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Data Acquisition for Race Cars

DAQ racecar driver

DAQ racecar driver

Data acquisition, for those unfamiliar with the term, is simply the process of collecting data on some physical process, and passing this data to a computer for analysis. Though most commonly used in industrial processes and scientific applications, there are many reasons why using systems like this can be useful for race cars.

First and foremost, implementing data acquisition systems in race cars provides a vast amount of data that can be invaluable in squeezing every last bit of performance from the vehicle. Formula One teams, for instance, employ dozens of people to analyze the torrents of data produced by their cars, in order to find out precisely where performance can be improved.

Before I take you through some of the other reasons to use data acquisition systems in race cars, it is worth noting that these systems are something quite different to telemetry. If you are involved with race cars in any way, it is likely that you are already used to using telemetry, and understand the key concepts of this.

Data acquisition is something different – whilst telemetry is typically used to improve performance during races or practice sessions, data acquisition produces data to be analyzed at a later date. Most commonly, it is used to improve mechanical performance of the race car itself, rather than that of the driver.

So what purposes can data acquisition be put to in a racing context?

Analyzing Vehicle and Driver Performance

Using Data Acquisition systems in race cars produces vast amounts of data that, when looked at in conjunction with telemetry, can be used to improve race performance. Collecting even the minimum level of data on in-race performance, such as RPM and engine temperature, can give valuable insights into where performance gains can be made.

 

Monitoring Reliability and Safety

Using data collected over longer periods can also be extremely useful in finding weak points in the reliability of vehicles. In fact, finding design weaknesses is almost impossible without collecting some form of data on how the vehicle was working during break downs.

In addition, using data acquisition on race cars can reveal potential failures before they happen. It might be the case, for instance, that tyre pressures are continually running near their design maxima, and that therefore tire failures are much more likely. Using data acquisition can reveal this before a catastrophic failure.

ASCERTAINING VEHICLE LIMITS

Using data acquisition techniques on race cars, it is possible to safely push vehicles closer to their limits. Analysis of data may reveal that certain components in the vehicle are capable of greater performance than is currently being wrung out of them, and that there is room to push them further.

In addition, analysis of this data can help drivers, by pointing out where they can push the vehicle further. Especially with unfamiliar vehicles, many drivers err on the side of caution, as they are unsure of the limits of the vehicle they are driving. Data acquisition for race cars can help them understand where these limits actually lie.

To learn more about DAQ products click here.

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PCI DATA ACQUISITION AND SIGNAL PROCESSING CONTROLLERS

PCI DATA ACQUISITION image

PCI DATA ACQUISITION image

Data Acquisition, in the broadest sense, is a process of collecting information on some real-world, physical system, and using this to analyze the system in question. In most cases, the system being measured is an industrial process or scientific experiment, and nowadays the data collected is almost always passed to some form of computer.

In recent years, the technology used to collect, process, and analyze such data is available off-the-shelf. In comparison to earlier systems, which were normally bespoke designs specific to a particular field or process, the use of off-the-shelf components means that they can be replaced easily and cheaply.

There are many factors to consider when designing and implementing a data acquisition system. First and foremost, the types of sensors used to collect data must be carefully though out. Secondly, the signal output by these sensors needs to processed into a form which is acceptable for a digital computer. Lastly, the software running on the computer being used to collect and analyze data must fit the purpose at hand.

For most engineers and scientists today, the standard choice of hardware is a PCI-based system. These systems use a standard connection between signal-collecting hardware and the hardware of the computer used to analyze this data. Though originally designed for the PC, in recent years PCI cards are available that will also work with Mac and other forms of computer system.

Today, I will give a quick introduction to the design and implementation of such systems, broken into the three main factors that must be thought about – the sensors used, the choice of signal processing controller, and the hardware and software required to work with the data collected.

Sensors

When designing and implementing any PCI data acquisition system, the first thing that must be considered is the type of sensors required. Whilst there are now sensors available for almost any kind of physical variable you wish to measure, it is worth noting that any sensor is likely to effect the operation of the system you are measuring, and it is therefore wise to use the minimum number of sensors required for your purposes.

In practice, this means carefully thinking through exactly the data you need to collect from the system you are interested in, and focusing on that type of data. Typically, one reading can act as a proxy for the total operation of the system – for instance, one of the most common variables measured by PCI data acquisition systems is temperature, which in many cases is used to indicate the smooth operation of industrial hardware.

Other examples of common phenomenon that measured by PCI data acquisition systems are light intensity, gas pressure, fluid flow, and force.

Signal Processing

Typically, PCI data acquisition systems use dedicated hardware to pass signals from sensors to the computer systems that will collect and analyze the data. Converting a messy, sometimes noisy, signal from a physical system into a format that can be used and manipulated on a computer can be a tricky business.

One of the first obstacles to be overcome in this regard is signal strength. As outlined above, typically sensors are designed to take the smallest amount of energy possible from the system they are being used to measure. In practice, this also means that the signal they output is of a vey low intensity, and must to amplified to be of any use.

