Condition Monitoring & Predictive Maintenance: Cost-Benefit Tradeoffs

In a previous blog post, we discussed the basics of the Potential-Failure (P-F) curve, which refers to the interval between the detection of a potential failure and occurrence of a functional failure. In this post we’ll discuss the cost-benefit tradeoffs of various maintenance approaches.

In general, the goal is to maximize the P-F interval, which is the time between the first symptoms of impending failure and the functional failure taking place. In other words, you want to become aware of an impending failure as soon as possible to allow more time for action. This, however, must be balanced with the cost of the methods of prevention, inspection, and detection.

There is a trade-off between the cost of systems to detect and predict the failures and how soon you might detect the condition. Generally, the earlier the detection/prediction, the more expensive it is. However, the longer it takes to detect an impending failure (i.e. the more the asset’s condition degrades), the more expensive it is to repair it.Every asset will have a unique trade-off between the cost of failure prevention (detection/prediction) and the cost of failure. This means some assets probably call for earlier detection methods that come with higher prevention costs like condition monitoring and analytics systems due to the high cost to repair (see the Prevention-1 and Repair-1 curves in the Cost-Failure/Time chart). And some assets may be better suited for more cost-efficient but delayed detection or even a “run-to-failure” model due to lower cost to repair (the Prevention-2 and Repair-2 curves in the Cost-Failure/Time chart).

 

There are four basic Maintenance approaches:

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Reactive

The Reactive approach has low or even no cost to implement but can result in a high repair/failure cost because no action is taken until the asset has reached a fault state. This approach might be appropriate when the cost of monitoring systems is very high compared to the cost of repairing or replacing the asset. As a general guideline, the Reactive approach is not a good strategy for any critical and/or high value assets due to their high cost of a failure.

Reactive approaches:

      • Offer no visibility
      • Fix only if it breaks – low overall equipment effectiveness (OEE)
      • High downtime
      • Uncertainty of failures

Preventative

The Preventative approach (maintenance at time-based intervals) may be appropriate when failures are age related and maintenance can be performed at regular intervals before anticipated failures occur. Two drawbacks to this approach are: 1) the cost and time of preventative maintenance can be high; and 2) studies show that only 18% of failures are age related (source: ARC Advisory Group). 82% of failures are “random” due to improper design/installation, operator error, quality issues, machine overuse, etc. This means that taking the Preventative approach may be spending time and money on unnecessary work, and it may not prevent expensive failures in critical or high value assets.

Preventative approaches:

      • Scheduled tune ups
      • Higher equipment longevity
      • Reduced downtime compared to reactive mode

Condition-Based

The Condition-Based approach attempts to address failures regardless of whether they are age-based or random. Assets are monitored for one or more potential failure indicators, such as vibration, temperature, current/voltage, pressure, etc. The data is often sent to a PLC, local HMI, special processor, or the cloud through an edge gateway. Predefined limits are set and alerts (alarm, operator message, maintenance/repair) are only sent when a limit is reached. This approach avoids unnecessary maintenance and can give warning before a failure occurs. Condition-based monitoring can be very cost-effective, though very sophisticated solutions can be expensive. It is a good solution when the cost of failure is medium or high and known indicators provide a reliable warning of impending failure.

Condition-based approaches:

      • Based on condition (PdM)
      • Enables predictive maintenance
      • Improves OEE, equipment longevity
      • Drastically reduces unplanned downtime

Predictive Analytics

Predictive Analytics is the most sophisticated approach and attempts to learn from machine performance to predict failures. It utilizes data gathered through Condition Monitoring, and then applies analysis or AI/Machine Learning to uncover patterns to predict failures before they occur. The hardware and software to implement Predictive Analytics can be expensive, and this method is best for high-value/critical assets and expensive potential failures.

