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1 Faulty 86.03 no yes yes yes yes yes yes yes
2 Faulty 372.8 yes yes yes yes yes yes yes yes
3 Faulty 336.92 no yes yes yes yes yes yes yes
4 Faulty 78.52 yes yes yes yes yes yes yes yes
5 Faulty 267.37 yes no no no no no no no
6 Faulty 50.92 yes yes yes yes yes yes yes yes
7 Faulty 194.47 n / a yes yes yes yes yes yes yes
8 Faulty 251.02 yes yes yes yes yes yes yes yes
9 Faulty 336.88 yes yes yes yes yes yes yes yes
10 Faulty 232.45 yes yes yes yes yes yes yes yes
11 Faulty 77.43 yes yes yes yes yes yes no no
12 Faulty 49.44 no yes yes yes yes yes yes no
13 Faulty 195.99 yes yes yes yes yes yes yes yes
14 Faulty 36.53 no yes yes yes yes yes yes yes
15 Healthy 877.78 n / a no no no no no no no
16 Healthy 2019.85 n / a no no no no no no no
17 Healthy 1608.58 n / a yes no no no no no no
18 Healthy 1479.9 n / a yes no no no no no no
19 Healthy 1093.09 n / a yes no no no no no no
20 Healthy 218.5 n / a no no no no no no no
21 Healthy 148.74 n / a no no no no no no no
Table 7.1: Authentic test cases.
Using a 90% threshold factor obviously causes false alarms in the training sets, cases 17 -20. The reason that the other healthy states sets are not producing any false alarms is that they have signi.cantly lower .uctuation levels than the training sets. Threshold factors of 100% to 130% provides good results for the data at hand, although a factor of 100% is unadvisable for training data with more representative .uctuation levels. Case 5 is the only non-detection for this range of threshold factors, and requires a factor of 60% to be detected, which obviously will create an unacceptable false alarm rate. Indicator decomposition, noise level estimate and slope is shown in (Fig.
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