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6.7. CONCLUSION
113
Table
6.5
contains
the
d.square sum error across ten test signals for each of the sigmoid methods, as well as the linear and the non-parametric method (NP). From this, it appears as if the non-parametric method is the most interesting. Evolutionary optimization and Trust Region using pre-de.ned order have slightly lower square sum error, but has the clear disadvantage of not optimizing function order. This makes the methods less reliable for signals with variable length and complexity. The non-parametric method is thus the favored progression analysis tool, as it produces repeated good results, and has superior computational speed.
Set EO TR RSV IEO TRD Line NP
1 41.853 26.6643 33.4888 54.2973 37.3468 25.5291 23.6302
2 7.696 9.1261 9.2098 55.9464 9.3499 50.1149 10.2
3 7.9776 7.4386 54.6359 33.9158 56.9523 14.8669 13.3758
4 20.9424 20.1561 19.1953 73.9504 22.7163 124.2189 42.2564
5 7.641 14.9461 71.0944 94.8918 68.3208 72 11.7348
6 20.0249 23.8039 84.5186 64.5285 9.9265 67.8417 16.1921
7 27.3242 30.1642 57.9581 2550.4486 50.9997 60.3503 18.0125
8 19.3812 4.7417 4.7386 76.1091 36.4489 62.1104 17.3813
9 2.3191 9.2118 9.2118 18.9621 8.0787 34.2552 11.0039
10 20.6041 15.2271 8.5354 13.0756 5.1164 30.4918 9.2168
μ 17.5763 16.1480 35.2587 303.6126 30.5256 54.1779 17.3004
σ 11.1148 8.2909 28.0450 749.3444 21.7045 29.7517 9.3080
Table 6.5: Progression analysis method comparison chart.
Acronyms: Evolutionary Optimization (EO), Trust Region (TR), Resid-ual Spectrum Validation (RSV), Iterative Evolutionary Optimization (IEO), Trust Region using Band-Limited Di.erentiator Pre-Processing (TRD), Line model (Line), Non-Parametric Progression Analysis (NP).
Chapter 7
Fault Detection
7.1 Introduction
This chapter will focus on fault detection based on trend-based feature ex-traction methods. Anomaly detection methods are developed both for the parametric and the non-parametric features. The objective of these detection methods is to identify abnormal indicator behavior without a priori knowl-edge of speci.c fault signatures. Although a framework for fault recognition is suggested, diagnosis is given lower priority. This because there is not suf-.cient training data to cover all failure modes, thus making training and validation of such a classi.cation system di.cult. Further, it is from an op-erational point of view su.cient to perform a go / no-go decision and a crude fault localization. Should a component be suspected faulty, the aircraft will in any case be subject to a through manual inspection.
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