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Machine Failures and Condition Monitoring – Selecting Sensors

In previous blogs, we discussed the different types of machine failures and their implications for different maintenance approaches, the cost-benefit tradeoffs of these maintenance approaches, and the progression of machine failures and indicators that emerge at various failure phases. We now will connect the different failure indicators to the sensors which can detect them.

The Potential – Functional Failure (P-F) curve gives a rough picture of when various indicators may emerge during the progression of a failure:

Each indicator can be detected by one or more types of sensors. Selection of the “best” sensor will depend on the machine/asset being monitored, other attributes being sensed, budget/cost-benefit tradeoff, and the maintenance approach. In some cases, a single-purpose, dedicated condition-monitoring sensor may be the right choice. In other cases, a multi-function sensor (“Smart Automation and Monitoring System sensor”) which can handle both condition monitoring and standard sensing tasks may be an elegant and cost-effective solution.

The table below gives some guidance to possible single- and multi-function sensors which can address the various indicators:

* Condition monitoring sensors are specialized sensors that can often detect multiple indicators including vibration, temperature, humidity, and ambient pressure.

# Smart Automation and Monitoring System sensors add condition monitoring sensing, such as vibration and temperature, to their standard sensing functions, such as photoelectric, inductive, or capacitive sensing

There is a wide range of sensors that can provide the information needed for condition monitoring indication. The table above can provide some guidance, selecting the best fit requires an evaluation of the application, the costs/benefits, and fit with the maintenance strategy.

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Industrial Machinery Failure Types and Implications for Maintenance Approaches

Industrial machinery can fail in many different ways and for many different reasons. For critical and/or expensive equipment, it is a major challenge to find a way to detect potential failures before they happen and to take action to prevent or minimize the effects. Closely tied to this is the tradeoff between the cost of detection and the cost of failure. We discussed some of these tradeoffs in the blog “Condition Monitoring & Predictive Maintenance: Cost-Benefit Tradeoffs.”

When assessing how equipment might fail, several industry studies* have identified six primary failure types which may be considered:

    • Type A: Lower probability of failure in early- and mid-life of the asset, with a dramatic increase in probability of failure in late-life. This is typical for mechanical devices, such as engines, fans, compressors, and machines.
    • Type B: Higher initial probability of failure when the asset is new, with a much lower/steady failure probability over the rest of the asset’s life. This is often the profile for electronic devices such as computers, PLCs, etc.
    • Type C: Lower initial probability of failure when the asset is new, with an increase to a steady failure probability in mid- and late-life. These are often devices with high stress work conditions, such as pressure relief valves.
    • Type D: Consistent probability of failure throughout the asset life, similar failure probability in early-, mid- and late-life. This can cover many types of industrial machines, often with stable, proven design and components.
    • Type E: Higher probability of failure in early- and late-life, a lower and constant probability of failure in mid-life (often called a “bathtub curve”). This can be devices that “settle in” after a wear-in period and then experience higher failures at the end of life, such as bearings.
    • Type F: Lower probability of failure when new, with a gradual increase over time and without the steep increase in failure probability at the end of life of Type A. This is often typical where age-based wear is steady and gradual in equipment such as turbine engines and structural components (pressure vessels, beams, etc.).

Age-related and non-age-related failures

These six failure types fall into two categories: age-related and non-age-related failures. The studies show that 15-30% of failures are age-related (Types A, E & F) and 70-85% of failures are non-age-related (Types B, C & D). The age-related failures have a clear correlation between the age of the asset and the likelihood of failure. In these cases, preventative maintenance at regular time-based intervals may be appropriate and cost-effective. The non-age-based failures are more “random,” due to improper design/installation, operator error, quality issues, machine overuse, etc. In these cases, preventative maintenance will likely not prevent failure and may waste time and money on unnecessary maintenance.

Table is based on data from studies conducted by United Airlines (1978), Broberg (1973), U.S. Navy (1993 MSDP) and U.S. Navy (2001 SUBMEPP) and ARC Consulting

The fact that approximately 80% of failures are non-age-related has major implications for manufacturers trying to decide on a maintenance approach. The traditional preventative-maintenance approach is not likely to address these failures and may even cause failures when improperly done. It is therefore important to consider a more proactive approach, such as condition-based monitoring or predictive maintenance, especially for assets that are critical to the process and/or expensive.