Predictive Analytics approaches:

      • Based on patterns – stored information
      • Based on machine learning
      • Improves OEE, equipment longevity
      • Avoids downtime

Each user will need to evaluate the unique attributes of their assets and decide on the best approach and trade-offs of the cost of prevention (detection of potential failure) against the cost of repair/failure. In general, a Reactive approach is only best when the cost of failure is very low. Preventative maintenance may be appropriate when failures are clearly age-related. And advanced approaches such as Condition Based monitoring and Predictive Analytics are best when the cost of repair or failure is high.

Also note that technology providers are continually improving condition monitoring and predictive solutions. By lowering condition monitoring system costs and making them easier to set up and use,  users can cost-effectively move from Reactive or Preventative approaches to Condition-Based or Predictive approaches.

Avoid Downtime in Metal Forming With Inductive & Photoelectric Sensors

Industrial sensor technology revolutionized how part placement and object detection are performed in metal forming applications. Inductive proximity sensors came into standard use in the industry in the 1960s as the first non-contact sensor that could detect ferrous and nonferrous metals. Photoelectric sensors detect objects at greater distances. Used together in a stamping environment, these sensors can decrease the possibility of missing material or incorrect placement that can result in a die crash and expensive downtime.

Inductive sensors

In an industrial die press, inductive sensors are placed on the bottom and top of the dies to detect the sheet metal for stamping. The small sensing range of inductive sensors allows operators to confirm that the sheet metal is correctly in place and aligned to ensure that the stamping process creates as little scrap as possible.

In addition, installing barrel-style proximity sensors so that their sensing face is flush with the die structure will confirm the creation of the proper shape. The sensors in place at the correct angles within the die will trigger when the die presses the sheet metal into place. The information these sensors gather within the press effectively make the process visible to operators. Inductive sensors can also detect the direction of scrap material as it is being removed and the movement of finished products.

Photoelectric sensors

Photoelectric sensors in metal forming have two main functions. The first function is part presence, such as confirming that only a single sheet of metal loads into the die, also known as double-blank detection. Doing this requires placing a distance-sensing photoelectric sensor at the entry-way to the die. By measuring the distance to the sheet metal, the sensor can detect the accidental entry of two or more sheets in the press. Running the press with multiple metal sheets can damage the die form and the sensors installed in the die, resulting in expensive downtime while repairing or replacing the damaged parts.

The second typical function of photoelectric sensors verifies the movement of the part out of the press. A photoelectric light grid in place just outside the exit of the press can confirm the movement of material out before the next sheet enters into the press. Additionally, an optical window in place where parts move out will count the parts as they drop into a dunnage bin. These automated verification steps help ensure that stamping processes can move at high speeds with high accuracy.

These examples offer a brief overview of the sensors you mostly commonly find in use in a die press. The exact sensors are specific to the presses and the processes in use by different manufacturers, and the technology the stamping industry uses is constantly changing as it advances. So, as with all industrial automation, selecting the most suitable sensor comes down to the requirements of the individual application.

IO-Link Event Data: How Sensors Tell You How They’re Doing

I have been working with IO-Link for more than 10 years, so I’ve heard lots of questions about how it works. One line of questions I hear from customers is about the operating condition of sensors. “I wish I knew when the IO-Link device loses output power,” or, “I wish my IO-Link photoelectric sensor would let me know when the lens is dirty.” The good news is that it does give you this information by sending Event Data. That’s a type of data that is usually not a focus of users, although it is available in JSON format from the REST API.

There are three types of IO-Link data:

      • Process Data – updated cyclically, it’s important to users because it contains the data for use in the running application, like I/O change of states or measurement values like temperature and position, etc.
      • Parameter Data – updated acyclically, it’s important to users because it’s the mechanism to read and write parameter values like setpoints, thresholds, and configuration settings to the sensor, and for reading non-time critical values like operating hours, etc.
      • Event Data – updated acyclically, it’s important to users because it provides immediate updates on device conditions.

