Demystifying Machine Learning

Machine learning can help organizations improve manufacturing operations and increase efficiency, productivity, and safety by analyzing data from connected machines and sensors, machine. For example, its algorithms can predict when equipment will likely fail, so manufacturers can schedule maintenance before problems occur, thereby reducing downtime and repair costs.

How machine learning works

Machine learning teaches computers to learn from data – to do things without being specifically told how to do them. It is a type of artificial intelligence that enables computers to automatically learn or improve their performances by learning from their experiences.

machine learning stepsImagine you have a bunch of toy cars and want to teach a computer to sort them into two groups: red and blue cars. You could show the computer many pictures of red and blue cars and say, “this is a red car” or “this is a blue car” for each one.

After seeing enough examples, the computer can start to guess which group a car belongs in, even if it’s a car that it hasn’t seen before. The machine is “learning” from the examples you show to make better and better guesses over time. That’s machine learning!

Steps to translate it to industrial use case

As in the toy car example, we must have pictures of each specimen and describe them to the computer. The image, in this case, is made up of data points and the description is a label. The sensors collecting data can be fed to the machine learning algorithm in different stages of the machine operation – like when it is running optimally, needs inspection, or needs maintenance, etc.

Data taken from vibration, temperature or pressure measures, etc., can be read from different sensors, depending on the type of machine or process to monitor.

In essence, the algorithm finds a pattern for each stage of the machine’s operation. It can notify the operator about what must be done given enough data points when it starts to veer toward a different stage.

What infrastructure is needed? Can my PLC do it?

The infrastructure needed can vary depending on the algorithm’s complexity and the data volume. Small and simple tasks like anomaly detection can be used on edge devices but not on traditional automation controllers like PLCs. Complex algorithms and significant volumes of data require more extensive infrastructure to do it in a reasonable time. The factor is the processing power, and as close to real-time we can detect the machine’s state, the better the usability.

Using Guided Changeover to Reduce Maintenance Costs, Downtime

A guided changeover system can drastically reduce the errors involved with machine operation, especially when added to machines using fully automated changeovers. Processing multiple parts and recipes during a production routine requires a range of machines, and tolerances are important to quantify. Only relying on the human element is detrimental to profits, machine maintenance, and production volumes. Implementing operator assistance to guide visual guidance will reveal inefficiencies and allow for vast improvements.

Removing human error

Unverified manual adjustments may cause machine fatigue or failure. In a traditional manual changeover system, the frequency of machine maintenance is greater if proper tolerances are not observed at each changeover. Using IO-Link can remove the variable of human error with step-by-step instructions paired with precise sensors in closed-loop feedback. The machine can start up and run only when all parts are in the correct position.

Preventative maintenance and condition monitoring

Preventative maintenance is achievable with the assistance of sensors, technology, and systems. Using condition monitoring for motors, pumps and critical components can help prevent the need for maintenance and notably improve the effectiveness of maintenance with custom alerts and notifications with a highly useful database and graphing function.

A repeatable maintenance routine based on condition monitoring data and using a system to guide machine changeover will prolong machine life and potentially eliminate downtime altogether.

For more, read this real-world application story, including an automated format change to eliminate human error, reduce waste and decrease downtime.

Understanding Image Processing Standards and Their Benefits

In the industrial image processing world, there are standards – GenICam, GigE Vision, and USB3 Vision – that are similar to the USB and Ethernet standards used in consumer products. What do these image processing standards mean, and what are their benefits?

The GenICam standard, which is maintained by the European Machine Vision Association (EMVA), serves as a base for all the image processing standards. This standard abstracts the user access to the features of a camera and defines the Standard Feature Naming Convention (SFNC) that all manufacturers use so that common feature names are used to describe the same functions.

Additionally, manufacturers can add specific “Quality of Implementation” features outside of the SFNC definitions to differentiate their products from ones made by other manufacturers. For example, a camera can offer specific features like frame average, flat field correction, logic gates, etc. GenICam/GigE Vision-based driver and software solutions from other manufacturers can also use these features without any problem.

“On-the-wire” standards

USB3 Vision and GigE Vision are “on-the-wire” interfaces between the driver and the camera. These standards are maintained by the Automated Imaging Association (AIA). You are probably familiar with “on-the-wire” standards and their advantages if you have used plug-and-play devices like USB memory sticks, USB mice, or USB hard disks. They work together without any problem, even if they are made by different manufacturers. It’s the same thing with GenICam/GigE Vision/USB3 Vision-based driver/software solutions. The standards define a transport layer, which controls the detection of a device, configuration (register access), data streaming (device detection), and event handling, and connects the interface to GenICam (Figure 1).