Preventative maintenance and regular inspection may be a good approach for assets more likely to experience age-based failures in Types A, E, and F. These include fans, bearings, and structural components – and in many cases, the cost of condition monitoring or predictive maintenance may not be worth the cost. But for critical components or equipment, such as bearings on an expensive milling machine or transfer line, it may be worthwhile to apply condition monitoring or predictive maintenance.

And when the assets are more likely to experience non-age-related failures (Types B, C, and D), the proactive approaches are better. Many industrial machines and industrial control/motion components fall into this category, and condition monitoring or predictive maintenance can significantly reduce preventative maintenance costs and unplanned failures while improving machine uptime and Overall Equipment Effectiveness.

You can use this information to improve your maintenance operations. Start by considering your maintenance approach(es), especially for your most critical assets:

    • Are they more likely to experience age-related failures or non-age-related failures?
    • Should you change your maintenance approach to be more proactive?
    • What components and indicators should you measure?

We’ll discuss ideas on how to assess your equipment for condition monitoring/predictive maintenance and what you might measure in separate blogs.

* Studies conducted by United Airlines (1978), Broberg (1973), U.S. Navy (1993 MSDP) and U.S. Navy (2001 SUBMEPP)

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Condition Monitoring & Predictive Maintenance: Addressing Key Topics in Packaging

A recent study by the Packaging Machinery Manufacturers Institute (PMMI) and Interact Analysis takes a close look at packaging industry interest and needs for Condition Monitoring and Predictive Maintenance. Customer feedback reveals interesting data on packaging process pain points and the types of machines and components which are best monitored, the data which should be gathered, current maintenance approaches, and the opportunity for a better way: Condition Monitoring and Predictive Maintenance.

What keeps customers awake at night?

The PMMI survey indicates that form, fill & seal machines are very critical to packaging processes and more likely to fail than many other machines. Also critical to the process and a common failure point are filling & dosing machines, and labeling machines.

These three categories of machines are in use in primary packaging and are often the key components in the production line; the downstream processes are usually less critical. They often process a lot of perishable products at high speeds, therefore, any downtime is a big problem for overall equipment effectiveness (OEE), quality, and profitability.

In terms of the components on these machines that are most likely to fail, the ones are pneumatic systems, gearboxes, motors/drives, and sensors.

How can customers reduce unplanned downtime and improve OEE?

Our data shows that the top customer issue is unplanned machine breakdowns, but many packaging firms use reactive or preventative maintenance approaches, which may not be effective for most failures. An ARC study found that only about 20% of failures are age-related. The 80% of failures that are non-age-related would likely not be addressed by reactive or preventative maintenance programs.

A better way to address these potential failures is to monitor the condition of critical machines and components. Condition monitoring can provide early detection of machine deterioration or impending failure and the data can be used for predictive maintenance. Many “smart sensors” can now measure vibration, temperature, humidity, pressure, flow, inclination, and many other attributes which may be helpful in notifying users of emerging problems. And some of these “smart sensors” can also “self-monitor” and help alert users to potential failures in the sensor itself.

What are packaging customers actually doing?

The good news is that the packaging industry is moving forward to find a better way and users understand that Condition Monitoring/Predictive Maintenance gives them the opportunity to prevent unplanned failures, reduce unplanned downtime, and improve OEE, quality and profitability. About 25% of customers have already implemented some sort of Condition Monitoring / Predictive Maintenance, while about 20% are piloting it and 30% plan to implement it. This means that 75% of customers are very interested in Condition Monitoring/Predictive Maintenance, by far the most interest in any technology discussed in the PMMI survey.

Where do you start?