It is therefore critical to use an amplifier that is able to amplify the signal cleanly. A noisy amplifier will ultimately warp and color the data collected, which in some cases can render it useless.

Another thing to think about when designing a PCI data acquisition system is the type of signal that you will use to pass data between the various parts of your system. Most sensors will output a single ended analog signal. Whilst this type of signal is good at capturing the raw state of the system being measured, it is also very susceptible to noise and distortion. A common fix for this problem is to convert the signal coming from the sensors into a differential signal, which is much more stable and easier to work with.

PCI Data Acquisition And Signal Processing Controllers

Once the signal has been amplified and cleaned up, it must be fed into a computerized system for collection and analysis. Nowadays, most data acquisition systems use standard PC hardware, meaning that if components of the system fail, they can be easily replaced with off-the-shelf items.

The most common format for getting an incoming signal into a computer nowadays is the use of PCI cards and other hardware. Though originally designed for PC, PCI cards compatible with many other systems are now available.

These external PCI cards are a solution to a common problem – that with complex data acquisition systems measuring many variables, the number of PCI inputs on a typical motherboard is too few. To work around this problem, an external card is used to combine multiple signals into a single input.

DAQ cards often contain multiple components that are able to perform signal processing before passing the signal to the software. In the most advanced cards, these functions are accessible via a bus by a micro-controller, though some cheaper systems used hard wired logic. For both types of card, proprietary device drivers are often needed.

These signal processing controllers are increasingly able to perform quite complex processing on incoming signals. For complex PCI data acquisition systems, this is invaluable for several reasons. First and foremost, it shifts the burden of signal processing away from the CPU, which in large complex systems can become choked by an overload of data.

The final stage in processing the signal is to pass it to software. Nowadays, a vast variety of different software solutions are available for use with PCI data acquisition system, and the choice of which to use depends on the type of data being collected and how it needs to be processed. Typically, however, such systems are based on commonly understood programming languages such as C++ or MATLAB, providing a large scope for customization.

Which to Choose DAQ Cards or DAQifi Products?

DAQ cards typically output data using a dedicated hard link, and in years past this often meant having a separate PC workstation for every data acquisition process. Not only did this mean extra expense in terms of hardware, it often meant that bringing data from several processes together was a manual, painful business. DAQifi cards send the collected data over a WiFi network – either an existing one, or one generated by the device itself – to custom software.

What this means in practice is that a single PC, tablet, or even smart phone can be used to aggregate all the data being collected, bringing it all together for easy analysis and manipulation. This also means that the computer being used to collect and manipulate data does not need any additional hardware to be used for this purpose.

In addition, DAQifi devices represent better value than many DAQ card solutions. This is because DAQ cards are often made to be used to collect one type of data only, and in many cases this means that a bank of cards must be used in order to collect even quite basic data. The flexibility of DAQifi devices makes them cheaper to implement in many situations.

This is especially true in situations where portability is paramount. The fact that DAQifi devices run on their own power makes them ideal for situations where having a dedicated PC workstation is simply impossible. This is the case in many industrial processes, where the environment is not conducive to the health of computer hardware, and also in situations where the system under study is inherently mobile, such as in automotive engineering.

Lastly, the user interface which comes as standard on DAQifi devices means that using them is incredibly simple in comparison to many DAQ card solutions. Often, even in high-end scientific applications, all that is needed from a data acquisition system is for it to feed data to a centralized device, in a format which is easy to work with, for later analysis.

This is exactly what DAQifi devices achieve, and it is therefore not surprising that they are eclipsing DAQ card solutions in many situations.

Read more about this in our DAQ vs DAQifi post.

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Types of Data Acquisition Systems

types of DAQ systems

types of DAQ systems

Data Acquisition Systems, often abbreviated to DAS or DAQ, are systems designed to measure and track some form of physical system, and convert this data into a form that can be viewed and manipulated on a computer.

The design an implementation of DAS is a complicated field. The first DAS were designed by IBM back in the 1960s, and were huge assemblages of computers and hardware. As the field has developed, more generic systems have become available, and accordingly it is now possible to measure and analyze almost any form of physical system.

DAS are now used in many different fields, from industrial production to scientific experiments, and the type of system used is different depending on each application.

In general, however, types of DAS can be broken into three components – the sensors used to collect data from the physical systems, the circuitry used to pass this data to a computer, and the computer system on which it can be viewed and analyzed.

If you are setting up a DAS, these are also the three factors that should be considered. Time spent thinking about exactly which data you need to collect, and how you want to work with the data once it is collected, can save significant time and money further down the line.

Let’s take a look some of the most common options in all three of these fields.

Sensors

The design of any DAS must start with the physical system which is being measured. With the range of sensors available today, it is possible to measure almost any physical property of the system you are interested in. Careful consideration must be made, therefore, of exactly the type of data you need to collect. It might be nice to be able to track the temperature of your industrial printer, for instance, but you need to think about whether this information will actually be useful for you.

