White Paper

How to Cut Energy Costs Using a Datalogger

Multi-Value Devices Record, Analyze and Graph Data for Energy Savings
CHESTERLAND, OH—March 22, 2016

If you’re a facilities technician or engineer, you can use a data logger (aka data acquisition system) to identify energy savings areas. These devices can measure and record many different values including current, voltage etc. In this latest White Paper, CAS DataLoggers outlines the basics of how datalogger users can monitor energy usage as part of an energy audit. Read more in our Energy Efficiency White Paper.

Identifying and Reducing Ground Loop Feedback

A Brief Introduction from CAS DataLoggers

CAS_data_loggersWhile grounding can prevent and resolve many power issues, it can also create serious issues of its own. One of the most common problems is known as ground loop feedback–often resulting when different electrical circuits are powering a system and its peripherals. This can also be a seasonal issue as hot dry temperatures can create signal noise for equipment end users, so the Application Specialists at CAS DataLoggers have put together this brief introduction to help reduce or eliminate interference issues. Read more on our White Papers page.

Cleanroom Monitoring and Alarming Using a DAQ and Control System

Delphin TopMessage Data Logger Records All Values and Provides Secure Storage
CHESTERLAND OH—January 21, 2013

DAQ and Control DeviceCAS DataLoggers recently provided a Delphin TopMessage data logger for a Cleanroom Monitoring and Alarming application in a major hospital which needed a DAQ and control system to record extensive cleanroom measurement data including pressure, temperature, air humidity and particle counts. Users also needed the system to control the cleanroom alarm horns and ‘traffic’ lights indicating current door lock status, running on user-friendly analysis and control software which could setup a secure measurement database storing all logged values. Read more on our Data Acquisition Applications Notes page.

Capturing and Simulating Rotational and Jitter Angle-based Measurements

Capturing and simulating the high-speed rotations involved in automated machines and test rigs often plays a major role in industrial automotive applications. Common requirements are accurate speed measurement over a large range of operation, detection of speed changes, capturing jitter, jitter simulation, and angle-based data acquisition. The Applications Specialists at CAS DataLoggers have put together this article to present some practical approaches to accomplish these tasks using an ADwin data acquisition system. Read more on our White Papers page.

Automated Pendulum Demo Shows ADwin’s Microsecond Response Time

ADwin-Gold Data Acquisition System

Automated pendulum demo using the Adwin-Gold data acquisition systemCAS DataLoggers Sales Manager Pete Martin impressed attendees with a unique ADwin demonstration at a successful exhibition at the 2012 Control System Integrators Association (CSIA) Executive Conference in Scottsdale, Arizona. This demo utilizes an interesting program highlighting how to achieve microsecond response times with a measurement and control system while operating in the Microsoft Windows environment, employing an inverted pendulum, which is highly unstable unlike the balance of a regular pendulum. Read more on our White Papers page.

R&D Testing for Calcium Treatment Water Filters

Delphin TopMessage Modular Data Acquisition System
CHESTERLAND OH—February 15, 2012

CAS DataLoggers recently supplied the data acquisition solution for a customer specializing in providing water purification filter systems and cartridges for industrial and household applications. These filter cartridges were widely used to improve water quality and taste, especially for coffee vending machines but including even large industrial plants. The filter material acted to reduce the water’s calcium content, balancing its pH level and binding other metallic ions. The filter granule material itself was developed in the customer’s R&D lab for each specific filter and purification treatment application individually, where they needed to be tested to gauge their effectiveness on water consumption. Read the entire article on our white papers page.

Cold Atom Quantum Physics Experiment

Utilizing Modular Real Time Data Acquisition and Control
CHESTERLAND OH—December 7, 2011

CAS DataLoggers recently provided the data acquisition and control solution for an associate professor of physics at a major university running an experiment producing ultracold quantum gases containing either bosons or fermions. The experiment took place under ultra-high vacuum inside a pyrex glass cell. Researchers collected the atoms in a magneto-optical trap (MOT) which consisted of 6 laser beams for each atomic species and a magnetic trap produced by two external coils with counter propagating current. While the MOT was on, bright purple LEDs caused light-assisted desorption of atoms from the walls of the cell so that they could be captured in the MOT. After a brief moment of optical molasses (with the lasers on but no magnetic field), the atoms were gently transported vertically about 5 cm to within 200 μm of a magnetic chip trap by changing the shape of the magnetic field with more coils. Current passing through a wire on the chip, along with external coils, was used to tightly trap the atoms. Radio frequency signals passed through another wire on the chip, which changed the shape of the trap and allowed the hottest atoms to escape, thus lowering the average energy of the atoms. This evaporative cooling could produce quantum degenerate gases, either Bose-Einstein condensates or degenerate fermions. To undertake this incredibly demanding application, the physics department needed a modular data acquisition system capable of highly-accurate measurements in real-time and which offered intuitive software with powerful graphing and display capabilities.