    • Look for the machines which cause you the most frustration. PMMI identified form, fill & seal, filling & dosing, and labeling machines, but there are other machines, including bottling, cartoning, and case/tray handling, that could fail and cause production downtime or damaged product.
    • Consider where, when, and how equipment can fail. Look to your own experience, ask partners with similar machines or perhaps the equipment supplier to help you determine the most common failure points and modes.
    • Analyze which parts of the machine fail. Moving parts are usually the highest potential failure point. On packaging machines, these include motors, gearboxes, fans, pumps, bearings, conveyors, and shafts.
    • Consider what to measure. Vibration is common, and often assessed in combination with temperature and humidity. On some machines, pressure, flow, or amperage/voltage should be measured.
    • Determine the most appropriate maintenance program for each machine. Consider the costs/benefits of reactive, preventative, condition-based monitoring or predictive approaches. In some cases, it may be OK to let a non-critical, low-value asset “run-to-failure,” while in other cases it might be worth investing in Condition Monitoring or Predictive Maintenance to prevent a critical machine’s costly failure.
    • Start small by implementing condition monitoring on one or two machines, and then scaling up once you’ve learned what does and doesn’t work. Using a low-cost sensor, which can be easily integrated with existing controls architectures or added on externally, is also a great way to start.

Condition Monitoring and Predictive Maintenance offer packaging firms a “better way” to address key topics including machine downtime, failures, and OEE. Users can move from a reactive to a proactive maintenance approach by monitoring attributes such as vibration and temperature on critical machines and then analyzing the data. This will allow them to detect and predict potential failures before they become critical, and thereby, reduce unplanned downtime, improve OEE, and save money.

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The 5 Most Common Types of Fixed Industrial Robots

The International Federation of Robotics (IFR) defines five types of fixed industrial robots: Cartesian/Gantry, SCARA, Articulated, Parallel/Delta and Cylindrical (mobile robots are not included in the “fixed” robot category). These types are generally classified by their mechanical structure, which dictates the ways they can move.

Based on the current market situation and trends, we have modified this list by removing Cylindrical robots and adding Power & Force Limited Collaborative robots. Cylindrical robots have a small, declining share of the market and some industry analysts predict that they will be completely replaced by SCARA robots, which can cover similar applications at higher speed and performance. On the other hand, use of collaborative robots has grown rapidly since their first commercial sale by Universal Robots in 2008. This is why collaborative robots are on our list and cylindrical/spherical robots are not.

Therefore, our list of the top five industrial robot types includes:

    • Articulated
    • Cartesian/Gantry
    • Parallel/Delta
    • SCARA
    • Power & Force Limited Collaborative robots

These five common types of robots have emerged to address different applications, though there is now some overlap in the applications they serve. And range of industries where they are used is now very wide. The IFR’s 2021 report ranks electronics/electrical, automotive, metal & machinery, plastic and chemical products and food as the industries most commonly using fixed industrial robots. And the top applications identified in the report are material/parts handling and machine loading/unloading, welding, assembling, cleanrooms, dispensing/painting and processing/machining.

Articulated robots

Articulated robots most closely resemble a human arm and have multiple rotary joints–the most common versions have six axes. These can be large, powerful robots, capable of moving heavy loads precisely at moderate speeds. Smaller versions are available for precise movement of lighter loads. These robots have the largest market share (≈60%) and are growing between 5–10% per year.

Articulated robots are used across many industries and applications. Automotive has the biggest user base, but they are also used in other industries such as packaging, metalworking, plastics and electronics. Applications include material & parts handling (including machine loading & unloading, picking & placing and palletizing), assembling (ranging from small to large parts), welding, painting, and processing (machining, grinding, polishing).

SCARA robots

A SCARA robot is a “Selective Compliance Assembly Robot Arm,” also known as a “Selective Compliance Articulated Robot Arm.” They are compliant in the X-Y direction but rigid in the Z direction. These robots are fairly common, with around 15% market share and a 5-10% per year growth rate.

SCARA robots are most often applied in the Life Sciences, Semiconductor and Electronics industries. They are used in applications requiring high speed and high accuracy such as assembling, handling or picking & placing of lightweight parts, but also in 3D printing and dispensing.

Cartesian/Gantry robots

Cartesian robots, also known as gantry or linear robots, move along multiple linear axes. Since these axes are very rigid, they can precisely move heavy payloads, though this also means they require a lot of space. They have about 15% market share and a 5-10% per year growth rate.

