Defense Tech Briefs - Sensor Validation Using Nonlinear Minor-Component Analysis

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Sensor Validation Using Nonlinear Minor-Component Analysis

Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio

Feb 01 2008

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Figure 2 depicts the FDI process according to the present concept. Under normal operating conditions with properly functioning sensors, the minor components are usually close to zero. The amounts by which the minor components differ from zero are summarized by means of a square weighted residual (SWR), which is calculated from a combination of training data and the residuals of the minor components. It has been proven that in comparison with a prior FDI measure calculated from non-weighted residuals, this SWR is more sensitive to faults and more robust to noise. This SWR in normal operation has a chisquare distribution that can easily be used to determine the threshold SWR value for a given confidence level. A fault is deemed to be detected when this SWR exceeds the threshold.

Figure 2. Readings From m Sensors are processed by m+1 NLMCA structures to determine which (if any) sensor is faulty.

Once a fault has been thus detected, a fault-isolation estimator is activated. This estimator contains a bank of NLMCA structures, each of which monitors only one sensor and uses training data from all the other sensors. The sensor monitored by the NLMCA structure that produces the smallest SWR is most likely the one that is faulty and is so labeled by fault-isolation logic.

Once a fault has been thus isolated, a reverse scan method is used to estimate the reading that the faulty sensor would generate if it were not faulty. The faulty-sensor reading is replaced by a substitute reading and the SWR is calculated for the substitute reading. The faulty-sensor reading is then replaced with another substitute reading and a new SWR calculated. This procedure is repeated a number of times to obtain a series of substitute-reading/SWR combinations spanning a range of values of the physical quantity desired to be measured by the faulty sensor. The substitute reading associated with the smallest SWR is taken to be the estimate of the reading that the sensor would generate if it were not faulty.

This work was done by Kenneth Semega of the Air Force Research Laboratory; Roger Xu, Guangfan Zhang, Leonard Haynes, and Chiman Kwan of Intelligent Automation, Inc.; and Xiaodong Zhang of GM R & D and Planning. AFRL-0035

This Brief includes a Technical Support Package (TSP).

Sensor Validation Using Nonlinear Minor-Component Analysis (reference AFRL-0035) is currently available for download from the TSP library.

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