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TECHFIND - SENSORS
Intelligent Outlier Detection Algorithm
Researchers at a USA goverment research institution and public university have developed a technique called Intelligent Outlier Detection Algorithm (IODA). If there are faulty readings or other problems associated with failing sensor, IODA is capable of alerting operators and isolating the bad data that is being transmitted. The technology will have broad applicability in several industries. Future applications include: cars and other transportation systems, power plants, satellites and space exploration, and data from radars and other observing instruments. In theory, the operator can somewhat maintain the system even while it is failing to potentially avoid a disaster. IODA's functions include perform quality control and assessment on time series data, especially in when the incoming data are too numerous for a human to easily review, or the consequences of a sensor failure would be significant. Unlike other algorithms, IODA focuses on comparing incoming data to common patterns of failure. This approach can be applied broadly because it is independent of a specific sensor or measurement. Many other algorithms are designed for a particular type of time series and can fail catastrophically when applied to different types of data. IODA treats the data as an image rather than a function of time. Using a special arrangement of the data in delay space, bad data is clustered in its own group separate from the accurate data.
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Researchers at a USA goverment research institution and public university have developed a technique called Intelligent Outlier Detection Algorithm (IODA). If there are faulty readings or other problems associated with failing sensor, IODA is capable of alerting operators and isolating the bad data that is being transmitted. The technology will have broad applicability in several industries. Future applications include: cars and other transportation systems, power plants, satellites and space exploration, and data from radars and other observing instruments. In theory, the operator can somewhat maintain the system even while it is failing to potentially avoid a disaster. IODA's functions include perform quality control and assessment on time series data, especially in when the incoming data are too numerous for a human to easily review, or the consequences of a sensor failure would be significant. Unlike other algorithms, IODA focuses on comparing incoming data to common patterns of failure. This approach can be applied broadly because it is independent of a specific sensor or measurement. Many other algorithms are designed for a particular type of time series and can fail catastrophically when applied to different types of data. IODA treats the data as an image rather than a function of time. Using a special arrangement of the data in delay space, bad data is clustered in its own group separate from the accurate data.
