Is your control system exceeding expectations by performing in an environment for which it is not rated, operating beyond its rated temperature, powering loads beyond the documented capacity, or is it destined for failure? So which is it, or is it more than one? In most cases where an issue is present, the answer is yes to more than one, including the last one: the equipment is functioning beyond rated capacity and destined for failure, yet the owner is unaware.

Over the years I have seen too many instances of control equipment being used beyond its documented limitations and expiring prematurely. Of course, when the equipment does fail it will likely be at the most inconvenient and costly time possible. Using equipment beyond its documented limitations either knowingly or in most cases out of ignorance, will result in premature equipment failure which translates into downtime and ultimately increased cost of ownership. Most all equipment and certainly all UL certified equipment has published temperature, temperature derating, voltage, current, and environment (NEMA) ratings applicable to the equipment. A UL certified control panel is a good start, but it’s not a guarantee and it’s not enough. Adhering to good design standards and practices which ensures the limitations of the equipment are not exceeded, followed up with a good internal quality control system is the recipe for developing a control system with a lower cost of ownership which can live up to the runtime hours and/or cycles published by the manufacturer.

Do you have a new project on the board, or a control system which tends to be a trouble maker and requires constant maintenance? If you plan to seek outside help, make sure you start down the right path and inquire about the company’s engineering standards and practices. Here are my top five suggestions to make sure your next control system design is going to meet your needs for years to come:

1. Ensure your supplier has good processes and standards around component selection taking into account electrical and environmental requirements.

2. Ensure your supplier has standardized procedures for preparing design deliverables such as control system cabinetry, instrumentation, etc. This might include instrument data sheets, heat calculations, power (load) calculations, and standardized drawing packages that are easy to read and facilitate future troubleshooting by operations.

3. If your company has standards, be sure your supplier is flexible enough to adopt them.

4. Ensure your supplier has robust quality control procedures to catch design errors before equipment is purchased and assembled, or worse yet, delivered to your facility.

5. Ensure your supplier is capable of meeting your facility’s area classification requirements. Look for suppliers who can supply UL 508A listed panels for normal situations or UL 698A listed panels for classified areas.

This post was written by Andy Crossman. Andy is a Control System Specialist at MAVERICK Technologies, a leading system integrator providing industrial automation, strategic manufacturing, and enterprise integration services in the manufacturing and process industries. MAVERICK delivers expertise and consulting in a wide variety of areas including industrial automation controls, distributed control systems, manufacturing execution systems, operational strategy, and business process optimization. The company provides a full range of automation and controls services – ranging from industrial cyber security to HMI systems design and remote facility management. Additionally MAVERICK offers industrial and technical staffing services, placing on-site automation, instrumentation and controls engineers.

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These days everyone has a continuous improvement program of some kind. Six Sigma is used quite extensively as is Lean Manufacturing. Some companies stick to one approach while others tailor their own programs using the best ideas from a wide variety of different sources.

Regardless of whichever particular flavor of continuous improvement program you use, it seems there’s always more to do. There are always more problems to tackle.

Once you solved one problem, another problem seems to pop up to take its place. But, after all, it’s a continuous improvement program so there’s always going to be more to do.

But, there are some things that can help and some tools that work really well that are geared specifically for continuous improvement programs. These tools can make a significant impact in a program and can help people working in the programs do a lot more and solve a lot more problems.

Data collection

One category of tools is data collection. The idea here is that to analyze a problem, you need lots of hard data about the problem. Guesses just won’t do. You have to know what’s actually going on. There are a lot of different ways that you can implement data collection, but the key is to get all the data you need and store it in a way that people can use.

DCS and PLC systems are great sources of data and are usually part of any data collection system. Networking is required to get the data from one place to another. And, some type of storage solution such as a data historian or general purpose database is also necessary.

Dashboards and reports

Once you have the data collected, you really need to get the data to the right people so that they can do something with it. There are lots of ways to do this and lots of ways that this can work, but the objective is just to get the data out to the people who need it.

Some people hate the word dashboards and some people love the word, but the analogy is so straightforward that it needs no explanation. There are lots of dashboard and reporting solutions out there, but the key is that they tie back to the data historian and the database and allow all the right people access to the data that they need, when they need it, and in the way that they need it.

