scanning is done with hand scanners held by workers since the scanner has to be in the exact position to see the bar code to get a good scan. While manual/hand-held scanning can be done using RFID, most times a fixed scanner can be used as long as the position of the RFID tag can be guaranteed within certain tolerances. These tolerances are much greater than with a bar code scanner.

With the advent of inexpensive consumable RFID labels, the ease and cost of transitioning to RFID technology has become more feasible for manufacturers and end users. These labels can be purchased for pennies each in rolls of several thousand at a time.

It should be noted that several companies now produce printers that can actually code the information on a RFID label tag while also printing data, including bar codes, on these label tags so you have the best of both worlds. Tags can be scanned automatically and data that can be read by the human eye as well as a bar code scanner.

Some companies have expressed concern about the usage of RFID in different countries due to local regulations regarding the frequencies of radio waves causing interferences.

This is not an issue for HF  and UHF technology. HF is an ISO standard (ISO 15693) technology so it applies to most everywhere. For UHF, which is more likely to be used due to the ability to scan at a distance and scan multiple tags at the same time, the only caveat is that different areas of the world allow scanners to only operate in certain frequencies. This is overcome by the fact that almost all UHF tags that I have encountered are what are called global tags.

This means these tags can be used in any of the global frequency ranges of UHF signals. For example, in the North America, the FCC restricts the frequency range for UHF RFID scanners to 902-928 MHz, whereas MIC in Japan restricts them to 952-954 MHz, ETSI EN 300-220 in Europe restricts them to 865-868 MHz, and DOT in India restricts them to 865-867 MHz. These global tags can be used in any of these ranges as they work from 860 to 960 MHz.

On the subject of UHF, it should be mentioned that in addition to the frequency ranges restricted by various part of the world, maximum antenna power is also locally restricted.

For more information on RFID for asset tracking, visit


Ensure Food Safety with Machine Vision

Government agencies have put food manufacturers under a microscope to ensure they follow food safety standards and comply with regulations. When it comes to the health and safety of consumers, quality assurance is a top priority, but despite this, according to The World of Health Organization, approximately 600 million people become ill each year after eating contaminated food, and 420,000 die.

Using human manual inspection for quality assurance checks in this industry can be detrimental to the company and its consumers due to human error, fatigue and subjective opinions. Furthermore, foreign particles that should not be found in the product may be microscopic and invisible to the human eye. These defects can lead to illness, recalls, lawsuits, and a long-term negative perception of the brand itself. Packaging, food and beverage manufacturers must realize these potential risks and review the benefits of incorporating machine vision. Although machine vision implementation may sound like a costly investment, it is small price to pay when compared to the potential damage of uncaught issues. Below I explore a few benefits that machine vision offers in the packaging, food and beverage industries.

Consumers expect and rely on safe products from food manufacturers. Machine vision can see through packaging to determine the presence of foreign particles that should not be present, ensuring these products are removed from the production line. Machine vision is also capable of inspecting for cross-contamination, color correctness, ripeness, and even spoilage. For example, bruises on apples can be hard to spot for the untrained eye unless extremely pronounced. SWIR (shortwave infrared) illumination proves effective for the detection of defects and contamination. Subsurface bruising defects become much easier to detect due to the optimization of lighting and these defected products can be scrapped.

Uniformity of Containers
Brand recognition is huge for manufacturers in this industry. Products that have defects such as dents or uneven contents inside the container can greatly affect the public’s perception of the product and/or company. Machine vision can detect even the slightest deformity in the container and ensure they are removed from the line. It can also scan the inside of the container to ensure that the product is uniform for each batch. Vision systems have the ability optimize lighting intensity, uniformity, and geometry to obtain images with good contrast and signal to noise. Having the ability to alter lighting provides a much clearer image of the point of interest. This can allow you to see inside a container to determine if the fill level is correct for the specific product.

Packaging is important because if the products shipped to the store are regularly defected, the store can choose to stop stocking that item, costing the manufacturer valuable business. The seal must last from production to arrival at the store to ensure that the product maintains its safe usability through its marked expiration date. In bottling applications, the conveyors are moving at high speeds so the inspection process must be able to quickly and correctly identify defects. A facility in Marseille, France was looking to inspect Heineken beer bottles as they passed through a bottling machine at a rate of 22 bottles/second (80,000 bottles/hour). Although this is on the faster end of the spectrum, many applications require high-speed quality checks that are impossible for a human operator. A machine vision system can be configured to handle these high-speed applications and taught to detect the specified defect.


It’s crucial for the labels to be printed correctly and placed on the correct product because of the food allergy threats that some consumers experience. Machine vision can also benefit this aspect of the production process as cameras can be taught to recognize the correct label and brand guidelines. Typically, these production lines move at speeds too fast for human inspection. An intuitive, easy to use, machine vision software package allows you to filter the labels, find the object using reference points and validate the text quickly and accurately.