The research team installed an ADwin-Pro Modular Real Time Data Acquisition and Control System to provide them with precise timing and deterministic control of the experiment’s processes. The ADwin system’s analog channels were used to control current in the coils or wires on the chip and the frequencies and amplitudes of the lasers. The analog output was programmed to step, ramp, or follow an S-shaped curve as desired. Easy programming of the ADwin Pro’s analog channels provided a simple way to control many devices in the lab. The digital channels were used to open shutters, trigger frequency sources, flip polarity of current sources, and trigger cameras. Several digital channels were also used to serially program frequency sources. Signals from the digital channels went to a digital buffer consisting of optical isolators to prevent ground loops from forming in the system.

For some experiments, the researchers chose to transfer these cold atoms from the magnetic chip trap to an optical dipole trap – crossed laser beams which caught the atoms as the chip trap was turned off. This purely optical trap gave experimenters the freedom to adjust the external magnetic field as they pleased, giving them the ability to address Feshbach resonances and to tune the interactions between the atoms. To image the atoms at the end of an experiment, the trap was turned off, the cloud expanded, and a pulse of laser light cast a shadow of the atoms onto a CCD camera. From the size, shape and density of the shadow, the team could determine the cloud’s physical properties.

Every aspect of this experiment required the precise control provided by the ADwin-Pro system. Voltage controlled acousto-optical modulators altered the frequency and amplitude of the laser light, while seven external coils as well as wires on the chip created the magnetic field. The way these magnetic fields switched on and off were important to keep the atoms cold. The team used voltage-controlled power supplies to control the current through these coils and wires. Other equipment including radio and microwave frequency sources, shutters and cameras required well-timed triggers.

By means of the configurable ADTools software, researchers were able to display the experiment’s real-time data graphically or numerically, to visualize process sequencings, or to set input values via potentiometers, sliders or push buttons. Additionally, ADtools constantly provided researchers with the current status of their ADwin system resources. The ADwin software environment could be used under Windows (2000/XP/Vista/Win7) and Linux, or as a stand-alone data acquisition system. Also, ADwin offered drivers for many of the popular programming environments including Visual Basic, Visual C/C++, LabVIEW/LabWindows, TestPoint and others.

The university’s physics department benefitted in several ways following installation of the ADwin-Pro Modular Real Time Data Acquisition and Control System. Using up to 480 analog/digital I/O inputs and high-performance DSP processor, the ADwin-Pro system performed real-time measurements at extremely high accuracy and also performed all the necessary control functions for the experiment. The system’s modular design offered researchers the flexibility to configure the cards as desired and to add more hardware as needed. Additionally, ADwin’s intuitive ADbasic and ADTools software added visualization, graphing and display features and ensured that the experiment continued uninterrupted with continual status of system resources.

Check out the Adwin-Pro product page here.

For further information on the ADwin-Pro Modular Real Time Data Acquisition and Control System, other data acquisition devices from ADwin, or to find the ideal solution for your application-specific needs, contact a CAS Data Logger Applications Specialist at (800) 956-4437 or visit the website at www.DataLoggerInc.com.

Contact Information:
CAS DataLoggers, Inc.
12628 Chillicothe Road
Chesterland, Ohio 44026
(440) 729-2570
(800) 956-4437

How to Select a Portable Data Acquisition System

Choosing the Ideal Solution for Any Application

CHESTERLAND OH—September 13, 2011

Portable data acquisition systems are used worldwide to capture data and conduct routine testing of vital infrastructures such as mass transit systems, power grids, and bridges, as well as heavy industrial processes and applications. These powerful yet compact data acquisition devices play an important role in the testing and monitoring of many critical systems, and selecting the most suitable device for a given application requires careful consideration. The ideal portable data acquisition system for most users is a compact, lightweight unit powered by either a self-contained battery or a single DC power source, requiring no other connection to function other than the sensors being monitored. Operating in remote areas, a user interface and means of communication with the device become vital features. Signal conditioning such as gain and filtering as well as high-capacity non-volatile data storage are other important considerations.