Analytics

The idea of analytics is pretty simple. The first steps are always getting the data and then just having access to the data. Analytics takes it all a big step further by providing tools to analyze the data, slice and dice the data, look at large volumes of data, and otherwise mine the data to get the real information and the real understanding out of it all that you need.

Analytics can be a lot more than just looking at data or even developing complex reports. Analytics is all about taking the data apart and putting it back together to see trends and relationships that you never knew about before and gaining a much deeper understanding of what’s actually going on in the real world.

Metrics

The idea of metrics takes a slightly different approach to things. The idea here is to define the key data that indicates the true performance of the manufacturing operation or process. Based on this idea, many people refer to metrics as key performance indicators. The idea is not merely to look at the data or even just analyze the data, but to identify and monitor the data elements that tell you how well the operation or process is actually running.

The important point here is that you have to get beyond the data to the specific measures of the operation or process. And, the measures need to indicate how well the operation or process is actually running, which is a lot more complicated that just how fast the machine is running or how many units are being produced per hour.

OEE

OEE, or overall equipment effectiveness, is one specific metric that has become a staple in a lot of continuous improvement programs around the world. OEE attempts to measure not merely how fast a line or piece of equipment is running but its true manufacturing performance.

OEE does this by combining three metrics which are typically calculated using a lot of underlying data. Availability is calculated based on runtime and downtime. Performance is calculated based on actual versus theoretical rates.  Quality is calculated based on actual first-pass quality. Together, availability, performance, and quality make up OEE and provide a very good standard approach to measuring the true performance of a manufacturing line or process.

Statistics

A specialized version of general analytics is statistical analysis. Statistical analysis in manufacturing usually takes the form of SPC (statistical process control) or SQC (statistical quality control). The short definition of SPC/SQC is the application of statistical methods to control manufacturing process or control manufacturing quality.

SPC/SQC is very valuable in manufacturing because the various statistical analyses can help you anticipate problems and avoid them before they really become problems. The statistical methods allow you to analyze trends in real-time and take corrective actions to prevent situations from becoming problems.

Conclusion

We’ve gone through a lot of ideas pretty quickly. But, you should be able to see that there are a lot of tools out there to help you with your continuous improvement program. These tools work really well and are geared specifically for continuous improvement programs. And, they can make a significant impact in a continuous improvement program. They can help the people working in the programs do a lot more and solve more problems. When you get a chance, take a close look at these tools and see if you don’t think that they can help your continuous improvement program.

This post was written by John Clemons, PE. John is the director of manufacturing IT at MAVERICK Technologies, a leading system integrator providing industrial automation, operational support, and control systems engineering services in the manufacturing and process industries. MAVERICK delivers expertise and consulting in a wide variety of areas including industrial automation controls, distributed control systems,manufacturing execution systems, operational strategy, and business process optimization. The company provides a full range of automation and controls services – ranging from PID controller tuning and HMI programming to serving as a main automation contractor. Additionally MAVERICK offers industrial and technical staffing services, placing on-site automation, instrumentation and controls engineers.

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Too many companies have lost their capacity to train inexperienced engineers. How can we hope to replace the practical knowledge lost to retirement?

A lot has been written lately about the shortage of candidates for positions in the process control field and how we’re going to grow the next generation of process control engineers and technicians. As with a lot of other careers, companies have grown accustomed to being able to pick and choose between candidates in recent years. That is coming to an end as more and more of my contemporaries finally decide to retire and as more and more of the installed base of control systems become terminally obsolete. Corporations have also gotten used to doing more with fewer people. The problem is that those they kept have their hands full just keeping things running. There’s no time to teach new people how to do the work.

We’d like to believe that if we just hire from the right engineering disciplines that we won’t have to train, but the reality is that the curricula in our universities do not teach much of what’s needed in the practical application of process control to a single discipline. Chemical engineering grads have been exposed to process design, so they have the tools to do some of the work, but if they’ve been exposed to process control, it is generally a theoretical approach. If the ChEs have been exposed to the electrical engineering curriculum, it amounts to just that, exposure. Electrical engineering grads are to some degree even more disadvantaged because if they’ve taken a controls course, it has focused on servo control and again the theoretical approach of Laplace transforms and Bode plots. The EE grads often have the added disadvantage of very little exposure to industrial processes. They may or may not get exposed to high voltage devices, thermodynamics, and fluid mechanics, though they may take survey courses of these topics. Mechanical engineers are in a similar boat as EEs since they may or may not get any process engineering along with their courses in fluids and thermodynamics and will have only taken an EE survey course and a freshman chemistry course.