These areas of the assembly process throughout packaging, food and beverage facilities should be considered for machine vision applications. Understanding what problems occur and the cost associated with them is helpful in justifying whether machine vision is right for you.

For more information on machine vision, visit



Turning Big Data into Actionable Data

While RFID technology has been available for almost seventy years, the last decade has seen widespread acceptance, specifically in automated manufacturing. Deployed for common applications like automatic data transfer in machining operations, quality control in production, logistics traceability and inventory control, RFID has played a major role in the evolution of data collection and handling. With this evolution has come massive amounts of data that can ultimately hold the key to process improvement, quality assurance and regulatory compliance. However, the challenge many organizations face today is how to turn all that data into actionable data.

Prominent industry buzzwords like Industry 4.0 and the Industrial Internet of Things (IIOT) once seemed like distant concepts conjured up by a marketing team far away from the actual plant floor, but those buzzwords are the result of manufacturing organizations around the globe identifying the need for better visibility into their operations. Automation hardware and the infrastructure that supports it has advanced rapidly due to this request, but software that turns raw data into actionable data is still very much in demand. This software needs to provide interactive feedback in the form of reporting, dashboards, and real time indicators.

The response to the demand will bring vendors from other industries and start-ups, while a handful of familiar players in automation will step up to the challenge. Competition keeps us all on our toes, but the key to filling the software gap in the plant is partnering with a vendor who understands the needs on the plant floor. So, how do you separate the pretenders from the contenders? I compiled a check list to help.

Does the prospective vendor have:

  • A firm understanding that down time and scrap need to be reduced or eliminated?
  • A core competency in automation for the plant floor?
  • Smart hardware devices like RFID and condition monitoring sensors?
  • A system solution that can collect, analyze, and transport data from the device to the cloud?
  • A user-friendly interface that allows interaction with mobile devices like tablets and phones?
  • The capability to provide customized reports to meet the needs of your organization?
  • A great industry reputation for quality and dependability?
  • A chain of support for pre-sales, installation, and post-sales support?
  • Examples of successful system deployments?
  • The willingness to develop or modify current devices to address your specific needs?

If you can check the box for all of these, it is a safe bet you are in good hands. Otherwise, you’re rolling the dice.

Document Product Quality and Eliminate Disputes with Machine Vision

“I caught a record-breaking walleye last weekend,” an excited Joe announced to his colleagues after returning from his annual fishing excursion to Canada.

“Record-breaking?  Really?  Prove it.” demanded his doubtful co-worker.

Well, I left my cell phone in the cabin so it wouldn’t get wet on the boat so I couldn’t take a picture, but I swear that big guy was the main course for dinner.”

“Okay, sure it was Joe.”

We have all been there — spotted a mountain lion, witnessed an amazing random human interaction, or maybe caught a glimpse at a shooting star.  These are great stories, but they are so much more believable and memorable with a picture or video to back them up.  Now a days, we all carry a camera within arm’s reach.  Capturing life events has never been easier and more common, so why not use cameras to document and record important events and stages within your manufacturing process?

As the smart phone becomes more advanced and common, so does the technology and hardware for industrial cameras (i.e. machine vision).  Machine vision can do so much more than pass fail and measurement type applications.  Taking, storing, and relaying pictures along different stages of a production process could not only set you apart from the competition but also save you costly quality disputes after it leaves your facility.  A picture can tell a thousand words, so what do you want to tell the world?  Here are just a couple examples how you can back up you brand with machine vision:

Package integrity: We have all seen the reduced rack at a grocery store where a can is dented or missing a label.  If this was caused by a large-scale label application defect, someone is losing business.  So, before everyone starts pointing fingers, the manufacturer could simply provide a saved image from their end-of line-vision system to prove the cans were labeled when shipped from their facility.

Assembly defects: When you are producing assembled parts for a larger manufacturer, the standards they set are what you live and die by.  If there is ever a dispute, having several saved images from either individual parts or an audit of them throughout the day could prove your final product met their specifications and could save your contract.

Barcode legibility and placement: Show your retail partners that your product’s bar code will not frustrate the cashier by having to overcome a poorly printed or placed barcode.  Share images with them to show an industrial camera easily reading the code along the packaging line ensuring a hassle-free checkout as well as a barcode grade to ensure their barcode requirements are being met.

In closing, pictures always help tell a story and make it more credible.  Ideally your customers will take your word for it, but when you catch the record-breaking walleye, you want to prove it.

Which RFID Technology is Best for Your Traceability Application?