When selecting a suitable portable data acquisition system for their specific applications, users face a bewildering choice of available manufacturers, models and specifications. Each system has its own configurations that make some units more suitable than others for certain applications. Additionally, purchasers must first consider several demanding requirements not necessary for traditional laboratory devices. Any existing and potentially damaging environmental extremes including temperature, excessive humidity, liquids, dust, shock and vibration should be carefully considered. Other relevant questions to ask include whether the data acquisition equipment can support the particular mix of sensors that will be used, as well as determining if the device has adequate memory and storage to support the specific project.

Before deciding on a specific manufacturer or solution, users need to form a clear idea of the results they require of any system they’ll be using. Sampling rates, as just one factor, are available in a wide variety from as low as once per day to higher than a million per second. Anticipating the future project’s needs today will save precious time and money on installation. Data acquisition system designs range from the simple to the complex, with an attendant variety in performance, features, and cost. Fortunately, in the face of all these options, fundamental guidelines are available for portable data acquisition equipment users to consider before making their purchase.

Of course, the main function of data acquisition systems are their basic function to accurately record data, and here the wide spectrum of degrees of accuracy attainable by units available on the market can make this a more involved decision than expected. A reliable constant, though, is that the accuracy of field measurements is heavily based upon the sensors being used. For most sensors that have been calibrated in the laboratory and installed in the field, accuracies in the range of 0.01 % to 1 % of full scale are typical, with many other sensors having less accuracy. The particular system’s input types must also match project requirements–for example, an application requiring extensive analog measurements would benefit from the portable Delphin Expert Key 200 series of data acquisition system, which features a full 28 analog inputs.

Likewise, the sampling speed of DAQ systems must also be taken into account when determining accuracy. The device must acquire signals quickly enough to avoid any loss of the data. The necessary data acquisition speed is calculated by using Nyquist’s Sampling Theorem, which states that a signal must be sampled at twice the frequency of the spectral signal components which are of interest in order to accurately reconstruct the waveform. The Delphin Expert Key 200 series mentioned above has a 100kHz maximum sample rate.

Also vital to many users’ considerations are the environmental conditions which the device will be subject to. Portable data acquisition equipment is naturally susceptible to damaging environments, such that ruggedized packaging of the DAQ unit itself, as well as its electrical components, is an essential manufactured precaution to ensure both device and data integrity. For example, portable DAQ equipment used in heavy industrial applications often needs to withstand a broad temperature range. In the absence of ruggedized models, a portable enclosure may give adequate protection for the system. In these harsh environments, and to retain their portability, DAQ systems need to be as compact and lightweight as possible. Further, beyond withstanding these environmental extremes, portable data acquisition units need to be able to survive in high shock and vibration environments such as the trunk of a car or onboard an airplane, or just as a result of the occasional accident such as being dropped by its owner. Other sealing and packaging precautions such as watertight housing should be under review for aquatic applications such as flow rate measurement and wastewater monitoring which partially or completely immerse the device.

For remote applications where access to a standard 120V AC power outlet isn’t available, electric power to the system can be provided either through an internal battery pack, or the user can connect an external wire to a DC power supply. Additionally, in order to conserve power and avoid unnecessary processor loadup, users with minimal processing requirements can select a lower performance CPU and rely on a capable storage system.

When analyzing recorded data and exporting to other formats, many users rate software primarily by how user-friendly it is, which often depends on graphical interface style, menu navigation and help tips. Continuing with the earlier example, the Delphin Expert Key 200 series features ProfiSignal software for data storage, display and analysis.

Choosing a portable data acquisition system featuring internal signal conditioning capabilities can greatly improve system quality and performance. Different types of signal conditioning include amplification, attenuation, and filtering. As always, the specific application involved and the types of sensors used to make the measurements will decide the type of signal conditioning required—to measure temperature, a device will probably need to use thermocouples or thermistors. Thermocouples produce a voltage that varies with the temperature, but connecting a thermocouple to a data acquisition system creates a cold junction point at the terminals that acts as a thermocouple itself. Signal conditioning is required to compensate for this, or else the recorded temperature which is taken from the total voltage will be altered by the additional voltage of the cold junction point. Signal conditioning can also be used for signal amplification to mitigate any noise distortion, and is also useful when a DAQ device is connected to other transducers such as strain gauges, accelerometers, etc.