Being a mechanical engineer, I had to learn how to design control circuits properly including motor control circuits. That included understanding issues around proper grounding techniques, which became even more problematic with the early DDC and DCSs because of their sensitivity to noise. I also had to learn proper methodology for installing instrument impulse lines, something no conventional curriculum teaches, nor do they teach how to select measuring devices. Which curriculum teaches you why you should always start large fans against a closed damper? Which teaches you why the pressure and flow measurements in the boiler’s main steam line should always come off the side of the pipe? Which teaches the advantage of a thermocouple over an RTD that has nothing to do with accuracy or range? (Hint: which one can be “fixed” with a hammer at 2:00 am?) Which teaches the design of failsafe circuits? These are the kinds of things that I mean when I speak of the practical application of process control. Such things can only be taught effectively on the job and, in some cases, through direct experience of what happens when you do it wrong.

So, what are your arcane bits practical process controls that you’ve learned over the years? What do you think is the best way to impart those gems of knowledge to the next generation? How can you convince your company that they have to train new grads on more than the company culture and HR policies?

This post was written by Bruce Brandt, PE. Bruce is the DeltaV technology leader at MAVERICK Technologies, a leading system integrator providing industrial automation, operational support, and control systems engineering services in the manufacturing and process industries. MAVERICK delivers expertise and consulting in a wide variety of areas including industrial automation controls, distributed control systems,manufacturing execution systems, operational strategy, and business process optimization. The company provides a full range of automation and controls services – ranging from PID controller tuning and HMI programming to serving as a main automation contractor. Additionally MAVERICK offers industrial and technical staffing services, placing on-site automation, instrumentation and controls engineers.

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A call came in the other day from a customer asking about replacing some legacy operator workstations and a primary domain controller server. He wanted to know if it would be possible to use Microsoft Windows 8 Professional or Server 2012 as the operating systems with the new workstations and primary domain controller, so he can get the most out of the longevity of the new software.

After further research, I could find no reason not to leverage Server 2012 on the new primary domain controller. I checked with the primary control vendor supporting this particular customer, and they saw no reason not to use Server 2012 on the primary domain controller. I went a step further and checked with colleagues on their experiences with the differences between Server 2008 R2 and Server 2012. Since there were no issues identified, I recommended the customer move forward with purchasing and installing this new platform as the primary domain controller for their plant control system. The customer ordered the new server hardware and their IT department purchased their first volume license of Server 2012 standard.

The experience of installing Server 2012 was very similar, if not the same as installing Windows 8 Professional. At the end of the installation, I was surprised to see the start button had disappeared as it had with Windows 8 Professional. With Server 2012, all of the applications are the desktop type, all neatly organized on the start screen based on which roles you assigned to the server (see graphic). On previous versions of Microsoft Server these were inadvertently buried in other submenus and took some digging to find.

This seems to be much more intuitive to use then the start screen that Windows 8 Professional has. I would also like to note that promoting the server to a domain controller was much more automated than previous Server versions, and it added the supporting roles necessary for the domain controller to function properly. Any additional help that I needed was easily found at Microsoft’s support site, or by simply doing an internet search. The other new feature that I found was the new Server Manager’s Dashboard. This makes it very easy to monitor any status and / or issues with any of the roles the server, or servers within a group are providing (see graphic).

During the deployment, the new domain controller computers throughout the plant were joined to the domain with no problems. The plant control system computers contained the following operating systems: Windows XP Professional, Windows 7 Professional, and Server 2008 R2.

This experience should highlight the fact that even though many of our prominent control vendors do not support the latest operating systems that Microsoft releases, this does not mean that supporting systems cannot use the latest Windows operating system available in supporting roles. This not only provides added value for the customer, but also allows us to become proficient on the latest software available. You too can be classified as an early adopter, even if you are working with legacy control software.

