Forum for Science, Industry and Business

Sponsored by:     3M 
Search our Site:

 

Innovative technique can spot errors in key technological systems

27.01.2010
Operators of satellites, weather instruments may receive early alerts

An innovative computational technique that draws on statistics, imaging and other disciplines has the capability to detect errors in sensitive technological systems ranging from satellites to weather instruments.

The patented technique, known as the Intelligent Outlier Detection Algorithm, or IODA, is described this month in the Journal of Atmospheric and Oceanic Technology.

IODA offers the potential to alert operators to faulty readings or other problems associated with failing sensors. If sensors malfunction and begin transmitting bad data, computers programmed with the algorithm could identify the problem and isolate that bad data.

IODA was developed by researchers at the National Center for Atmospheric Research (NCAR) and the University of Colorado at Boulder (CU).

The National Science Foundation (NSF), NCAR's sponsor, funded the research. "This technology will have broad applicability in many new areas," says Steve Nelson, NSF program director for NCAR.

The developers of the algorithm say its principles can eventually be used in a vast range of technological settings, including cars and other transportation systems, power plants, satellites and space exploration, and data from radars and other observing instruments.

"This could, at least in theory, enable operators to keep a system performing even while it's failing," says Andrew Weekley, a software engineer at NCAR who led the algorithm development effort. "When a system starts to fail, it's absolutely critical to be able to control it as long as possible. That can make the difference between disaster or not."

IODA is designed to perform quality control on time series data--that is, data collected over time, such as wind speeds over the course of a month.

The algorithm, an expert system that draws on statistics, graph theory, image processing and decision trees, can be applied in cases where the correct assessment of data is critical, the incoming data are too numerous for a human to easily review, or the consequences of a sensor failure would be significant.

At present the algorithm consists of several thousand lines of a technical computing language known as MATLAB. The researchers may expand and translate it into a computer programming language such as C so it can be used for commercial purposes.

Ensuring the quality of incoming time series data is a priority for virtually any organization involved in complex operations. If sensors begin relaying inaccurate information, it can be highly challenging for personnel or automated systems to separate good data from bad, especially in cases involving enormous amounts of information.

Typically, to identify bad data, complex operations may rely on multiple sensors, as well as algorithms that characterize specific relationships among the data being collected, and identify failures when the data unexpectedly change.

A drawback in most of these algorithms, however, is they are designed for a particular type of time series and can fail catastrophically when applied to different types of data, especially in situations where there are numerous and sometimes subtle errors.

IODA, however, compares incoming data to common patterns of failure--an approach that can be applied broadly because it is independent of a specific sensor or measurement.

Weekley and co-authors took a new approach to the problem when they began developing IODA 10 years ago. Whereas existing methods treat the data as a function of time, Weekley conceived of an algorithm that treats the data as an image.

This approach mimics the way a person might look at a plot of data points to spot an inconsistency.

For example, if a person looked at a line drawn between points on a graph that represented morning temperatures rising from 50 to 70 degrees, and then spotted a place where that smooth line was broken, dipping precipitously because of numerous data points down at 10 degrees, the person would immediately suspect there was a bad sensor reading.

In cases where there are thousands or even millions of data points about temperature or other variables, pinpointing the bad ones can be more difficult.

But Weekley thought that a computer could be programmed to recognize common patterns of failure through image processing techniques.

Then, like a person eyeing data, the computer could identify problems with data points such as jumps and intermittency; view patterns in the data; and determine not only whether a particular datum is bad but also characterize how it is inaccurate.

"Our thought was to organize a sequence of data as an image and apply image processing techniques to identify a failure unambiguously," Weekley says. "We thought that, by using image processing, we could teach the system to detect inconsistencies, somewhat like a person would."

The research team came up with ways of arranging data points in a time series into clusters, both in a domain that represents the data points over time and in another domain known as delay space.

Delay space, which offers another way to detect differences in the data, is a technique that pairs a data point in the time series with the previous value.

Using the clusters from both the time domain and delay space, bad data are separated into their own cluster, clearly distinct from the cluster of accurate data. At the same time, IODA can calculate quality scores indicating if each individual data point is good or bad.

"I would say the approach we report in the paper is a radical departure from the usual techniques found in the time series literature," says Kent Goodrich, a CU mathematician and a co-author of the paper.

"The image processing and other techniques are not new, but the use of these images and techniques together in a time series application is new. IODA is able to characterize good and bad points very well in some commonly encountered situations."