When designed to monitor remote unattended systems, suitable DAQ devices have communications capabilities utilizing telephone connections or wireless systems to download data to remote PCs. They also require ample built-in storage and user interfaces to enable setup and control. Advanced solutions use built-in testing capabilities allowing users to sit back as the system acquires the data.

Taking into account all these specifications when choosing the right portable DAQ device is certainly an involved process, but one made easier by always keeping the needs of the present and any future applications foremost in mind. Check out the Delphin Expert Key Data Acquisition System product page here.

For further information on the Delphin Expert Key 100-200 series of portable data acquisition devices, other Delphin data acquisition devices, or to find the ideal solution for your application-specific needs, contact a CAS Data Logger Applications Analyst at (800) 956-4437 or visit the website at www.DataLoggerInc.com.

Contact Information:
CAS DataLoggers, Inc.
12628 Chillicothe Road
Chesterland, Ohio 44026
(440) 729-2570
(800) 956-4437

What Does “Real-Time” Mean?

Data Acquisition Devices Operating in Real-Time

CHESTERLAND OH—August 10, 2011

Many vendors claim to have “real-time” systems, but without defining what they mean by the phrase and what time frame they have in mind, it’s impossible to compare and contrast real-time systems. For example, many people have seen automobiles that claim to have “real-time four-wheel drive.” Does this mean that other vehicles have non-real-time operation? What exactly does “real-time” mean? This confusion can be clarified by defining the phrase and describing what time scales are appropriate for different applications. Specific examples of system operation based on different response times further help users understand what is possible and impossible for their real-time applications.

In general, there are three factors that define exactly what real-time operation encompasses. In addition, it’s essential to identify a time frame for real-time operation. Any real-time system must have stable and repeatable program execution–the program must produce the same result for any given set of inputs and it must do so within the same time frame. Therefore a system that produces the same output, but exhibits variability in the time it takes from input to output, cannot be considered real-time. Another key to any real-time system is the time it takes from an event or change of an input occurring to the instant the output updates. It’s critical that a real-time system provides a guaranteed time for which the output will respond to a change in the input. Finally, the execution of the program must be predictable. It’s not acceptable for the output of the program to be dependent on factors that are unknown, or at minimum, uncontrollable. Any factors that determine the output state for a given input state must be known and controllable. However, these three factors alone are not adequate to describe or compare real-time systems since they do not establish a time frame for operation.

Every process or application has its own real-time context–the time needed for the system to respond to a change or request. This time frame can vary dramatically, depending on the process. However, there are a few categories of typical time scales for real-time systems. At the low end of the scale, there are systems where a response time measured in minutes is acceptable. A restaurant could easily be considered a real-time operation – they take orders, cook the food and (hopefully!) deliver it in a guaranteed and predictable manner. In an average sit-down restaurant, the food might take 15 minutes to a half-hour. On the other hand, at McDonald’s, a wait of three minutes might be at the high end of customer expectations.

Moving on to faster systems, there are many examples where a response within seconds is necessary. NASCAR race fans marvel at the real-time response of pit crews when a car comes in for fuel or a tire change. In this case, getting in and out in tens of seconds is often expected. Additionally, any processes where temperature control is needed can manage response times measured in seconds. In conditions with a large thermal mass, temperature changes occur very slowly and a controller needs to respond to changes within a few seconds to keep the temperature at a desired set point.

Even faster systems require response times that are measured in milliseconds. These include devices like PLCs that are controlling production lines, test stands that apply a stimulus and measure the response of a system or device being tested, or systems that require feedback control loops with less than 1 kHz of bandwidth. The latter could be a simple PI or PID control loop for temperature, pressure, flow, voltage, current or position.

On the farthest end of the real-time spectrum, some of the fastest systems demand response times measured in microseconds. Examples include high-speed test stands, fast digital controllers with bandwidths up to 500 kHz, and positioning systems that employ electron beams or lasers–each need extremely fast controllers. These capabilities are often beyond that of a general purpose system and require dedicated hardware designed expressly for the target application.