Examples of common phenomenon that are measured by DAS are temperature, light intensity, gas pressure, fluid flow, and force.

For each variable to be measured, there exists a particular type of sensor. Sensors, in this sense, are essentially transducers, transforming physical energy into electrical energy. For instance, a basic pressure sensor will be activated and driven by the pressure it is measuring, and pass this information as an electronic signal to the DAS.

For this reason, it is important to recognize that it is not possible to measure every variable you want to without effecting the system itself. This is because any sensor will effect the system it is designed to measure, and remove energy from it. This is especially important if the system being measured works on small tolerances, because the addition of even a small sensor to these systems can drain too much energy from them for effective operation.

In short, though there is likely a sensor available to measure almost any aspect of your systems, it is not always wise to try and measure every variable. Instead, think carefully about the data you actually need, and use the minimum number of sensors that will achieve this.

Signal Processing

Typically, DAS use dedicated hardware to pass signals from sensors to the computer systems that will collect and analyze the data. Converting a messy, sometimes noisy, signal from a physical system into a format that can be used and manipulated on a computer can be a tricky business.

One of the first obstacles to be overcome in this regard is signal strength. As outlined above, typically sensors are designed to take the smallest amount of energy possible from the system they are being used to measure. In practice, this also means that the signal they output is of a vey low intensity, and must to amplified to be of any use.

It is therefore critical to use an amplifier that is able to amplify the signal cleanly. A noisy amplifier will ultimately warp and color the data collected, which in some cases can render it useless.

Another thing to think about when designing a DAS is the type of signal that you will use to pass data between the various parts of your system. Most sensors will output a single ended analog signal. Whilst this type of signal is good at capturing the raw state of the system being measured, it is also very susceptible to noise and distortion. A common fix for this problem is to convert the signal coming from the sensors into a differential signal, which is much more stable and easier to work with.

Computer Hardware and Software

Once the signal has been amplified and cleaned up, it must be fed into a computerized system for collection and analysis. Nowadays, most DAS use standard PC hardware, meaning that if components of the system fail, they can be easily replaced with off-the-shelf items.

First and foremost, the signal must be converted into a digital format that the computer understands. Typically, this is done using the pre-existing ports on a PC, such as the parallel ports, USB, etc. Another approach is to use cards connected to slots in the motherboard. With this second approach, a common problem is that the number of ports on a PCI card is too few to accept all of the inputs needed. To work around this problem, a breakout box is used to combine multiple signals into a single input.

DAS cards often contain multiple components that are able to perform signal processing before passing the signal to the software. In the most advanced cards, these functions are accessible via a bus by a microcontroller, though some cheaper systems used hard wired logic. For both types of card, proprietary device drivers are often needed.

The next stage in processing the signal is to pass it to software. Nowadays, a vast variety of different software solutions are available for use with DAQ, and the choice of which to use depends on the type of data being collected and how it needs to be processed. Typically, however, such systems are based on commonly understood programming languages such as C++ or MATLAB, providing a large scope for customization.

Despite these changes, the data acquisition market has changed and DAQ Cards find themselves increasingly an obsolete form of DAQ. Newer devices that allow for more robust feature and capabilities while not being limited by fixed cards are now starting to dominate the market.

The Advantages of DAQifi Devices

DAQ cards typically output data using a dedicated hard link, and in years past this often meant having a separate PC workstation for every data acquisition process. Not only did this mean extra expense in terms of hardware, it often meant that bringing data from several processes together was a manual, painful business. DAQifi cards send the collected data over a WiFi network – either an existing one, or one generated by the device itself – to custom software.

What this means in practice is that a single PC, tablet, or even smart phone can be used to aggregate all the data being collected, bringing it all together for easy analysis and manipulation. This also means that the computer being used to collect and manipulate data does not need any additional hardware to be used for this purpose.

In addition, DAQifi devices represent better value than many DAQ card solutions. This is because DAQ cards are often made to be used to collect one type of data only, and in many cases this means that a bank of cards must be used in order to collect even quite basic data. The flexibility of DAQifi devices makes them cheaper to implement in many situations.

This is especially true in situations where portability is paramount. The fact that DAQifi devices run on their own power makes them ideal for situations where having a dedicated PC workstation is simply impossible. This is the case in many industrial processes, where the environment is not conducive to the health of computer hardware, and also in situations where the system under study is inherently mobile, such as in automotive engineering.

Lastly, the user interface which comes as standard on DAQifi devices means that using them is incredibly simple in comparison to many DAQ card solutions. Often, even in high-end scientific applications, all that is needed from a data acquisition system is for it to feed data to a centralized device, in a format which is easy to work with, for later analysis.

This is exactly what DAQifi devices achieve, and it is therefore not surprising that they are eclipsing DAQ card solutions in many situations.

Read more about this in our DAQ vs DAQifi post.