Let’s dig deeper into Event Data. An Event is a status update from the IO-Link device when a condition is out of its normal range. The Event is labeled as a Warning or Error based on the severity of the condition change.

When an Event occurs on the IO-Link device, the device sets the Event Flag bit in the outgoing data packet to the IO-Link Master. The Master receives the Event Flag and then queries the IO-Link device for the Event information.

It is important to note that this is a one-time data message. The IO-Link device only sends the Event Flag at the moment the condition is out of range, and then again when the condition is back in range.

Event Data Types, Modes, and Codes

Event Data has three following three components:

      • Event Type – categorized in three ways
        • Notification – a simple event update; nothing is abnormal with the IO-Link device
        • Warning – a condition is out of range and risks damaging the IO-Link device
        • Error – a condition is out of range and is affecting the device negatively to the point that it may not function as expected
      • Event Mode – categorized in three ways
        • Event notice – usually associated with Event Type notifications, message will not be updated
        • Event appears – the condition is now out of range
        • Event disappears – the condition is now back in range
      • Event Code
        • A two-byte Hex code that represents the condition that is out of range

IO-Link condition monitoring sensor

To bring all these components together, let’s look at a photoelectric IO-Link sensor with internal condition monitoring functions and see what Events are available for it in this device manual screenshot. This device has Events for temperature (both warning and error), voltage, inclination (sensor angle is out of range), vibration, and signal quality (dirty lens).

By monitoring these events, you have a better feel for the conditions of your IO-Link device. Along with helping you identify immediate problems, this can help you in planning preventive and planned maintenance.

An IO-Link condition monitoring sensor uses Event Data similarly to report when conditions exceed the thresholds that you have set. For example, when the vibration level exceeds the threshold value, the IO-Link device sends the Warning event flag and the IO-Link Master queries for the event data. The event data consists of an Event Type, an Event Mode, and an Event Code that represents the specific alarm condition that is out of range. Remember this is a one-time action; the IO-Link sensor will not report this again until the value is in an acceptable range.

When the vibration level is back in range, the alarm condition is no longer present in the IO-Link device, the process repeats itself. In this case the Event Type and Event Code will be the same. The only change is that the Event Mode will report Event Disappears.

Within the IO-Link Specification there is a list of defined Event Codes that are common across all vendors. There is also a block of undefined Event Code values that allow vendors to create Event Codes that are unique to their specific device.

“I wish the IO-Link device would let me know….” In the end, the device might be telling you what you want to know, especially if the device has condition monitoring functions built into it. If you want to know more about condition monitoring in your IO-Link devices, check out the Event section in the vendor’s manuals so you can learn how to use this information.

Control Meets IIoT, Providing Insights into a New World

In manufacturing and automation control, the programmable logic controller (PLC) is an essential tool. And since the PLC is integrated into the machine already, it’s understandable that you might see the PLC as all that you need to do anything in automation on the manufacturing floor.

Condition monitoring in machine automation

For example, process or condition monitoring is emerging as an important automation feature that can help ensure that machines are running smoothly. This can be done by monitoring motor or mechanical vibration, temperature or pressure. You can also add functionality for a machine or line configuration or setup by adding sensors to verify fixture locations for machine configuration at changeovers.

One way to do this is to wire these sensors to the PLC and modify its code and use it as an all-in-one device. After all, it’s on the machine already. But there’s a definite downside to using a PLC this way. Its processing power is limited, and there are limits to the number of additional processes and functions it can run. Why risk possible complications that could impact the reliability of your control systems? There are alternatives.

External monitoring and support processes

Consider using more flexible platforms, such as an edge gateway, Linux, and IO-Link. These external sources open a whole new world of alternatives that provide better reliability and more options for today and the future. It also makes it easier to access and integrate condition monitoring and configuration data into enterprise IT/OT (information technology/operational technology) systems, which PLCs are not well suited to interface with, if they can be integrated at all.