USB3 Vision builds on the GigE Vision standard by including accessories like cables. The mechanics are part of the standard and defines lockable cable interfaces, as one example. This creates a more robust interface for manufacturing environments.

Are standards a must-have?

Technically, standards aren’t necessary. But they make it possible to use products from multiple manufacturers and make devices more useful in the long term. For a historical comparison, look at USB 2.0 cameras and GigE Vision. USB 2.0 industrial cameras were introduced in 2004 and only worked with proprietary drivers (Figure 2) between the client and Vision Library/SDK and between the driver and camera. Two years later, Gigabit Ethernet cameras were introduced with the GigE Vision image processing standard, which didn’t require proprietary drivers to operate.

In the case of a system crash, users of the USB 2.0 cameras wouldn’t know whether the proprietary driver or the software library was to blame, which made them difficult to support. During the decision phase of selecting sensors and support, the customer had to keep the product portfolio in mind to meet their specifications. Afterward, the application was implemented and only worked with the proprietary interfaces of the manufacturer. In case of future projects or adaptions –for example, if a new sensor was required –it would have been necessary for the manufacturer to offer this sensor. Otherwise, it was necessary to change the manufacturer, which meant that a new implementation of the software was necessary as well. In contrast, flexibility is a big advantage with Gigabit Ethernet cameras and GigE Vision: GigE Vision-compliant cameras can be used interchangeably without regard to the manufacturer.

Despite this obvious benefit, USB cameras are more prevalent in certain image processing fields like medicine, given that the applications define the camera’s sensor resolution, image format and image frequency (bandwidth), and the environment for the purpose of cable length, frame grabber, or digital camera solution. With such tightly-defined requirements, USB cameras solve the challenges of these applications.

It’s hard to believe, but a few years ago, there weren’t any standards in the image processing market. Each manufacturer had its own solution. These times are gone – the whole market has pulled together, to the benefit of customers. Because of the standards, the interaction between hardware, driver, and software delivers the experience of a uniform piece. The quality of the market is improved. For the customer, it is easier to make product decisions since they are not locked into one company’s portfolio. With standards-compliant products, the customer can always choose the best components, independent of the company. With GenICam as a base, the image processing market offers the best interface for every application, either with GigE Vision or USB3 Vision.

IO-Link Changeover: ID Without RFID – Hub ID

When looking at flexible manufacturing, what first comes to mind are the challenges of handling product changeovers. It is more and more common for manufacturers to produce multiple products on the same production line, as well as to perform multiple operations in the same space.

Accomplishing this and making these machines more flexible requires changing machine parts to allow for different stages in the production cycle. These interchangeable parts are all throughout a plant: die changes, tooling changes, fixture changes, end-of-arm tooling, and more.

When swapping out these interchangeable parts it is crucial you can identify what tooling is in place and ensure that it is correct.

ID without RFID

When it comes to identifying assets in manufacturing today, typically the first option companies consider is Radio-Frequency Identification (RFID). Understandably so, as this is a great solution, especially when tooling does not need an electrical connection. It also allows additional information beyond just identification to be read and written on the tag on the asset.

It is more and more common in changeover applications for tooling, fixtures, dies, or end-of-arm tooling to require some sort of electrical connection for power, communication, I/O, etc. If this is the case, using RFID may be redundant, depending on the overall application. Let’s consider identifying these changeable parts without incurring additional costs such as RFID or barcode readers.

Hub ID with IO-Link

In changeover applications that use IO-Link, the most common devices used on the physical tooling are IO-Link hubs. IO-Link system architectures are very customizable, allowing great flexibility to different varieties of tooling when changeover is needed. Using a single IO-Link port on an IO-Link master block, a standard prox cable, and hub(s), there is the capability of up to: 

    • 30 Digital Inputs/Outputs or
    • 14 Digital Inputs/Outputs and Valve Manifold Control or
    • 8 Digital Inputs/Outputs and 4 Analog Voltage/Current Signals or
    • 8 Analog Input Signals (Voltage/Current, Pt Sensor, and Thermocouple)

When using a setup like this, an IO-Link 1.1 hub (or any IO-Link 1.1 device) can store unique identification data. This is done via the Serial Number Parameter and/or Application Specific Tag Parameter. They act as a 16- or 32-byte memory location for customizable alphanumeric information. This allows for tooling to have any name stored within that memory location. For example, Fixture 44, Die 12, Tool 78, EOAT 123, etc. Once there is a connection, the controller can request the identification data from the tool to ensure it is using the correct tool for the upcoming process.