This post was written by John Boyd. John is a technology leader at MAVERICK Technologies, a leading system integrator providing industrial automation, operational support, and control systems engineering services in the manufacturing and process industries. MAVERICK delivers expertise and consulting in a wide variety of areas including industrial automation controls, distributed control systems, manufacturing execution systems, operational strategy, and business process optimization. The company provides a full range of automation and controls services – ranging from PID controller tuning and HMI programming to serving as a main automation contractor. Additionally MAVERICK offers industrial and technical staffing services, placing on-site automation, instrumentation and controls engineers.

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You’ve seen the equations, but have you thought about how those elements work together? Part 2: Adding integral and derivative to the mix.

Last week we started with proportional. Now let’s look at the next part of the equation, the integral component:

The most striking (and scariest) part of this equation is the big integral sign in the middle. If you’ve had high school calculus, you think to yourself, “I’ve got this. Integrals don’t scare me. I just need to find the area under the curve from time zero to time t of the error function.”

But, this is the real world. What is time zero? How do I integrate an error function? The good news is that the real definition is much simpler than calculus. What the PID function does is take a portion of the error and adds it to a running total. This running total, sometimes called reset, is added to the output. Since reset increases or decreases a little at a time, it adjusts the output of the valve incrementally each scan.

For a PI controller, the two factors that we have covered so far are K_p and K_i, but if you look at the faceplate for most industrial systems, there is only one K (gain) that has no units, and a τ_i (integral time constant) designated as seconds or minutes per repeat. So, a little translation is required. Most industrial controllers don’t use the independent form of the equation shown above. Instead, they use the dependent form of the equation:

The K is typically the same as the proportional gain, K_p.  The factor τ_i determines how much of the error is going to be applied to the accumulated reset on each scan. So in the big mathy equation, K_i can essentially be replaced by the faceplate parameters:  K⁄τ_i.

What’s important to understand from this is that the gain that affects the proportional action of a controller also affects the integral action. But, the integral time constant τ_i only affects the integral action.

In pseudo code this would look like:

Error := Setpoint – ProcessValue;

Reset := Reset + K/tau_i * Error;

Output := K * Error + Reset;

The unit’s minutes per repeat for the integral time constant τ_i  comes from the fact that if the error stays constant, that is how long would it take for the integral accumulator to repeat the proportional change in output.

Note: Another way of specifying the integral tuning parameter is in seconds, and then it is the reciprocal of seconds per repeat. If the integral time constant is in seconds, the bigger the number, then the slower the response. If the integral is in seconds per repeat, the opposite is true.

Derivative

Now let’s look at derivative:

Again, this is another mathy looking equation with a simple explanation. The mathy definition first, the output will be changed by the derivative (or rate of change) of the error function. What this means is that the output will be affected by the change in error from one scan to another. Adding this to our pseudocode gives us:

Error := Setpoint – ProcessValue;

Reset := Reset + K/tau_i * Error;

Output := K * Error + Reset + (PreError * K/tau_d);

PreError := Error;  //Save the error for the next scan

What is intended is for the output to change as soon as the process variable begins to move either toward or away from the setpoint. What results can be a very quick response to a change in error from one scan to another.

The intention of derivative action is to respond to changes as they begin to occur. For example, if a temperature is starting to rise, the valve will begin to open as soon as it sees the change instead of waiting for it to cross a setpoint. This can result in a very rapid response to a small change. This rapid response can become unstable if there is noise in the process variable or on a setpoint change. So, the derivative action is often filtered separately and is sometimes calculated on PV only to ignore setpoint changes.

Summary

So now we have reviewed the three components of the PID algorithm. One way they have been described is in terms of the flow of time. P depends on the present error, I on the accumulation of past errors, and D on the prediction of future errors based on current rate of change.

This post was written by Scott Hayes. Scott is a senior engineer at MAVERICK Technologies, a leading system integrator providing industrial automation, operational support, and control systems engineering services in the manufacturing and process industries. MAVERICK delivers expertise and consulting in a wide variety of areas including industrial automation controls, distributed control systems, manufacturing execution systems, operational strategy, and business process optimization. The company provides a full range of automation and controls services – ranging from PID controller tuning and HMI programming to serving as a main automation contractor. Additionally MAVERICK offersindustrial and technical staffing services, placing on-site automation, instrumentation and controls engineers.

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