Here are some practical examples of this type of augmented or add-on/retrofit functionality:

      • Motor or pump vibration condition monitoring
      • Support-process related pressure, vibration and temperature monitoring
      • Monitoring of product or process flow
      • Portable battery based/cloud condition monitoring
      • Mold and Die cloud-based cycle/usage monitoring
      • Product changeover, operator guidance system
      • Automatic inventory monitoring warehouse system

Using external systems for these additional functions means you can readily take advantage of the ever-widening availability of more powerful computing systems and the simple connectivity and networking of smart sensors and transducers. Augmenting and improving your control systems with external monitoring and support processes is one of the notable benefits of employing Industrial Internet of Things (IIoT) and Industry 4.0 tools.

The ease of with which you can integrate these systems into IT/OT systems, even including cloud-based access, can dramatically change what is now available for process information-gathering and monitoring and augment processes without touching or effecting the rudimentary control system of new or existing machines or lines. In many cases, external systems can even be added at lower price points than PLC modification, which means they can be more easily justified for their ROI and functionality.

Identify Failures Before They Happen: The PF Curve

The P-F curve is often mentioned in condition monitoring and predictive maintenance discussions. “P-F” refers to the interval between the detection of a potential failure (P) and the occurrence of a functional failure (F).

Fig. 1

The P-F curve is an illustrative generalization of what happens to an asset, machine or component as it ages, degrades, and eventually fails. It shows the different stages of an asset’s life, how machine failures progress, and how and when different symptoms emerge which might signal impending (or actual) failure.

The time scale in Fig. 1 is obviously exaggerated, and most assets operate for a lengthy period of time before failure starts to occur. The steepness of the failure portion of the curve can vary from asset to asset, but it generally follows the same pattern as shown in the diagram.

At first, performance degradation is minor and may not require significant action. As time progresses, the potential failure indicators become stronger and more easily detectable and the performance degradation becomes more severe, eventually ending in catastrophic failure.

The timeline is split into three domains:

      • Proactive domain – the failure is relatively far off (machine may still be new). Proactive activities include designing for reliability, precision installation & alignment and life cycle asset management. These can significantly extend the time until potential and functional failures occur.
      • Predictive domain – the failure may still be far off, but symptoms are emerging and offer (relatively) early warning signs. Timely action may be taken to prevent failure or replace failing equipment before catastrophic failure occurs.
      • Fault domain – the failure is occurring or inevitable, and symptoms indicate immediate action is needed to address the failure.

During these domains, different indicators/symptoms emerge. Ultrasonic, vibration and oil analysis often signal problems early; then temperature rise and noise emerge a bit later; and finally, parts come loose and more severe damage occurs. Depending on the asset, other indicators may be shown by activities including corrosion monitoring, motor current/power analysis and process parameter trending (e.g., flows, rates, pressures, temperatures, etc.).

By analyzing which symptoms of failure are likely to appear in the predictive domain for a given piece of equipment, you can determine which failure indicators to prioritize in your own condition monitoring and predictive maintenance discussions.

Click here to read more about condition monitoring.

Add Automation to Gain Safety and Control in Manufacturing

Industry automation not only has a positive effect on the improvement of production processes, it also significantly improves employee safety. New technologies can minimize the need for employees to work in dangerous situations by replacing them all together or by working cooperatively alongside them.

Overcoming fears of automation
Many workers fear technological progress due to the generally accepted view that robots will replace people in their workplaces. But their fears are conjecture. According to a study published in 2017 by scientists at the Universities of Oxford and Yale, AI experts predict a 50% chance of AI outperforming humans at all tasks within 45 years. But, instead of replacing all workers, there is a stronger chance AI will eliminate dangerous manual labor and evolve other roles. Following are a few examples.