By using IO-Link, there are a plethora of options for changeover tooling design, regardless of various I/O requirements. Also, you can identify your tooling without adding RFID or any other redundant hardware. Even so, in the growing world of Industry 4.0 and the Industrial Internet of Things, is this enough information to be getting from your tooling?

In addition to the diagnostics and parameter setting benefits of IO-Link, there are now hub options with condition monitoring capabilities. These allow for even more information from your tooling and fixtures like:

    • Vibration detection
    • Internal temperature monitoring
    • Voltage and current monitoring
    • Operating hours counter

Flexible manufacturing is no doubt a challenge and there are many more things to consider for die, tooling and fixture changes, and end-of-arm tooling outside of just ID. Thankfully, there are many solutions within the IO-Link toolbox.

For your next changeover, I recommend checking out Non-Contact Inductive Couplers Provide Wiring Advantages, Added Flexibility and Cost Savings Over Industrial Multi-Pin Connectors for a great solution for non-contact connectivity that can work directly with Hub ID.

Reducing Assembly Line Mistakes With the Error Proofing Platform Station

About 18 months ago, one of the major automotive companies came to the Indicon Conference looking for a way to decrease mistakes on the assembly line. They found a solution in a concept named the Error Proofing Platform Station (EPP).

How it works

The EEP works by using a bar code reader, in this case a scanner, to verify that the correct parts are being used in the assembly process. The scanner connects to an RS232-to-digital-converter module, and from there to an IO-Link networking block which enables two-way communication of information with the PLC. IO-Link blocks can connect hundreds of devices, versus traditional blocks that can only connect eight to sixteen devices. This greatly simplifies the hardware, cabling and installation costs.

EEP station design

The overall design of this EPP station grabbed the automotive company’s attention for several reasons.  It is effective both in its simplicity as well as the small footprint that it takes up. The design of the components allows it to sit on the plant floor instead of having to be installed in a cabinet like previous designs. They especially liked the wiring design where a single cable goes from the IO-Link block at is managed by a single IP address back to the PLC. Should one of the devices fail, you simply replace a single cable or device and move on.

The old days of unwinding the cables and spending hours trying to decipher which cable goes to which device are gone.

The current roll-out has been at four separate plants with plans for 10 more in the next four years. Expansion of this innovation is being targeted toward the other major manufacturers.

IO-Link Safety: What It Is and Isn’t

Comparing “IO-Link” and “Safety” to “IO-Link Safety”

There are many I/O blocks that have “IO-Link” and “Safety” in their descriptions, which can cause some confusion about which safety features they include. Here’s an overview of different safety-named blocks and how they compare to IO-Link Safety.

Safety Network Blocks

These blocks have I/O ports that use Pin 4 and Pin 2 as OSSD signals (safety ports). OSSD—output switching signal devices—send 24-volt signals over two wires to confirm that a device is operating in a safe condition. If 0 volts are detected in either signal, besides their safety-checking 0-volt pulses, it’s read as a safety event that signals the machine to go into a safe state. Safety network blocks are only for standard (non-network) safety devices. These blocks communicate directly back to a Safety Controller over safety protocols like CIP Safety, PROFIsafe, etc. These blocks typically can monitor between 8-16 standard safety devices. There is no intelligence built into the safety devices.

Safety Network Blocks with IO-Link

Blocks in this category usually have a mixture of I/O ports on them. The ports can range from standard I/O to standard IO-Link communication, and in addition, include ports that use Pin 4 and Pin 2 as OSSD signals (safety ports). These blocks communicate over the safety protocols with only a few ports to connect standard (non-network) safety devices. There is some versatility with these blocks since you can wire standard sensors, IO-Link devices, and safety devices to it. The drawback is, you will always run short of the port style you need and, in the end, use more blocks to cover either the safety or IO-Link needs of the application. There is no intelligence built into the safety devices.

