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.
When looking at flexible manufacturing, what first comes to mind are the challenges of handling product changeovers. It is more and more common for manufacturers to produce multiple products on the same production line, as well as to perform multiple operations in the same space.
Accomplishing this and making these machines more flexible requires changing machine parts to allow for different stages in the production cycle. These interchangeable parts are all throughout a plant: die changes, tooling changes, fixture changes, end-of-arm tooling, and more.
When swapping out these interchangeable parts it is crucial you can identify what tooling is in place and ensure that it is correct.
ID without RFID
When it comes to identifying assets in manufacturing today, typically the first option companies consider is Radio-Frequency Identification (RFID). Understandably so, as this is a great solution, especially when tooling does not need an electrical connection. It also allows additional information beyond just identification to be read and written on the tag on the asset.
It is more and more common in changeover applications for tooling, fixtures, dies, or end-of-arm tooling to require some sort of electrical connection for power, communication, I/O, etc. If this is the case, using RFID may be redundant, depending on the overall application. Let’s consider identifying these changeable parts without incurring additional costs such as RFID or barcode readers.
Hub ID with IO-Link
In changeover applications that use IO-Link, the most common devices used on the physical tooling are IO-Link hubs. IO-Link system architectures are very customizable, allowing great flexibility to different varieties of tooling when changeover is needed. Using a single IO-Link port on an IO-Link master block, a standard prox cable, and hub(s), there is the capability of up to:
30 Digital Inputs/Outputs or
14 Digital Inputs/Outputs and Valve Manifold Control or
8 Digital Inputs/Outputs and 4 Analog Voltage/Current Signals or
8 Analog Input Signals (Voltage/Current, Pt Sensor, and Thermocouple)
When using a setup like this, an IO-Link 1.1 hub (or any IO-Link 1.1 device) can store unique identification data. This is done via the Serial Number Parameter and/or Application Specific Tag Parameter. They act as a 16- or 32-byte memory location for customizable alphanumeric information. This allows for tooling to have any name stored within that memory location. For example, Fixture 44, Die 12, Tool 78, EOAT 123, etc. Once there is a connection, the controller can request the identification data from the tool to ensure it is using the correct tool for the upcoming process.
By using IO-Link, there are a plethora of options for changeover tooling design, regardless of various I/O requirements. Also, you can identify your tooling without adding RFID or any other redundant hardware. Even so, in the growing world of Industry 4.0 and the Industrial Internet of Things, is this enough information to be getting from your tooling?
In addition to the diagnostics and parameter setting benefits of IO-Link, there are now hub options with condition monitoring capabilities. These allow for even more information from your tooling and fixtures like:
Vibration detection
Internal temperature monitoring
Voltage and current monitoring
Operating hours counter
Flexible manufacturing is no doubt a challenge and there are many more things to consider for die, tooling and fixture changes, and end-of-arm tooling outside of just ID. Thankfully, there are many solutions within the IO-Link toolbox.
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.
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.
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.
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.
Vibration is all around us. We can feel it and we can hear it. Some vibrations we find pleasant, such as music that we like to listen to, and some vibrations we find unpleasant such as scratching fingernails across the chalkboard. Humans also can predict when something is about to fail or determine when something needs our attention based on the vibrations we can feel or hear in our surroundings. An example almost anyone can relate to is when you are driving or riding in a car and the tires are out of balance or are damaged. In addition to the audible noise, you can feel the vibration through the steering wheel and the chassis of the car. Frequency and amplitude of the vibration typically increase as you speed up, and often amplify your worry as well. This can push you to find the cause of the vibration and fix it.
This same principle can be used in a manufacturing plant environment, which is what makes monitoring vibration so important. Without it, machines break down and stop, costing you time, and money. We all know that one maintenance guru that has a special gift of being able to determine what is happening with a machine based on its vibration feedback, the one who can place his hand on a machine, or hear the machine speak to him, and determine what is wrong with it.
However, using this institutional knowledge isn’t full-proof and it can introduce additional variables in the mix; sometimes resulting in wasted parts, labor, unplanned machine downtime, loss of production, etc. And as tenured staff retires and is replaced with less experienced staff, it has become even more important to remove the human element from the equation and properly capture the data to determine the root cause of mechanical issues. But how? By equipping machines with a monitoring system, the machine can then continuously monitor itself. And when the variables exceed the preset acceptable thresholds, the machine can act based on predetermined actions set by the OEM manufacturer or the maintenance team.
There are many monitoring systems on the market today that vary in complexity and cost. More complex systems include sensors, cables, data acquisition cards, computers, analysis software, data base, cloud subscription, and paid service contracts to pinpoint exact condition of the equipment or asset that is being monitored. This type of system or service is very costly, and in most cases, it is cost prohibitive to be used on non-critical equipment or assets. However, there are lower cost solutions that may not be able to pinpoint what has failed but can tell you when something wrong with the machine that needs to be examined by the maintenance technician. Such devices can be easily integrated into an existing controls architecture and can provide continuous condition monitoring of the machine or asset. Practice of continuous condition monitoring of machines can save the company valuable time and money by reducing unscheduled machine downtime, eliminating wasted parts and time for unnecessary scheduled maintenance, improving total OEE (Overall Equipment Effectiveness) of the machine, and increasing production. This all leads to increased profits.