Cartesian robots are often used in handling, loading/unloading, sorting & storing and picking & placing applications, but also in welding, assembling and machining. Industries using these robots include automotive, packaging, food & beverage, aerospace, heavy engineering and semiconductor.

Delta/Parallel robots

Delta robots (also known as parallel robots) are lightweight, high-speed robots, usually for fast handling of small and lightweight products or parts. They have a unique configuration with three or four lightweight arms arranged in parallelograms. These robots have 5% market share and a 3–5% growth rate.

They are often used in food or small part handling and/or packaging. Typical applications are assembling, picking & placing and packaging. Industries include food & beverage, cosmetics, packaging, electronics/ semiconductor, consumer goods, pharmaceutical and medical.

Power & Force Limiting Collaborative robots

We add the term “Power & Force Limiting” to our Collaborative robot category because the standards actually define four collaborative robot application modes, and we want to focus on this, the most well-known mode. Click here to read a blog on the different collaborative modes. Power & Force Limiting robots include models from Universal Robots, the FANUC CR green robots and the YuMi from ABB. Collaborative robots have become popular due to their ease of use, flexibility and “built-in” safety and ability to be used in close proximity to humans. They are most often an articulated robot with special features to limit power and force exerted by the axes to allow close, safe operation near humans or other machines. Larger, faster and stronger robots can also be used in collaborative applications with the addition of safety sensors and special programming.

Power & Force Limiting Collaborative robots have about 5% market share and sales are growing rapidly at 20%+ per year. They are a big success with small and mid-size enterprises, but also with more traditional robot users in a very broad range of industries including automotive and electronics. Typical applications include machine loading/unloading, assembling, handling, dispensing, picking & placing, palletizing, and welding.

Summary­

The robot market is one of the most rapidly growing segments of the industrial automation industry. The need for more automation and robots is driven by factors such as supply chain issues, changing workforce, cost pressures, digitalization and mass customization (highly flexible manufacturing). A broad range of robot types, capabilities and price points have emerged to address these factors and satisfy the needs of applications and industries ranging from automotive to food & beverage to life sciences.

Note: Market share and growth rate estimates in this blog are based on public data published by the International Federation of Robotics, Loup Ventures, NIST and Interact Analysis.

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Condition Monitoring & Predictive Maintenance: Machine Failure Indicators & Detection Methods

In our previous blogs, we discussed the basics of the P-F (Potential – Functional Failure) curve and the cost-benefit tradeoffs of various maintenance approaches. We’ll now describe the measures that can be taken to discover failure indicators along the P-F curve.The basic concept of the P-F curve is that as a machine or asset deteriorates, various symptoms/indicators emerge. The early-stage indicators may be harder to detect and may require more sophisticated and expensive systems to analyze, but they give you more time to take action to prevent a catastrophic failure. They allow users to choose times to service a machine when it’s less disruptive to the manufacturing process and when only minor maintenance actions, such as changing lubricant, replacing a filter or balancing a fan, are needed rather than major parts repair/replacement. The later-stage indicators may be more obvious and simpler to notice, but they may require extensive and expensive maintenance since greater deterioration has taken place.

Some monitoring methods can be done on a continual basis by using a permanently mounted sensor that takes samples at intervals of once an hour or more often. Others can only be done on a one-time or periodic basis, as when a sensor is brought in for special analysis, perhaps once a month or less often.

Common indicators and detection methods

This version of the P-F curve lists several common indicators and detection methods, in rough order of when they might start to reveal deterioration in an asset:

    • Ultrasonic Spike Energy. Ultrasonic condition monitoring sensors are often expensive and used in portable systems to take one-time readings, but they can provide very early potential failure detection.
    • Vibration Analysis. Sensors and evaluation tools can range from very simple and low cost to sophisticated and expensive. The vibration analysis is done on either a one-time, periodic, or continual basis and often gives an early insight into emerging problems.
    • Oil Analysis. An oil analysis may signal the need for additional, relatively simple maintenance actions, such as lubricating bearings, changing lubricant, or scheduling maintenance. This can usually be done on a one-time basis, but perhaps periodically, such as monthly or annually.
    • Temperature Analysis. This analysis can indicate emerging “hot spots” on a machine, such as bad bearings or excessive friction that signal a future failure. Depending on the measurement system and asset, it can be an early or a late indicator of impending failure.
    • Pressure & Flow. These indicators can fall into either the predictive or the fault domain, depending on implementation. If a proactive approach is taken, they might be condition indicators that can provide an early indication of potential failure; if a reactive approach is taken, they might be indicators of a functional failure (failure already occurring).
    • Audible Noise. Noise is often an indicator of deterioration moving into the fault domain, and requiring more immediate action than vibration, temperature, or ultrasonic indicators.
    • Hot to Touch. Generally, once bearings, motors, or shafts become hot to the touch, failure is imminent and quick action is needed to avoid catastrophic failure.
    • Mechanically Loose. This indicator may fall into preventative maintenance (maintenance performed at time-based intervals rather than based on need) and may not catch impending failures until it is too late. Parts, which are obviously loose, can indicate a deeper problem, often close to failure.
    • Ancillary Damage. This detects when other parts of the machine/assets are being damaged prior to a catastrophic failure (for example, a damaged belt due to belt misalignment caused by a failing bearing). Generally, when this is found, it is too late to prevent the failure of the asset.

This list does not cover all possible indicators. Machine users and builders may have others depending on their unique application – other potential methods to detect asset deterioration include monitoring of current, corrosion, or leaks.

The “best” indicator and approach will depend on each user’s and each asset’s unique risk/cost/benefit profile. Machine builders and users should work closely with an experienced condition monitoring solution provider who provides multiple solutions to help consider and assess the tradeoffs associated with various approaches.

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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:

:

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.

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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).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.

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Sensor and Device Connectivity Solutions For Collaborative Robots

Sensors and peripheral devices are a critical part of any robot system, including collaborative applications. A wide variety of sensors and devices are used on and around robots along with actuation and signaling devices. Integrating these and connecting them to the robot control system and network can present challenges due to multiple/long cables, slip rings, many terminations, high costs to connect, inflexible configurations and difficult troubleshooting. But device level protocols, such as IO-Link, provide simpler, cost-effective and “open” ways to connect these sensors to the control system.

Just as the human body requires eyes, ears, skin, nose and tongue to sense the environment around it so that action can be taken, a collaborative robot needs sensors to complete its programmed tasks. We’ve discussed the four modes of collaborative operation in previous blogs, detailing how each mode has special safety/sensing needs, but they have common needs to detect work material, fixtures, gripper position, force, quality and other aspects of the manufacturing process. This is where sensors come in.

Typical collaborative robot sensors include inductive, photoelectric, capacitive, vision, magnetic, safety and other types of sensors. These sensors help the robot detect the position, orientation, type of objects, and it’s own position, and move accurately and safely within its surroundings. Other devices around a robot include valves, RFID readers/writers, indicator lights, actuators, power supplies and more.

The table, below, considers the four collaborative modes and the use of different types of sensors in these modes:

Table 1.JPG

But how can users easily and cost-effectively connect this many sensors and devices to the robot control system? One solution is IO-Link. In the past, robot users would run cables from each sensor to the control system, resulting in long cable runs, wiring difficulties (cutting, stripping, terminating, labeling) and challenges with troubleshooting. IO-Link solves these issues through simple point-to-point wiring using off-the-shelf cables.

Table 2.png

Collaborative (and traditional) robot users face many challenges when connecting sensors and peripheral devices to their control systems. IO-Link addresses many of these issues and can offer significant benefits:

  • Reduced wiring through a single field network connection to hubs
  • Simple connectivity using off-the-shelf cables with plug connectors
  • Compatible will all major industrial Ethernet-based protocols
  • Easy tool change with Inductive Couplers
  • Advanced data/diagnostics
  • Parametarization of field devices
  • Faster/simpler troubleshooting
  • Support for implementation of IIoT/Industry 4.0 solutions

IO-Link: an excellent solution for simple, easy, fast and cost-effective device connection to collaborative robots.

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Power & Force Limiting Cobots for Dull, Dirty and Dangerous Applications

Collaborative robots, or cobots, is currently one of the most exciting topics in automation. But what do people mean when they say “collaborative robot”? Generally, they are talking about robots which can safely work near and together with humans. The goal of a collaborative robot system is to optimize the use of humans and robots, building on the capabilities of each.