Safety over IO-Link Blocks

In this system/architecture, there are standard IO-Link Masters communicating to the Safety PLCs/Controllers over standard protocols like EtherNet/IP, PROFINET, etc. Connected to the IO-Link Ports of these Masters are Safety over IO-Link devices, currently limited to only Safety over IO-Link hubs. The Safety PLCs/Controllers communicate via safety protocols like PROFIsafe to the standard IO-Link Master, and then using the IO-Link communication channel, they bridge the gap to the Safety over the IO-Link hub via the “black channel.” These Safety over IO-Link hub’s ports use Pin 4 and Pin 2 as OSSD signals (safety ports), so standard (non-network) safety devices can be connected. This system provided a “gap filler” while IO-Link Safety was being developed. In this system/architecture, the standard IO-Link Masters allowed standard IO-Link devices and Safety over IO-Link hubs to be connected to any ports. This brought even more versatility to an application and the beginnings of the benefits of IO-Link. Still, there is no intelligence built into the safety devices.

IO-Link Safety

IO-Link Safety adds a safety communication layer to IO-Link. The difference between this and Safety over IO-Link is that this safety layer applies to both the IO-Link Master and IO-Link Safety devices. Within a CIP Safety or PROFIsafe network, the safety communication protocol has top priority over standard EtherNet/IP or PRIFONET data if both are existing on the same physical network. The same is true for IO-Link Safety: both standard and safety IO-Link protocols can exist on the same physical cable between the IO-Link Master ports and IO-Link Safety devices, with IO-Link Safety carrying the top priority. For a deep dive into the IO-Link Safety protocol, I suggest visiting the IO-Link Consortium’s website at io-link.com. In this system/architecture, you have IO-Link Safety Masters, which communicate to the Safety PLCs/Controllers over safety protocols like CIP Safety, PROFIsafe, etc. The ports on the Masters can utilize Pin 4 and Pin 2 as OSSD signals (safety ports), so standard (non-network) safety devices can be connected. Pin 4 can also be used to carry standard IO-Link and IO-Link Safety communication to standard IO-Link devices and IO-Link Safety devices, respectively. This allows for the most versatile safety solution in the market–IO-Link Safety Masters that can accept standard (non-network) safety devices, standard IO-Link devices, and IO-Link Safety devices. Intelligence in the IO-Link Safety devices is now available.

Benefits of IO-Link Safety

    • IO-Link Safety devices are fieldbus neutral: you just need to specify the IO-Link Safety Master to match the Safety PLCs/Controllers protocol.
    • IO-Link Safety Master port versatility: standard (non-network) safety devices, standard IO-Link devices, and IO-Link Safety devices can be connected.
    • Parameter storage: standard IO-Link and IO-Link Safety device’s parameters can be stored for ease of device replacement.
    • Smart IO-Link Safety device data: more data available, like internal temperature, humidity, number of cycles, power consumption, diagnostics, etc.
    • Simplified wiring: IO-Link Safety devices are still connected to the IO-Link Master port with a standard 3 to 4 conductor cable.
    • IIoT fit: IO-Link Safety gives more visibility to upper-level systems like SCADA, allowing safety device-level monitoring.

I am looking forward to seeing how quickly IO-Link Safety will be accepted, with how IO-Link numbers have skyrocketed over the last few years. The future looks great for IO-Link with IO-Link Safety, IO-Link Wireless and in the future, Single-Pair Ethernet (SPE). With all these new capabilities, what application can’t IO-Link support?

Choosing Sensors Suitable for Automation Welding Environments

Standard sensors and equipment won’t survive for very long in automated welding environments where high temperatures, flying sparks and weld spatter can quickly damage them. Here are some questions to consider when choosing the sensors that best fit such harsh conditions:

    • How close do you need to be to the part?
    • Can you use a photoelectric sensor from a distance?
    • What kind of heat are the sensors going to see?
    • Will the sensors be subject to weld large weld fields?
    • Will the sensors be subject to weld spatter?
    • Will the sensor interfere with the welding process?

Some solutions include using:

    • A PTFE weld spatter resistant and weld field immune sensor
    • A high-temperature sensor
    • A photoelectric diffuse sensor with a glass face for better resistance to weld spatter, while staying as far away as possible from the MIG welding application

Problem, solution

A recent customer was going through two sensors out of four every six hours. These sensors were subject to a lot of heat as they were part of the tooling that was holding the part being welded. So basically, it became a heat sink.

The best solution to this was to add water jackets to the tooling to help cool the area that was being welded. This is typically done in high-temperature welding applications or short cycle times that generate a lot of heat.

    • Solution 1 was to use a 160 Deg C temp sensor to see if the life span would last much longer.
    • Solution 2 was to use a plunger prob mount to get more distance from the weld area.

Using both solutions was the best solution. This increased the life to one week of running before it was necessary to replace the sensor. Still better than two every 6 hours.

