How IO-Link Sensors With Condition Monitoring Features Work With PLCs

As manufacturers continually look for ways to maximize productivity and eliminate waste, automation sensors are taking on a new role in the plant. Once, sensors were used only to provide detection or measurement data so the PLC could process it and run the machine. Today, sensors with IO-Link measure environmental conditions like temperature, humidity, ambient pressure, vibration, inclination, operating hours, and signal strength. By setting alarm thresholds, it’s possible to program the PLC to use the resulting condition monitoring data to keep machines running smoothly.

Real-time data for real-time response

A sensor with condition monitoring features allows a PLC to use real-time data with the same speed it uses a sensor’s primary process data. This typically requires setting an alarm threshold at the sensor and a response to those alarms at the PLC.

When a vibration threshold is set up on the sensor and vibration occurs, for example, the PLC can alert the machine operator to quickly check the area, or even stop the machine, to look for a product jam, incorrect part, or whatever may be causing the vibration. By reacting to the alarm immediately, workers can reduce product waste and scrap.

Inclination feedback can provide diagnostics in troubleshooting. Suppose a sensor gets bumped and no longer detects its target, for example. The inclination alarm set in the sensor will indicate after a certain degree of movement that the sensor will no longer detect the part. The inclination readout can also help realign the sensor to the correct position.

Detection of other environmental factors, including humidity and higher-than-normal internal temperatures, can also be set, providing feedback on issues such as the unwanted presence of water or the machine running hotter than normal. Knowing these things in real-time can stop the PLC from running, preventing the breakdown of other critical machine components, such as motors and gearboxes.

These alarm bits can come from the sensors individually or combined together inside the sensor. Simple logic, like OR and AND statements, can be set on the sensor in the case of vibration OR inclination OR temperature alarm OR humidity, output a discrete signal to pin 2 of the sensors. Then pin 2 can be fed back through the same sensor cable as a discrete alarm signal to the PLC. A single bit showing when an alarm occurs can alert the operator to look into the alarm condition before running the machine. Otherwise, a simple ladder rung can be added in the PLC to look at a single discrete alarm bit and put the machine into a safe mode if conditions require it.

In a way, the sensor monitors itself for environmental conditions and alerts the PLC when necessary. The PLC does not need to create extra logic to monitor the different variables.

Other critical data points, such as operating hours, boot cycle counters, and current and voltage consumption, can help establish a preventative and predictive maintenance schedule. These data sets are available internally on the sensors and can be read out to help develop maintenance schedules and cut down on surprise downtimes.

Beyond the immediate benefits of the data, it can be analyzed and trended over time to see the best use cases of each. Just as a PLC shouldn’t be monitoring each alarm condition individually, this data must not be gathered in the PLC, as there is typically only a limited amount of memory, and the job of the PLC is to control the machines.

This is where the IT world of high-level supervision of machines and processes comes into play. Part two of my blog will explore how to integrate this sensor data into the IT level for use alongside the PLC.

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.

Increasing Productivity in the Injection Molding Process

Part of calculating the productivity in an injection molding operation is to figure out the maximum number of items you’d be able to produce if everything worked perfectly. Unfortunately, “everything working perfectly” is not something you often see in manufacturing. How can you get closer to that ideal number? One answer lies in a little sensor which can monitor environmental conditions vital to your operation. With it you can reduce your machine downtime and the amount of scrap you produce.

Condition monitoring sensors seem to be taking the automation world by storm. These sensors take various measurements including temperature, ambient pressure, relative humidity, and vibration. They report the data digitally, which makes it easy to track performance. What used to require several sensors now requires only one.

Monitor humidity in plastic granule drying process

Following the plastic injection molding process from beginning to end we can see the usefulness of this one sensor. Plastic granules need to be dried before they go into the machine. If the moisture level is too high, it can cause splay marks to show up on the final product, which then has to be scrapped. This can be costly and can extend lead times if it is not detected early on. The condition monitoring sensor can track ambient humidity so you can stop that problem in its tracks before it creates waste and increases overhead.

Monitor temperature in the injection molding process

One of the biggest variables to any injection molding process is temperature. Some common temperature-related issues in injection molding include blistering, burn marks, degradation of the polymer used, stringiness, and warping. These are caused by temperature variations that cause the resin to be too hot or too cold. Condition monitoring sensors can detect swings in temperature to prevent products having to be scrapped.

Monitor vibration to detect mechanical wear

It’s clear that condition monitoring sensors can helpfully measure environmental factors, but what about mechanical wear? Vibration sensors can monitor mechanical wear on bearings, linear drives, gearboxes and much more by plotting vibration data. It’s even more effective if they measure vibration on more than one axis so you can see the direction of vibration and not just the overall amount. This way you can be proactive and plan your maintenance in advance instead of being in a constant reactive state, trying to patch problems as they come up. Using vibration data gathered by a condition monitoring sensor, you can avoid the costly consequences of unscheduled downtime.

In conclusion there are many different applications that condition monitoring sensors can be used for in injection molding operations. By tracking a variety of different measurements including vibration, temperature, and humidity, you will be able to improve the efficiency and productivity of your entire operation by using this one compact sensor. It provides a low-cost solution so that you can reduce the scrap that is cutting into your profits. And reduce the amount of downtime that causes so many unnecessary headaches. Put these smart sensors to work for you.

How Hot is Hot? – The Basics of Infrared Temperature Sensors

Detecting hot objects in industrial applications can be quite challenging. There are a number of technologies available for these applications depending on the temperatures involved and the accuracy required. In this blog we are going to focus on infrared temperature sensors.

Every object with a temperature above absolute zero (-273.15°C or -459.8°F) emits infrared light in proportion to its temperature. The amount and type of radiation enables the temperature of the object to be determined.

In an infrared temperature sensor a lens focuses the thermal radiation emitted by the object on to an infrared detector. The rays are restricted in the IR temperature sensor by a diaphragm, to create a precise measuring spot on the object. Any false radiation is blocked at the lens by a spectral filter. The infrared detector converts radiation into an electrical signal. This is also proportional to the temperature of the target object and is used for signal processing in a digital processor. This electrical signal is the basis for all functions of the temperature sensor.

There are a number of factors that need to be taken into account when selecting an infrared temperature sensor.

  • What is the temperature range of the application?
    • The temperature range can vary. Balluff’s BTS infrared sensor, for example, has a range of 250°C to 1,250°C or for those Fahrenheit fans 482°F to 2,282° This temperature range covers a majority of heat treating, steel processing, and other industrial applications.
  • What is the size of the object or target?
    • The target must completely fill the light spot or viewing area of the sensor completely to ensure an accurate reading. The resolution of the optics is a relationship to the distance and the diameter of the spot.

  • Is the target moving?
    • One of the major advantages of an infrared temperature sensor is its ability to detect high temperatures of moving objects with fast response times without contact and from safe distances.
  • What type of output is required?
    • Infrared temperature sensors can have both an analog output of 4-20mA to correspond to the temperature and is robust enough to survive industrial applications and longer run lengths. In addition, some sensors also have a programmable digital output for alarms or go no go signals.
    • Smart infrared temperature sensors also have the ability to communicate on networks such as IO-Link. This network enables full parameterization while providing diagnostics and other valuable process information.

Infrared temperature sensors allow you to monitor temperature ranges without contact and with no feedback effect, detect hot objects, and measure temperatures. A variety of setting options and special processing functions enable use in a wide range of applications. The IO-Link interface allows parameterizing of the sensor remotely, e.g. by the host controller.

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