There are four modes of robot collaborative operation defined by the global standard ISO/TS 15066. We discussed these modes in a previous blog, Robot Collaborative Operation.

This post will go more deeply into the most commonly used mode: power & force limiting. Robots in this category include ones made by Universal Robots, as well as FANUC’s green robots and ABB’s Yumi.

What is power & force limiting?

Power & force limiting robots are designed with limited power and force, along with physical features to avoid or reduce injury or damage in case of contact. These robots are generally smaller, slower and less powerful than traditional robots but also more flexible and able to work near or with humans — assuming a risk assessment determines it is safe to do so.

The standards define the creation of a shared or collaborative work space for the robot and human, and define how they may interact in this space. In a power & force limiting application, the robot and operator can be in the shared/collaborative work space at the same time and there may be contact or collision between the operator and the collaborative robot system (which includes the robot, gripper/tool and work piece). Under the proper conditions the features built into the power & force limiting robot allow this close interaction and contact to occur without danger to the operator.

What technologies allow these robots to work closely with humans?

The limiting of the robot’s force can be implemented in several ways. Internal torque/feedback sensors in the joints, external sensors or “skins” and/or elastic joints are some of the methods robot suppliers use to assure low force or low impact. They also design possible contact areas to avoid injury or damage by using rounded edges, padding, large surface areas, etc. to soften contact. Grippers, tools and work pieces also need to be considered and designed to avoid injury or damage to people and equipment.

Peripherally, additional sensors in the robots, grippers, tools, work holders and surrounding work stations are critical parts of high performance robot applications. Connecting these sensors through protocols such as IO-Link and PROFISafe Over IO-Link allows more tightly integrated, better performing, and safer collaborative robot systems.

Where are power & force limiting robots typically applied?

Similar to traditional robots, power & force limiting robots are best applied in applications which are dull, dirty and/or dangerous (the 3 Ds of robotics). They are especially well suited to applications where the danger is ergonomic — repetitive tasks which cause strain on an operator. In many cases, power & force limiting robots are being applied to cooperate closely with people: the robots take on the repetitive tasks, while the humans take on the tasks which require more cognitive skills.

A large number of the customers for power & force limiting robots are small or medium-sized enterprises which can not afford the investment and time to implement a traditional robot, but find that power & force limiting robots fit within their budget and technical capabilities.

What are some of the benefits and drawbacks to power & force limiting robots?

Benefits:

  • Low cost
  • Fast, simple programming and set up; often does not require special knowledge or training
  • Small and lightweight
  • Easy to deploy and redeploy
  • Can be fenceless
  • Low power usage
  • Close human-robot interaction

Drawbacks:

  • Slow
  • Small payload
  • Low force
  • Low precision (not always the case, and improving)

Final Thoughts

Buying a power & force limiting robot does not necessarily mean that fences or other safeguards can be removed; a risk assessment must be completed in order to ensure the application is appropriately safeguarded. The benefits, however, can be significant, especially for smaller firms with limited resources. These firms will find that power & force limiting robots are very good at cost-effectively solving many of their dull, dirty and dangerous applications.

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The Emergence of Device-level Safety Communications in Manufacturing

Manufacturing is rapidly changing, driven by trends such as low volume/high mix, shorter lifecycles, changing labor dynamics and other global factors. One way industry is responding to these trends is by changing the way humans and machines safely work together, enabled by updated standards and new technologies including safety communications.

In the past, safety systems utilized hard-wired connections, often resulting in long cable runs, large wire bundles, difficult troubleshooting and inflexible designs. The more recent shift to safety networks addresses these issues and allows fast, secure and reliable communications between the various components in a safety control system. Another benefit of these communications systems is that they are key elements in implementing the Industrial Internet of Things (IIoT) and Industry 4.0 solutions.

Within a typical factory, there are three or more communications levels, including an Enterprise level (Ethernet), a Control level (Ethernet based industrial protocol) and a Device/sensor level (various technologies). The popularity of control and device level industrial communications for standard control systems has led to strong demand for similar safety communications solutions.