Taking the above factors into consideration can make for a happy weld cell if time and care are put into the design of the system. It’s not always easy to get the right solution as some parts are so small or must be placed in tight areas. That’s why there are so many choices.

Following these guidelines will help significantly.

Capacitive, the Other Proximity Sensor

What is the first thing that comes to mind if someone says “proximity sensor?” My guess is the inductive sensor, and justly so because it is the most used sensor in automation today. There are other technologies that use the term proximity in describing the sensing mode, including diffuse or proximity photoelectric sensors that use the reflectivity of the object to change states and proximity mode of ultrasonic sensors that use high-frequency sound waves to detect objects. All these sensors detect objects that are in close proximity to the sensor without making physical contact. One of the most overlooked or forgotten proximity sensors on the market today is the capacitive sensor.

Capacitive sensors are suitable for solving numerous applications. These sensors can be used to detect objects, such as glass, wood, paper, plastic, or ceramic, regardless of material color, texture, or finish. The list goes on and on. Since capacitive sensors can detect virtually anything, they can detect levels of liquids including water, oil, glue, and so forth, and they can detect levels of solids like plastic granules, soap powder, sand, and just about anything else. Levels can be detected either directly, when the sensor touches the medium, or indirectly when it senses the medium through a non-metallic container wall.

Capacitive sensors overview

Like any other sensor, there are certain considerations to account for when applying capacitive, multipurpose sensors, including:

1 – Target

    • Capacitive sensors can detect virtually any material.
    • The target material’s dielectric constant determines the reduction factor of the sensor. Metal / Water > Wood > Plastic > Paper.
    • The target size must be equal to or larger than the sensor face.

2 – Sensing distance

    • The rated sensing distance, or what you see in a catalog, is based on a mild steel target that is the same size as the sensor face.
    • The effective sensing distance considers mounting, supply voltage, and temperature. It is adjusted by the integral potentiometer or other means.
    • Additional influences that affect the sensing distance are the sensor housing shape, sensor face size, and the mounting style of the sensor (flush, non-flush).

3 – Environment

    • Temperatures from 160 to 180°F require special considerations. The high-temperature version sensors should be used in applications above this value.
    • Wet or very humid applications can cause false positives if the dielectric strength of the target is low.
    • In most instances, dust or material buildup can be tuned out if the target dielectric is higher than the dust contamination.

4 – Mounting

    • Installing capacitive sensors is very similar to installing inductive sensors. Flush sensors can be installed flush to the surrounding material. The distance between the sensors is two times the diameter of the sensing distance.
    • Non-flush sensors must have a free area around the sensor at least one diameter of the sensor or the sensing distance.

5 – Connector

    • Quick disconnect – M8 or M12.
    • Potted cable.

6 – Sensor

    • The sensor sensing area or face must be smaller or equal to the target material.
    • Maximum sensing distance is measured on metal – reduction factor will influence all sensing distances.
    • Use flush versions to reduce the effects of the surrounding material. Some plastic sensors will have a reduced sensing range when embedded in metal. Use a flush stainless-steel body to get the full sensing range.

These are just a few things to keep in mind when applying capacitive sensors. There is not “a” capacitive sensor application – but there are many which can be solved cost-effectively and reliably with these sensors.

Converting Analog Signals to Digital for Improved Performance

We live in an analog world, where we experience temperatures, pressures, sounds, colors, etc., in seemingly infinite values. There are infinite temperature values between 70-71 degrees, for example, and an infinite number of pressure values between 50-51 psi.

Sensors today continue to use analog circuitry to measure a natural process, but more often, the electrical analog signal is then converted to a digital (binary) signal.

How a signal is converted from analog to digital?

A variety of mechanical and electrical transducer technologies, such as Bourdon tube, piezoresistive, manometers, strain gages, and capacitive can be found in a typical pressure sensor. Any one of these can be used to sense pressure and convert the physical pressure into an analog electrical signal. The analog output continuously varies as the pressure rises and lowers. For many sensors of the past, the story ends here. The sensor works well if certain precautions are met, but enhanced features are limited. This sensor would be comprised of electrical components, such as diodes, capacitors, op-amps, and resistors, with typical signal outputs of 0-5VDC, 0-10VDC, +/- 10V, 4-20mA, 0-20mA, etc.

Analog output sensors provide an infinitely varying signal and converting it to digital cannot improve the accuracy of the measured value. Nor will it increase the amount of information we receive from the natural world. So why do we do it?

Why convert to digital signals?

