Instrumentation: Data Acquisition: Sampling Rate

When a dynamic process such as vibration has to be studied it is important to perform measurements correctly. The data acquisition process converts an analog signal obtained from a sensor into a discrete function. This is done by sampling the signal at equal intervals. The speed at which the sampling takes place is called sampling rate. Why do it? Because the resulting data is very convenient to transmit, process and store.

The faster the sampling rate, the closer the digital function is to the original signal. So, what sampling rate is really needed? It depends on the highest frequency of interest. Technically, the sampling rate has to be at least twice the highest frequency of interest. The ratio used in the industry is 2.56, which is called Nyquist factor. This means that if the highest frequency we would like to study is 1000 Hz, the sampling rate has to be at least 2560 samples per second.

Now, how much data do we have to collect to have enough to calculate a spectrum? This depends on the spectrum resolution we are trying to achieve. At 800 lines per spectrum we will need 800*2.56=2048 samples. Here, again we multiply by the same Nyquist factor. Time needed to collect the samples we need is found by dividing the number of samples by the sampling rate: 2048/2560=0.8 seconds. It is easy to see that at lower frequencies and higher spectrum resolution it might take quite a while to collect the data. That is why it is sometimes takes so long to acquire a data set.

Instrumentation - Data Acquisition

Sensor signals have to be converted to data (digitized) before they can be processed and stored. This process is called data acquisition. For vibration data the best solution is to acquire a dynamic signal, the function of acceleration over time. This function can later be processed with several algorithms to yield valuable machine condition and diagnostic data. The data acquisition process assigns a number to every level of acceleration at equal time intervals. It takes place in a data acquisition device that contains a micro processor, an ADC (analog to digital converter), necessary filters, and other components. In addition, the data acquisition device has to have means for sending the data out for further processing and storage.

There are many decisions that have to be made about the data acquisition. Here are some of the questions that are typically asked about data acquisition:

How much data has to be collected?

What is the required sampling rate?

What is the proper ADC resolution?

How the signal has to be filtered?

From the practical standpoint these selections must be made behind the scene in software and not by the user. However, many data acquisition systems assume technically savvy users who know how to use these parameters. A more user friendly system should ask a different set of questions:

What is required frequency resolution?

What is required analysis bandwidth?

What is required dynamic resolution?

Answers to these questions help set up proper data acquisition parameters and they borderline with our next topic - Data Processing and Storage.

Low Power Sensors

One important point was not covered by my previous post. As almost any sensor needs power to operate, it is becoming more important how much power each sensor uses. It could be surprising since we are talking of very low amounts of power in any case. Why would we care about this? Because power wiring of machine monitoring systems adds costs in installation and in some locations simply cannot be done.

As wireless data communications are becoming a standard, the last remaining tether is the power connection. In the field of remote machine monitoring we don't deal with a mobile application because heavy equipment does not normally travel around the plant. Even with that, there is a need for fully autonomous monitoring solutions that will not need an external power hookup. These systems will have to harvest energy from the environment or the machines they monitor.

Suddenly, the power consumption by the sensors has become important. Here again we can find that MEMS sensors require significantly lower power to operate in comparison with traditional analog sensors. The MEMS technology will be evolving to produce sensors with more features and lower power requirements in the future, but even today their benefits are obvious.

Instrumentation -- Sensors

Since we are talking specifically about machine condition monitoring, our primary interest will be in vibration sensors. Over many years the standard vibration sensor has been an accelerometer. The sensing technology in a standard accelerometer is pretty much the same today as it was thirty years ago.

In spite of its wide use the standard accelerometer is not an ideal vibration sensor. It requires a charge amplifier that is usually built into the sensor body and an analog signal conditioner, which adds to the cost. Low frequency applications are challenging because the sensor is not sensitive to static acceleration while the dynamic acceleration is very low at low frequencies. These sensors require regular recalibration in condition monitoring applications. However the main issue with traditional accelerometers is cost. An accurate, temperature stable accelerometer with a wide frequency range can cost a few hundred dollars.

The good news is that soon this is going to change. New technology is already here and is waiting to be widely adopted. I am talking about MEMS accelerometers. The MEMS technology has been widely used for sensors and MEMS accelerometers are commonly used in electronic devices as motion sensors. Industrial applications could not benefit from the MEMS technology until last year when Analog Devices has released the ADXL001 accelerometer. Designed specifically for industrial applications, this accelerometer features a wide frequency range, high temperature stability, excellent low frequency characteristics, and does not need recalibration.