    • Automation in palletizing systems
      Before automation-based solutions entered factories, laborers had to do most work by hand. A work system based on the strength of the human body, however, does not bring good results. Workers tire quickly, causing a decrease in their productivity. And with time, health problems related to regularly carrying heavy daily loads also begin to appear. Until recently, employees of the palletizing departments struggled with these problems. But today, robots are carrying out the work of moving, stacking, and transporting products on pallets.
    • Automation forging processes
      Also, until recently, forging processes in the metallurgical industry were performed with the help of human workers. There are still factories today in which blacksmiths are responsible for putting the hot metal element under the hammer to form the final shape of the product. Such a device hits with a force of several dozen tons, several times a minute. Being at the hammer is therefore extremely dangerous and may cause permanent damage to the worker’s health. Elevated temperatures in the workplace can also have negative effects on the body.

      At
      most businesses, forging processes are now fully automated. Robots specially prepared for such work feed the elements to the automatic hammer with their grippers. And sensory solutions help make the job safer by detecting the presence of people or undesirable elements within the working machine. The quality control of manufactured products is also extremely important and more easily controlled with an automated system.
    • Automation in welding processes
      Welding processes are another dangerous activity in which automation is starting to play a key role. During welding work, toxic fumes are released from the gas lagging, which the welder regularly inhales. This can result in serious poisoning or chronic respiratory diseases. Welding also produces sparks which can lead to severe burns and worker blindness.

      Again, automation makes the process safer. High-class welding machines exist on the market that can work continuously, under human control. With such solutions, it is necessary to use appropriate protection systems to protect employees against possible contact with machines during work. Automation in this situation eliminates a dangerous role, and creates a new, safer, and, some would say, better work role.

Skillful design of automation systems
While factory automation eliminates some threats to workers, others often arise, creating the need for strict design plans prepared by specialists in this field. It is necessary to prepare the automation system in such a way that it not only ensures safety, it does so without reducing productivity or creating downtime which can cause the employee to bypass security systems. The systems blocking the working space of the machine should not interfere with the worker and the worker should not interfere with the system. Where possible, instead of a mechanical lock, an optical curtain at the feeding point should be used to stop the machine’s operation if a foreign object breaks the curtain’s beam of the light. Mechanical locks blocking access to the working space should be in places where it is not necessary to open the door frequently.

Successful human-machine collaboration
When designing automation systems in production companies, it is also necessary to remember that often a human is working alongside the robot. In palletizing systems, for example, a person is responsible for preparing the place for packing and cleaning the working area. For the work to go smoothly, it may be worth creating two positions next to each other. Mechanisms on the market today allow you to control the work of robots at a given position, assigning them to the workspace. Special security scanners prevent the robots from moving to positions where someone is working.

IO-Link Benefits in Robotic Weld Cell Tooling

By Scott Barhorst

Working previously as a controls engineering manager in robotic welding, I have seen some consistent challenges when designing robotic weld cell systems.

For example, the pre-engineered-style welding cells I’ve worked with use many types of tooling. At the same time, space for tooling and cabling is limited, and so is the automation on board, with some using PLC function and others using a robot controller to process data.

One approach that worked well was to use IO-Link in the systems I designed. With its simple open fieldbus communication interface and digital transmission, it brought a number of benefits.

    1.  IO-Link’s digital signals aren’t affected by noise, so I could use smart sensors and connect them with unshielded 4-pin cables.
    2.  Expandability was easy, either from the Master block or by adding discrete I/O modules.
    3.  IO-Link can use the ID of the block to identify the fixture it is associated with to make sure the correct fixture is in the correct location.
    4.  Cabling is simplified with IO-Link, since the IO-Link Master can control both inputs, outputs, and control valve packs. That means that the only cables needed will be 24V power, Ethernet, weld ground (depending on the system), and air.
    5.  Fewer cables means less cost for cables and installation, cable management is improved, and there are fewer cables to run through a tailstock or turntable access hole.

One system I designed used 1 IO-Link Master block, 3 discrete I/O modules, and 1 SMC valve manifold controlled via IO-Link. This tooling had 16 clamps and 10 sensors, requiring 42 total inputs and control of 16 valves. The system worked very well with this setup!