There are a lot of articles on using RFID for traceability, but it’s hard to know where to begin. Examples of traceability include locating an important asset like a specific mold that is required to run a machine or verifying a specific bin of material required to run production. Spending time looking for these important assets leads to lost time and production delays. RFID can help but understanding the different RFID capabilities will narrow down the type of RFID that is required.

Not all RFID technology is the same. Each RFID technology operates differently and is categorized by the frequency band of the radio spectrum, such as low frequency, high frequency and ultra-high frequency. In low and high frequency RFID, the read range between RFID tag and reader antenna is measured in millimeters and inches. The read range on ultra-high frequency (UHF) RFID technology can range from one meter to 100 meters. Typically, inventory traceability is done using ultra-high frequency band of the radio frequency spectrum, due to the need to read the asset at a further distance so it does not interfere with the production flow. Also, there are cases where there needs to be a reading of multiple tags in an area at the same time to determine where an asset is located. UHF RFID technology allows for simultaneous reading of multiple RFID tags from a single antenna reader.

There are two types of UHF RFID, passive and active.  Passive UHF RFID means that the RFID tags themselves have no additional power source. The UHF reader antenna sends out an electromagnetic wave field, and the RFID tags within the electromagnetic field have an internal antenna that receives the energy which activates the integrated circuit inside the tag to reflect the signals back to start communicating. The read distance between the passive RFID tag and antenna reader is determined by several factors, such as the size of the electromagnetic wave field generated out of the reader antenna and the size of the receiver antenna on the RFID tag. Typical read ranges on passive UHF systems can be anywhere from one to 12 meters, where the larger the power and RFID tag, the longer the range.

Active UHF RFID systems do not require the tag to reflect signals back to communicate because the active RFID tag has its own transmitter and internal battery source. Because of this, with active UFH RFID you can get read ranges of up to 100 meters. There are active tags which wake up and communicate when they receive a radio signal from a reader antenna, while others are beacons which emit a signal at a pre-set interval. Beacon active tags can locate in real time the location of the asset that the RFID tag is attached to. However, a downfall to active RFID tags is the battery life on the tag. If the battery is dead, then the asset will no longer be visible.

Figure 1

Once the strengths and weaknesses of each type of UHF RFID system is known, it’s easier to work with the constraints of the system. For example, the application in Figure 1 shows a reader antenna for reading bins of material placed a few feet away so that its’ not in the way of production. A passive UHF RFID system will work in this case, due to the distance between the antenna and the RFID tag on the bin a few feet away. There is no need to worry about battery life on the passive RFID tag.

Figure 2

If the exact location of a production mold is required in a large facility, then using an active UHF RFID system is likely a better fit. Incorporating an active RFID tag that sends out a beacon at a fixed interval to a data center ensures the location of all assets are always known. With this setup, the exact location of the mold can be found at any time in the facility.

Examining the different types of RFID technology can help determine the correct one to use in a traceability application. This includes analyzing the pros and cons of each technology and seeing which one is the best fit for the application.

The Right Mix of Products for Recipe-Driven Machine Change Over

The filling of medical vials requires flexible automation equipment that can adapt to different vial sizes, colors and capping types. People are often deployed to make those equipment changes, which is also known as a recipe change. But by nature, people are inconsistent, and that inconsistency will cause errors and delay during change over.

Here’s a simple recipe to deliver consistency through operator-guided/verified recipe change. The following ingredients provide a solid recipe-driven change over:

Incoming Components: Barcode

Fixed mount and hand-held barcode scanners at the point-of-loading ensure correct parts are loaded.

Change Parts: RFID

Any machine part that must be replaced during a changeover can have a simple RFID tag installed. A read head reads the tag in ensure it’s the correct part.

Feed Systems: Position Measurement

Some feed systems require only millimeters of adjustment. Position sensor ensure the feed system is set to the correct recipe and is ready to run.

Conveyors Size Change: Rotary Position Indicator

Guide rails and larger sections are adjusted with the use of hand cranks. Digital position indicators show the intended position based on the recipes. The operators adjust to the desired position and then acknowledgment is sent to the control system.

Vial Detection: Array Sensor

Sensor arrays can capture more information, even with the vial variations. In addition to vial presence detection, the size of the vial and stopper/cap is verified as well. No physical changes are required. The recipe will dictate the sensor values required for the vial type.

Final Inspection: Vision

For label placement and defect detection, vision is the go-to product. The recipe will call up the label parameters to be verified.

Traceability: Vision

Often used in conjunction with final inspection, traceability requires capturing the barcode data from the final vials. There are often multiple 1D and 2D barcodes that must be read. A powerful vision system with a larger field of view is ideal for the changing recipes.

All of these ingredients are best when tied together with IO-Link. This ensures easy implantation with class-leading products. With all these ingredients, it has never been easier to implement operator-guided/verified size change.