Safety architectures based on the most popular control level protocols are now common and often reside on the same physical media, thereby simplifying wiring and control schemes. The table, below, includes a list of the most common safety control level protocols with their Ethernet-based industrial “parent” protocols and the governing organizations:

Ethernet Based Safety Protocol Ethernet Based Control Protocol Governing Organization
CIP Safety Ethernet IP Open DeviceNet Vendor Association (ODVA)
PROFISafe PROFINET PROFIBUS and PROFINET International (PI)
Fail Safe over EtherCAT (FSoE) EtherCAT EtherCAT Technology Group
CC-Link IE Safety CC-Link IE CC-Link Partner Association
openSAFETY Ethernet POWERLINK Ethernet POWERLINK Standardization Group (EPSG)

 

These Ethernet-based safety protocols are high speed, can carry fairly large amounts of information and are excellent for exchanging data between higher level devices such as safety PLCs, drives, CNCs, HMIs, motion controllers, remote safety I/O and advanced safety devices. Ethernet is familiar to most customers, and these protocols are open and supported by many vendors and device suppliers – customers can create systems utilizing products from multiple suppliers. One drawback, however, is that devices compatible with one protocol are not compatible with other protocols, requiring vendors to offer multiple communication connection options for their devices. Other drawbacks include the high cost to connect, the need to use one IP address per connected device and strong influence by a single supplier over some protocols.

Device level safety protocols are fairly new and less common, and realize many of the same benefits as the Ethernet-based safety protocols while addressing some of the drawbacks. As with Ethernet protocols, a wide variety of safety devices can be connected (often from a range of suppliers), wiring and troubleshooting are simplified, and more data can be gathered than with hard wiring. The disadvantages are that they are usually slower, carry much less data and cover shorter distances than Ethernet protocols. On the other hand, device connections are physically smaller, much less expensive and do not use up IP addresses, allowing the integration into small, low cost devices including E-stops, safety switches, inductive safety sensors and simple safety light curtains.

Device level Safety Protocol Device level Standard Protocol Open or Proprietary Governing Organization
Safety Over IO-Link/IO-Link Safety* IO-Link Semi-open/Open Balluff/IO-Link Consortium
AS-Interface Safety at Work (ASISafe) AS-Interface (AS-I) Open AS-International
Flexi Loop Proprietary Sick GmbH
GuardLink Proprietary Rockwell Automation

* Safety Over IO-Link is the first implementation of safety and IO-Link. The specification for IO-Link Safety was released recently and devices are not yet available.

The awareness of, and the need for, device level safety communications will increase with the desire to more tightly integrate safety and standard sensors into control systems. This will be driven by the need to:

  • Reduce and simplify wiring
  • Add flexibility to scale up, down or change solutions
  • Improve troubleshooting
  • Mix of best-in-class components from a variety of suppliers to optimize solutions
  • Gather and distribute IIoT data upwards to higher level systems

Many users are realizing that neither an Ethernet-based safety protocol, nor a device level safety protocol can meet all their needs, especially if they are trying to implement a cost-effective, comprehensive safety solution which can also support their IIoT needs. This is where a safety communications master (or bridge) comes in – it can connect a device level safety protocol to a control level safety protocol, allowing low cost sensor connection and data gathering at the device level, and transmission of this data to the higher-level communications and control system.

An example of this architecture is Safety Over IO-Link on PROFISafe/PROFINET. Devices such as safety light curtains, E-stops and safety switches are connected to a “Safety Hub” which has implemented the Safety Over IO-Link protocol. This hub communicates via a “black channel” over a PROFINET/IO-Link Master to a PROFISafe PLC. The safety device connections are very simple and inexpensive (off the shelf cables & standard M12 connectors), and the more expensive (and more capable) Ethernet (PROFINET/PROFISafe) connections are only made where they are needed: at the masters, PLCs and other control level devices. And an added benefit is that standard and safety sensors can both connect through the PROFINET/IO-Link Master, simplifying the device level architecture.

Safety

Combining device level and control level protocols helps users optimize their safety communications solutions, balancing cost, data and speed requirements, and allows IIoT data to be gathered and distributed upwards to control and MES systems.