An additional note: It’s good to think beyond the process at hand to how it might be used in the future. A system built on IO-Link is much more adaptable to different tooling when a change-over is needed. Click here to read more about how to use IO-Link in welding environments.

 

 

 

 

 

Add Safety and Accessibility With Remote Amplifiers

Why did the sensor cross the road?

To work remotely, of course.

Even sensors are working remotely these days, and some have good reason. Many applications dictate that the sensing element be placed remotely from its associated electronics. Let’s looks at a few common examples of this.

This may be for safety’s sake, such as in oil and gas applications where housing the bulk of the electronics away from a hazardous area reduces the likelihood of an electrical discharge, or where there are environmental concerns, such as temperature or vibration. By placing the majority of the electronics safely away, only the minimal number of components are subjected to the extremes.

Another good reason for remote placement is accessibility. In some cases, for example, the sensor must be mounted in a difficult to reach place, and having remote electronics installed in a more accessible location allows for easier access for the needed periodic re-teaching, adjusting, etc.

Separate electronics are also used when the sensing element needs to be designed into a very tight space. These very small sensor elements are likely to be customized to fit into a device directly, often leaving no room for the remainder of the electronics.

Remote placement is typically used out of necessity, but it doesn’t have to limit sensor capability or performance.

A typical amplifier with jog button, selector switch, and display.
Typical amplifier with jog button, selector switch, and display

Separately housed electronics, known as amplifiers, can do more than just house the electronics that support the sensing elements; they also provide a way to configure the sensors through buttons and displays. The amplifier delivers the smart features that larger sensors possess, without increasing the sensor size.

Let’s take a look at an amplifier designed to work with the micromote photoelectric sensors.

Micromote photoelectric sensor with 2mm diameter.
Micromote photoelectric sensor with 2mm diameter

Micromotes are extremely small photoelectric sensors that direct a very tight beam of collimated light at a target. The light emission is specifically engineered for the application, either attenuating or refracting as it interacts with the object to be detected. Many of these applications involve detecting very small bubbles in a stream of fluid, micro-bubbles that are smaller than the human eye can detect.  Others may be used to detect the edge of a microscope slide or count very small drops of liquid.  They are precision engineered to detect small objects in small spaces.

The amplifier will receive a power source, and in return it will provide power to the sensing element. But beyond the supporting electronics, what else might a good amp do?

    • Provide a choice of output types (PNP/NPN/Analog/NO/NC)
    • Supply an adequate frequency response for the fast counting of objects
    • Use LED indicators to help troubleshoot connections and warn of unstable signals
    • Provide on/off signal delays (pulse stretching) for those super fast applications
    • Allow the signal hysteresis to be adjusted to suit the application
    • Provide a way to lock the set parameters from inadvertent changes
    • Offer an alarm output if the application is out of specified limits
    • Include a display to navigate through the menus and to display signal strength when operating
    • Teach the application through the use of selector switches
    • Deliver auto synchronization

So, the next time you have a demanding application that requires a sensor to work remotely, consider a premium amplifier — one that not only supports the sensing element, but provides the smart features that today’s best sensors offer. You just might find that working remotely has many advantages, including a more integrated final product, which is more accessible to tune, and with additional features.

Evolution of Pneumatic Cylinder Sensors

Today’s pneumatic cylinders are compact, reliable, and cost-effective prime movers for automated equipment. They’re used in many applications, such as machinery, material handling, assembly, robotics, and medical. One challenge facing OEMs, integrators and end users is how to detect reliably whether the cylinder is fully extended, retracted, or positioned somewhere in between before allowing machine movement.

A widely used method for cylinder position detection is to attach magnetically actuated switches or sensors to the sides of the cylinder using brackets, or by inserting them into a slot extruded into the body of the cylinder. Magnetic field sensors detect an internal magnet that is mounted on the moving piston through the aluminum cylinder wall.