When the research team tested IODA, they found it accurately isolated incorrect data in several cases.

For example, they applied the algorithm to wind readings from anemometers in Alaska that contained faulty errors due to a loose nut, which left the anemometers unable to consistently measure gusts in high-wind situations. The algorithm identified the bad readings, separating them into a series of clusters away from the good data.

"This technique has very broad implications," Weekley says. "Virtually all control systems rely on time series data at some level, and the ability to identify suspect data along with the possible failure is very useful in creating systems that are more robust.

"We think it is a powerful methodology that could be applied to almost all sequences of measurements that vary over time."

Cheryl Dybas | EurekAlert!
Further information:
http://www.nsf.gov

More articles from Information Technology:

nachricht Study suggests buried Internet infrastructure at risk as sea levels rise
18.07.2018 | University of Wisconsin-Madison

nachricht Microscopic trampoline may help create networks of quantum computers
17.07.2018 | University of Colorado at Boulder

All articles from Information Technology >>>

The most recent press releases about innovation >>>

Die letzten 5 Focus-News des innovations-reports im Überblick:

Im Focus: First evidence on the source of extragalactic particles

For the first time ever, scientists have determined the cosmic origin of highest-energy neutrinos. A research group led by IceCube scientist Elisa Resconi, spokesperson of the Collaborative Research Center SFB1258 at the Technical University of Munich (TUM), provides an important piece of evidence that the particles detected by the IceCube neutrino telescope at the South Pole originate from a galaxy four billion light-years away from Earth.

To rule out other origins with certainty, the team led by neutrino physicist Elisa Resconi from the Technical University of Munich and multi-wavelength...

Im Focus: Magnetic vortices: Two independent magnetic skyrmion phases discovered in a single material

For the first time a team of researchers have discovered two different phases of magnetic skyrmions in a single material. Physicists of the Technical Universities of Munich and Dresden and the University of Cologne can now better study and understand the properties of these magnetic structures, which are important for both basic research and applications.

Whirlpools are an everyday experience in a bath tub: When the water is drained a circular vortex is formed. Typically, such whirls are rather stable. Similar...

Im Focus: Breaking the bond: To take part or not?

Physicists working with Roland Wester at the University of Innsbruck have investigated if and how chemical reactions can be influenced by targeted vibrational excitation of the reactants. They were able to demonstrate that excitation with a laser beam does not affect the efficiency of a chemical exchange reaction and that the excited molecular group acts only as a spectator in the reaction.

A frequently used reaction in organic chemistry is nucleophilic substitution. It plays, for example, an important role in in the synthesis of new chemical...

Im Focus: New 2D Spectroscopy Methods

Optical spectroscopy allows investigating the energy structure and dynamic properties of complex quantum systems. Researchers from the University of Würzburg present two new approaches of coherent two-dimensional spectroscopy.

"Put an excitation into the system and observe how it evolves." According to physicist Professor Tobias Brixner, this is the credo of optical spectroscopy....

Im Focus: Chemical reactions in the light of ultrashort X-ray pulses from free-electron lasers

Ultra-short, high-intensity X-ray flashes open the door to the foundations of chemical reactions. Free-electron lasers generate these kinds of pulses, but there is a catch: the pulses vary in duration and energy. An international research team has now presented a solution: Using a ring of 16 detectors and a circularly polarized laser beam, they can determine both factors with attosecond accuracy.

Free-electron lasers (FELs) generate extremely short and intense X-ray flashes. Researchers can use these flashes to resolve structures with diameters on the...

All Focus news of the innovation-report >>>

Anzeige

Anzeige

VideoLinks
Industry & Economy
Event News

Leading experts in Diabetes, Metabolism and Biomedical Engineering discuss Precision Medicine

13.07.2018 | Event News

Conference on Laser Polishing – LaP: Fine Tuning for Surfaces

12.07.2018 | Event News

11th European Wood-based Panel Symposium 2018: Meeting point for the wood-based materials industry

03.07.2018 | Event News

 
Latest News

Machine-learning predicted a superhard and high-energy-density tungsten nitride

18.07.2018 | Materials Sciences

NYSCF researchers develop novel bioengineering technique for personalized bone grafts

18.07.2018 | Life Sciences

Why might reading make myopic?

18.07.2018 | Health and Medicine

VideoLinks
Science & Research
Overview of more VideoLinks >>>