Sounds like a perfect vibration sensor? Well, not so fast. There is a gap between the sensor's microchip form factor and the industry's requirement for robustness. This sensor needs an appropriate packaging, in which the sensor will be sealed from the environment and have means to be mounted to a machine. InCheck Technologies is working on a version that a user will be able to permanently mount into a small hole in a bearing housing. The package will also include a temperature sensor microchip, forming a dual sensor.

Speaking about temperature sensors, MEMS sensors can compete with traditional thermocouples and RTD's in the -40°C to +125°C temperature range. Unlike RTD's MEMS temperature sensors produce linear output and unlike thermocouples they do not require external electronics.

Did I mention the main advantage of the MEMS sensors? Yes, they are pretty inexpensive. The ADXL001 is still a little pricey, but it already beats the traditional technology hands down. And since the MEMS output is compatible with standard ADC input, no addition analog electronic components are necessary. With machine monitoring applications the low cost will be the key to wide adoption. The future apparently is with the MEMS technology.

Machine Health Monitoring

Assuming now that we are all on board for predictive maintenance, what do we do next? Our goal is to prevent failures, so, obviously, we need to monitor our machines. This could be done just by inspection (listening to noise, touch for temperature and vibration, visual inspection) or by collecting data. Since the inspection by perception is biased and hard to document, I personally prefer data. Specifically, if we are talking about rotating machines, we need vibration data. With proper processing the vibration data can help diagnose almost any mechanical problem, especially when the data is tracked over time. Other parameters can be monitored as necessary. Temperature, pressure, electrical parameters, etc. - all can yield valuable machine health data.

We will talk about manual "walk around" data collection on a separate occasion. For now we assume a permanent installation. Whether we collect data manually or install permanent sensors, the monitoring system has to have a few layers between Machines and Users that look like this:

Machines --> Instrumentation --> Communication --> Processing and Storage --> Presentation --> Users

Instrumentation layer consists of sensors and data acquisition hardware. To minimize cost of installation the data acquisition hardware should be installed close to the machines. Long sensor wires are not only expensive and hard to install, they sometimes cause deterioration of the analog signal and loss of accuracy. The instrumentation layer outputs data in digital format.

Communication layer transmits the data to a server where it is processed and stored. Today it would be a shame if we ignored standard network infrastructure that makes this transmission easy. Costs are drastically reduced by using standard off the shelf networking components. Standard Ethernet (wired or wireless) and the Internet are the best choice for the communication layer.

Processing and Storage layer is typically a dedicated server computer that runs a few key pieces of software - communication server, digital signal processor, and database. It also contains an alert generator that informs users if something goes wrong.

Presentation layer is a web portal where user can login to monitor the data and control the system. The presentation layer sends to user's browser a program that shows the data in graphic format, gives interactive access to stored data and to all machines regardless of their location. The web portal can be accessed from anywhere with a secure login.

Clearly, the above system architecture is somewhat simplified, but for the purposes of this discussion it is fine. The point we are trying to make here is that today's technology provides tools to make a very inexpensive system that can monitor machine health condition with high accuracy.

Next time we will talk about sensors.

Welcome to InCheck Technologies blog!

Welcome!

In this blog we will be discussing various aspects of remote machine monitoring technology for predictive maintenance. This topic is very important in the industry. Remote monitoring helps machine users to organize machine maintenance in a proactive way, saving money in the process.

Anybody who uses industrial machines knows that they have to be maintained. What people sometimes don't realize is how much this maintenance costs over the life of a machine. So, let's take a look.

Each machine provides a specific service. When you are buying a pump, your goal is to obtain pumping service from it. The cost of pumping service however is not limited to the cost of the pump but has to include installation, energy cost, cost of maintenance, and, at the end of our pump's life, the cost of disposal. Surprisingly, the cost of maintenance is second only to energy costs and can be up to 35% of the total, leaving the initial cost (the cost of the pump and installation) behind at about 10 % of the total, see the diagram below.

Typical life cycle costs of an industrial pump (source: Hydraulic Institute)

Unfortunately, this is just the cost of regular maintenance activities. It does not include the cost of downtime or additional costs that are usually incurred if a machine fails suddenly. This is a real problem. Due to unexpected failures this cost can be dramatic. In many cases the service has to be restored as soon as possible, leaving little choice but paying extra for parts, labor, overtimes, and lost production capability. And we are not even talking about the cost of poor maintenance.

The answer to the challenge lies in a properly organized predictive maintenance program. The goal of such a program is to monitor health condition of each machine, track developing faults, and repair the machines before they fail. This is easier to say than to do. There are two parts in this endeavor - organizational and technological. We will be talking mostly of the technology part, occasionally touching on how to make it work from the organizational standpoint.