Because there are more and more solutions available in the market today, there are few things you need to consider when choosing the right solution for your application:
Overall cost of implementation – hardware, software, and any installation costs?
Is the solution proprietary? Hardware, software, or communications?
Is there an annual service contract(s)? Subscriptions?
Does the machine/asset require periodic or continuous monitoring?
Quality of data? Do you need to know the exact failure point or is knowing that the machine is operating outside of its specified parameters good enough?
Can the system be easily expanded for the future state?
Are there any additional features that can aid in analyzing the condition of the machine such as pressure, temperature, humidity?
Knowing what you need and want ahead of time will help you better choose the correct solution for your application without wasting money and time on unnecessary features and functions.
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.
Most industrial processes do not take place in a climate-controlled laboratory or clean room environment. Real-world industrial activity generates or takes place under harsh conditions that can damage or shorten the life expectancy of equipment, especially electronic sensors.
A cross-section of industrial users was surveyed about operating conditions in their facilities. The responses revealed that plant operators are challenged by a variety of difficult environmental factors, the biggest being heat, dust/dirt/water contamination, vibration, and extreme temperature swings.
Over one-third of the industrial users surveyed reported that premature sensor failure is a problem in their operations. That is a surprisingly high percentage and something that needs to be addressed to restore lost productivity and maintain long-term competitiveness.
Many heavy industries are dependent on automated hydraulic cylinders to move and control large loads precisely. The cylinder position sensors are often subjected to damaging environmental conditions that shorten their life expectancy, leading to premature failure.
In any continuous manufacturing process such as steel production, increased throughput is the path to higher profits through maximum utilization of fixed capital investments. In order to achieve increased throughput, more sophisticated control systems are being deployed. These systems enable ever-higher levels of automation but can present new challenges in terms of managing system reliability. Maintenance of profit margins depends on the line remaining in production with minimal unexpected downtime.
It is essential that control components, such as sensors, be selected in accordance with the rigorous demands of steel industry applications. Standard sensors intended for use in more benign manufacturing environments are often not suitable for the steel industry and may not deliver dependable service life.
When specifying sensors for steel production applications, some environmental conditions to consider include:
Heat
High-temperature M30 proximity sensor.
High temperatures exist in many areas of the steel-making process, such as the coke oven battery, blast furnace, electric arc furnace, oxygen converter, continuous casting line, and hot rolling line. Electronic components are stressed by elevated temperatures and can fail at much higher rates than they would at room temperature. Heat can affect sensors through conduction (direct transfer from the mounting), convection (circulating hot air), or radiation (line-of-sight infrared heating at a distance). The first strategy is to install sensors in ways that minimize exposure to these three thermal mechanisms. The second line of defense is to select sensors with extended temperature ratings. Many standard sensors can operate up to 185° F (85° C) but high temperature versions can operate to 212° F (100° C) or higher. Extreme temperature sensors can operate to 320° F (160° C) or even 356° F (180° C).
Don’t forget to consider the temperature rating of any quick-disconnect cables that will be used with the sensors. Many standard cable materials will melt or break down quickly at higher temperatures. Fiberglass-jacketed cables, for example, are rated to 752° F (400° C).
Shock and Vibration
Hydraulic cylinder position sensor rated at 150 G shock.
Steel making involves large forces and heavy loads that generate substantial amounts of shock under normal and/or abnormal conditions. Vibration is also ever-present from motors, rollers, and moving materials. As with heat, look for sensors with enhanced specifications for shock and vibration. For sensors with fixed mountings, look for shock ratings of at least 30 G. For sensors mounted to equipment that is moving (for example, position sensors on hydraulic cylinders), consider sensors with shock ratings of 100 to 150 G. For vibration, the statement of specifications can vary. For example, it may be stated as a frequency and amplitude, such as 55 Hz @ 1 mm or as acceleration over a frequency range, such as 20 G from 10…2000 Hz.
Don’t forget that the quick-disconnect connector can sometimes be a vulnerability under severe shock. Combat broken connectors with so-called “pigtail” or “inline” connectors that have a flexible cable coming out of the sensor that goes to a quick-disconnect a few inches or feet away.
Mechanical Impact
Proximity sensor bunkered in a protective mounting block.
The best way to protect sensors from mechanical impact is to install them in protective mounting brackets (a.k.a. “bunker blocks”) or to provide heavy-duty covers over them. When direct contact with the sensor cannot be avoided, choose sensors specifically designed to handle impact.
Another strategy is to use remote sensor actuation to detect objects without making physical contact with the sensor itself.
Corrosion and Liquid Ingress
In areas with water spray and steam, such as the scale cracker on a hot strip line, corrosion and liquid ingress can lead to sensor failure. Look for stainless steel construction (aluminum can corrode) and enhanced ingress protection ratings such as IP68 or IP69K.
When All Else Fails…Rapid Replacement
Quick-change prox mounts for proximity sensors.
If and when a sensor failure inevitably occurs, choose products and accessories that can minimize the downtime by speeding up the time required for replacement.
Strategies include quick-change sensor mounts, rapid-replacement sensor modules, and redundant sensor outputs.
In the case of redundant sensor outputs, if the primary output fails, the system can continue to operate from the secondary or even tertiary output.