RFID Gaining Traction in Tire Manufacturing

RFID is one of the hottest trending technologies in the tire industry. It has the potential to increase efficiency in tire production and logistics processes and gather large amounts of data for IIoT.

This technology will:

  • Reveal transparency deep in the processes
  • Minimize the number of rejected tires
  • Improve production processes for fewer failures
  • Increase control of materials
  • Improve the overall quality of individual tires

The challenge of using RFID in the tire industry is dealing with the harsh environments of some of the production areas in automotive plants. But the benefits of RFID to the tire industry are becoming more and more a reality. Suppliers of RFID are talking to tire manufacturing engineers, automation teams, material handling teams and R&D development engineers to develop better tools. For now, here are some examples of where RFID can be implemented in the tire creation process to improve efficiency, quality and cost.

In the mixing process, RFID “labels” are applied to all the chemicals and rubber compounds to assure the mixing of the right recipe of materials. RFID readers can be mounted on TBMs (Tire Build Machines), which are located before the curing press process, to assure the right material reels, parts and tools are in place before the expensive tire build process occurs.

There is also a growing need for RFID in the curing and mold processes. It important to manage the molds and the parts of the mold, like the bead rings, mold containers and mold segments. These are very expensive and there are hundreds in the average plant. Tags need to be able to sustain temperatures above 300 °F continuously for 8 hour shifts with little to no cooling down time.

RFID is an excellent tool to monitor material flow throughout the whole manufacturing process. RFID can be added to a trolly, AGV, conveyors and hook-chain conveyors.

While RFID is already being implemented by some tire manufacturers, there is much room for much growth in this conservative industry. As more manufacturers lean into IIoT and the need for data, RFID will surely be used more and more often.

The tire industry is excited to roll in RFID technology and pumped up to implement it where it makes the most sense and ROI dollars.

For more information about the tire industry, visit

How Condition Monitoring has Evolved and Its Role in IIoT

In recent years, as IIoT and Industry 4.0 have become part of our everyday vocabulary, we’ve also started hearing more about condition monitoring, predictive maintenance (PdM) and predictive analytics. Sometimes, we use these terms interchangeably as well. Strictly speaking, condition monitoring is a root that enables both predictive maintenance and predictive analytics. In today’s blog we will brush up a little on condition monitoring and explore its lineage.

Equipment failures have been around since the beginning of time. Over the years, through observation (collecting data) and brute-force methods, we learned that from time-to-time every piece of equipment needs some TLC. Out of this understanding, maintenance departments came to existence, and there we started having experts that could tell based on touch, smell and noise what is failing or what has gone wrong.

Figure 1: Automation Pyramid

Then we started automating the maintenance function either as a preventative measure (scheduled maintenance) or through some automated pieces of equipment that would collect data and provide alerts about a failure. We proudly call these SCADA systems – Supervisory Control and Data Acquisition. Of course, these systems did not necessarily prevent failures, but help curtail them.  If we look at the automation pyramid, the smart system at the bottom is a PLC and all the sensors are what we call “dumb sensors”. So, that means, whatever information the SCADA system gets would be filtered by the PLC. PLCs were/have been/ and are always focused on the process at hand; they are not data acquisition equipment. So, the data we receive in the SCADA system is only as good as the PLC can provide. That means the information is primarily about processes. So, the only alerts maintenance receives is when the equipment fails, and the process comes to a halt.

With the maintenance experts who could sense impending failures becoming mythological heroes, and  SCADA systems that cannot really tell us the story about the health of the machines, once again, we are looking at condition monitoring with a fresh set of eyes.

Sensors are at the grass root level in the automation pyramid, and until the arrival of IO-Link technology, these sensors were solely focused on their purpose of existence; object detection, or measurement of some kind. The only information one could gather from these sensors was ON/OFF or a signal of 4-20mA, 0-10V, and so on. Now, things are different, these sensors are now becoming pretty intelligent and they, like nosy neighbors, can collect more information about their own health and the environment. These intelligent sensors can utilize IO-Link as a communication to transfer all this information via a gateway module (generally known as IO-Link master) to whomever wants to listen.

Figure 2: IO-Link enabled Balluff photo-eye

The new generation of SCADA systems can now collect information not only from PLCs about the process health, but also from individual devices. For example, a photo-eye can measure the intensity of the reflected light and provide an alert if the intensity drops beyond a certain level, indicating a symptom of pending failure. Or a power supply inside the cabinet providing an alert to the supervisory control about adverse conditions due to increase temperature or humidity in the cabinet. These types of alerts about the symptoms help maintenance prevent unplanned downtime on the plant floor and make factories run more efficiently with reduced scrap, reduced down-time and reduced headaches.

Figure 3: The Next Generation Condition Monitoring

There are many different condition monitoring architectures that can be employed, and we will cover that in my next blog.