Considering the preceding requirements for real-time systems and the response time requirements, it is apparent that, for many applications, Windows is not a suitable environment. Essentially, Windows is not 100% stable, as the performance of any application running under Windows is highly dependent on any other activities running on the same CPU. In a best-case scenario, users may simply see delays in the execution of one process under Windows due to another process executing at a higher priority. In the worst case, a badly-behaving process can crash the computer, causing all other processes to stop. This outcome is not acceptable if these processes are in charge of critical control or positioning functions.

Windows is not inherently designed to be a real-time platform. It uses a priority-based time-slicing approach to allocate the resources of the CPU among the multiple processes running at any one time. Typically, kernel processes that interface directly with hardware (including disk drives, the keyboard, mouse or video display) run at the highest priority. And, because of the time slicing approach, users aren’t guaranteed of when a process will be serviced. Therefore, whenever users have a control application running at a lower priority and somebody moves the mouse around, the real-time application will be delayed.

Computer devices that rely on real-time, such as sound cards and disk writers, are able to operate using local processors that buffer and manage data to enable real-time operation. Any device that operates under Windows and needs real-time has its own local intelligence. Modern Windows-based systems such as disk drive controllers, sound cards, and fieldbus cards all have a local processor to achieve the tight timing required for normal operation. While it’s possible to use Windows with a real-time kernel, such as the real-time extensions to Windows, it’s much better to use a device with local intelligence for real-time that communicates with Windows for display and data storage. See Table 1 for an example of system response on real-time.

Table 1 – A system with a 25 µs response time

Control Frequency Process Cycle Time Response Time
(% of cycle time)
Process A 1 kHz 1000 µs 2.5%
Process B 10 kHz 100 µs 25%
Process C 40 kHz 25 µs 100%
Process D 100 kHz 10 µs 250%

In this case, processes C and D cannot run since they consume 100% or more of the CPU resources.

Table 2 compares the system performance when the response time is even faster at 1 µs.

Table 2—A system with a 1 µs response time

Control Frequency Process Cycle Time Response Time
(% of cycle time)
Process A 1 kHz 1000 µs 0.03%
Process B 10 kHz 100 µs 0.3%
Process C 40 kHz 25 µs 1.2%
Process D 100 kHz 10 µs 3%

For older 486 DOS-based systems, average response times range from 10 to 40 µs. These systems typically have much lower overhead than newer Windows-based systems. Windows systems with real-time kernel extensions are somewhat slower, with response times of 25 to 200 µs. In addition to these, National Instruments markets several different real-time products. In a detailed publication, Benchmarking LabVIEW Real-Time FieldPoint Systems, they indicate that the typical input to output time is 5 to 50 ms, depending on the number of channels and system configuration. Meanwhile, Linux-based real-time systems can achieve response times on the order of 5 to 20 µs; even faster are RISC/DSP systems which can process requests in 1 to 10 µs; and still faster are the ADwin-Pro DSP-based systems which have demonstrated response times as fast as 300 ns.

The hardware and software architecture of the ADwin-Pro system has been optimized for real-time operation. For example, online calculation for each measured value for applications like PID loops or digital filters are processed immediately after measurements are made, resulting in very fast updating of output values. The architecture allows overlapping A/D or D/A conversions in parallel with calculations for the highest throughput. For calculations, maximum CPU performance is achieved through the use of a 32-bit floating-point DSP.

In summary, ‘real-time’ control means different things to different people and companies. Without having a defined time frame, it’s impossible to contrast different systems that claim to have real-time response. For the best performance in a Windows environment, however, it’s essential to have a local processor dedicated to maintaining the real-time control functions. At the high end of the real-time spectrum, the ADwin-Pro system has one of the fastest response times available for applications that demand precise and repeatable control. Check out the Adwin-Pro’s product page here.

For further information on the Delphin ADwin-Pro DSP system, other data acquisition and control solutions, and data logging and remote monitoring applications, or to find the ideal solution for your application-specific needs, contact a CAS Data Logger Applications Analyst at (800) 956-4437 or visit the website at www.DataLoggerInc.com.

Contact Information:
CAS DataLoggers, Inc.
12628 Chillicothe Road
Chesterland, Ohio 44026
(440) 729-2570
(800) 956-4437