The selection of which type of magnetic sensors to use depends on your application needs and specific data requirements.

Magnetic Sensor Types

Reed switches

The reed switch is the most simplistic and most often used end-of-stroke sensor available on the market. It consists of two flattened ferromagnetic nickel and iron reed elements enclosed in a hermetically sealed glass tube. The tube aids in minimizing contact arcing and prevents moisture from getting to the switch elements. As an axially aligned magnet approaches the switch element, the reed elements are magnetized and attracted together completing the circuit.

AMR and GMR sensors

Most cylinder manufacturers and OEMs use electronic sensors with either magnetoresistive technology (AMR) or giant magnetoresistive (GMR). Both versions are based on a change in resistance. One advantage of these sensors is that they will work with the axially magnetized magnet and, in some cases, the radially magnetized magnet. GMR sensors can be physically smaller than the AMR sensors. They are more sensitive, more precise and have a better hysteresis. Versions exist that provide reverse polarity protection, overload protection, and short circuit protection.

The initial cost of an AMR or GMR sensor may be slightly more than a reed sensor, however, this cost is increasingly less, especially if you figure the cost of downtime when the reed switch fails. AMR and GMR sensors are also three-wire devices, unlike the two-wire reed switches. In the end, the AMR and GMR sensors are the better solution since there are no moving parts and they typically last much longer than the reed switch.

Position detection sensors for both C-slots and T-slots

Pneumatic cylinders typically have either a C-slot or T-slot feature in the extrusion of the cylinder body. Many sensor housings have these same housing profiles and the sensor can either be dropped into the slot from above and tightened with a screw or slid in from the end of the cylinder provided there is no end plate. For round cylinders or tie rod cylinders, additional brackets are available that can use either a C-slot or T-slot sensor. This allows for commonality of sensors for end users and OEMs to meet the needs of many applications and reduce the number of sensor part numbers and inventory.

Today, there are more options than ever for piston position detection in pneumatic cylinders, including different housing styles to meet the cylinder extrusions. Also available are two sensors – one for extended and one for retracted – that share a single, four-pin connection. These magnetic sensors are also available now with weld field immunity for harsh welding applications.

Technology has advanced as well. Now cylinder sensors can be taught to trigger at certain points along the travel of the piston. The user simply moves the piston to a desired location and presses a button to set the switching location. This teachable sensor can also be connected to IO-Link, allowing up to eight switching points for flexibility in several applications.

Over the years, many users have abandoned reed switches, due to their failure rate, in favor of mechanical or inductive sensors to detect pneumatic cylinder position. AMR and GMR sensors are smaller, faster, easy to integrate, and are much more reliable. With the vast improvements in sensor technology, AMR and GMR sensors should now be considered the primary solution for detecting cylinder position.

Start Condition Monitoring With Vibration Sensors

IIOT (Industrial internet of things) has gained much traction and attraction in past years. With industries getting their assets online for monitoring purposes and new IO-Link sensors providing a ton of information on a single package, monitoring machines has become economically feasible.

Vibration is one of the most critical metrics regarding the health of machines, providing early detection of potential faults – before they cause damage or equipment failure. But since this is a relatively new field and use case, there is not much information about it. Most customers are confused about where to start. They want a baseline to begin monitoring machines and then finetune them to their use case.

“Vibration is one of the most critical metrics regarding the health of machines…”

One approach to solve this is to hire a vibration expert to determine the baseline and the best location to mount the vibration measuring sensor. Proper setup increases the threshold of getting into condition monitoring as a new user figures out the feasibility of such systems.

I direct my customers to this standardized baseline chart from ISO, so they can determine their own baselines and the best mounting positions for their sensors. The table shows the different standards for severity for different machine classes. These standards detail the baseline vibration and show the best place to mount the sensor based on the machine type.

Click here for more information on the benefits of condition monitoring.