There are several good reasons for converting analog to digital signals. Analog uses more power than digital and it’s more difficult to encrypt, decode, or synchronize. Analog outputs also have a slow rate of transmission. But typically, the biggest reasons are that analog signals weaken and pick up electrical noise as they traverse, and they’re difficult to process and store.

Noises and transmission rates

Electrical energy from motors, contactors, and other electrical devices can become induced into the sensor’s analog electronics, creating noise on the signal. Analog amplifiers can increase the signal strength to extend transmission distances, but it also amplifies the induced noise. The transmission of digital signals, on the other hand, is faster and has negligible distortion. And although a digital signal may need an amplifier for long lengths, too, digital regeneration can more easily correct any 0/1 errors and amplify the signal without amplifying any noise.

Converting a continuously variable signal into 1s and 0s

An analog-to-digital converter (ADC) is an integrated circuit that performs the conversion. While this process includes many important steps, and there are several popular techniques, each has three main processes: sampling, quantizing, and encoding.

Sampling is a process used to select a subset of values from a larger set. In our case, we are starting with an infinite set of values from the analog signal and want to capture a snapshot of the signal at certain time intervals. With a sampling rate of 500Hz, the ADC will grab and hold a value from the analog signal 500 times per second.

Once the signal is sampled, it is quantized. This involves mapping the sample from a set of infinite signal values down to a finite number of values. If there were 100 available increments for quantizing a 0-5vdc signal, for example, the infinite output would now be reduced to 100 available signal level choices with 0 volts mapping as 0, 2.5 volts mapping as 50, and 5 volts as 99.

Lastly, the quantized signal level is encoded to binary form, where it can benefit from the processing, storage, and transmission advantages that come with a digital signal. A quantized level of 50, encoding with an 8-bit processor, would be 00011001, equating to a 2.5vdc signal.

In actual practice, we do not use 100 increments to quantize. The ADC, which is based on the number of bits within the processor within the ADC chip, determines the amount of quantizing increments or levels. Eight bits provide 256 increments. Twelve bits provide 4096 increments or steps, as it is also referred.

Is 12 bits worth of increments (4096 steps) enough resolution?

5VDC /4096 steps = .00122V/step or 1.22mV/step

In most applications, a small step of 1.22mV is acceptable. The original analog signal is now sampled at a specific time, and an increment closest to the value is chosen as the signal level. The quantizing process in this case will round the infinite analog value that was sampled to the nearest multiple of 1.22mV.

The output signal is now a square wave, rather than the original sinusoidal. The peak of each square wave is always the same amplitude, with the peak of the wave representing a “1” and the trough or zero amplitude being a “0.”

The sensor output, now digitized, is capable of further processing, offering enhanced product features such as faster transmission rates, negligible distortion, and the ability to communicate to advanced systems such as IO-Link.

A digital to analog converter (DAC) can convert the signal back to analog, but complete restoration is no longer possible due to the samples taken only at specific times, and the quantizing step rounding off to the nearest increment.

So, the next time you see a spec sheet that says “12-bit resolution,” rest assured you are working with a sensor that has some enhanced capabilities.

Inductive Sensors and Their Unlimited Uses in Automation

Inductive sensors (also known as proximity sensors or proxes) are the most commonly used sensors in mechanical engineering and industrial automation. When they were invented in the 1960s, they marked a milestone in the development of control systems. In a nutshell, they generate an electromagnetic field that reacts to metal targets that approach the sensor head. They even work in harsh environments and can solve versatile applications.

There are hardly any industrial machines that work without inductive sensors. So, what can be solved with one, two, three, or more of them?

What can you do with one inductive sensor?

Inductive sensors are often used to detect an end position. This could be in a machine for end-of-travel detection, but also in a hydraulic cylinder or a linear direct drive as an end-of-stroke sensor. In machine control, they detect many positions and trigger other events. Another application is speed monitoring with a tooth wheel.

What can you do with two inductive sensors?

By just adding one more sensor you can get the direction of rotational motion and take the place of a more expensive encoder. In a case where you have a start and end position, this can also be solved with a second inductive sensor.

What can you do with three inductive sensors?

In case of the tooth wheel application, the third sensor can provide a reference signal and the solution turns into a multiturn rotary encoder.

What can I do with four inductive sensors and more?

For multi-point positioning, it may make sense to switch to a measurement solution, which can also be inductive. Beyond that, an array of inductive sensors can solve identification applications: In an array of 2 by 2 sensors, there are already 16 different unique combinations of holes in a hole plate. In an array of 3 by 3, it would be 512 combinations.