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Fig. 1. Architecture of the proposed adaptive classification procedure.

Figure 1 Architecture of the proposed adaptive classification procedure.

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Then, the first term in the right hand side of (13) can be manipu- lated as follows By relying on the above relation (12), the weighted information po- tential of the current feature point can be manipulated as
Then, the first term in the right hand side of (13) can be manipu- lated as follows By relying on the above relation (12), the weighted information po- tential of the current feature point can be manipulated as
Listing 1. Pseudo-code for the proposed adaptive estimation algorithm.
Listing 1. Pseudo-code for the proposed adaptive estimation algorithm.
Fig. 2. The overall architecture of the proposed adaptive fault-detection expert system.
Fig. 2. The overall architecture of the proposed adaptive fault-detection expert system.
Fig. 3. The pneumatic laboratory installation used for the experimental verification.
Fig. 3. The pneumatic laboratory installation used for the experimental verification.
Fig. 6. A screen-shot of the user interface application. Fig. 5. Estimates of the process parameters. Outputs of the Kalman filter.
Fig. 6. A screen-shot of the user interface application. Fig. 5. Estimates of the process parameters. Outputs of the Kalman filter.
Fig. 4. Process input and output signals.
Fig. 4. Process input and output signals.
Fig. 7. Reciprocal value of the weighted information potential of the current feature vector.
Fig. 7. Reciprocal value of the weighted information potential of the current feature vector.
Fig. 9. Index of the currently active focal point. Fig. 8. Reciprocal values of the weighted information potential of focal points.
Fig. 9. Index of the currently active focal point. Fig. 8. Reciprocal values of the weighted information potential of focal points.
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Computer ScienceFault Detection and Isolation
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