08.11.2014 Views

Probabilistic Performance Analysis of Fault Diagnosis Schemes

Probabilistic Performance Analysis of Fault Diagnosis Schemes

Probabilistic Performance Analysis of Fault Diagnosis Schemes

SHOW MORE
SHOW LESS

You also want an ePaper? Increase the reach of your titles

YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.

where the subscript k has been omitted for the sake <strong>of</strong> clarity.<br />

Example 3.3 (0-1 Loss). Suppose that the loss matrix<br />

[ ]<br />

0 1<br />

L =<br />

1 0<br />

is used for all time. This is typically referred to as “0-1 loss” in the literature [4, 61]. By equation<br />

(3.13), the corresponding Bayesian risk <strong>of</strong> a test V at time k is<br />

R k (Q k ,V ) = P fp,k + P fn,k<br />

= P f,k Q 0,k + (1 − P d,k )Q 1,k<br />

= 1 − c k .<br />

Thus, placing an upper bound on the 0-1 risk R k (Q k ,V ) is equivalent to placing a lower bound<br />

on the probability <strong>of</strong> correctness c k .<br />

3.4 Characterizing the Range <strong>of</strong> Achievable <strong>Performance</strong><br />

In Section 3.3, the performance <strong>of</strong> a test was given in terms <strong>of</strong> the probabilities {P f,k }<br />

and {P d,k }. In this section, we consider the complementary problem <strong>of</strong> determining what<br />

performance values (P f,k ,P d,k ) ∈ [0,1] 2 are achievable by some test. Again, we draw on the<br />

tools <strong>of</strong> statistical hypothesis testing to address this issue. Namely, we use the Neyman–<br />

Pearson Lemma [71] and the receiver operating characteristic (roc) [57] to characterize the<br />

limits <strong>of</strong> achievable performance. To facilitate our discussion, we first introduce the concept<br />

<strong>of</strong> a randomized test.<br />

3.4.1 Randomized Tests<br />

Up to this point, we have focused our attention on tests V = (F,δ), where both the residual<br />

generator F and the decision function δ are deterministic. However, it is possible to design<br />

and implement tests that are nondeterministic. In this section, we introduce nondeterministic<br />

or randomized tests and use them to characterize the set <strong>of</strong> achievable performance<br />

points.<br />

Definition 3.4. A hypothesis test V is said to be a randomized test if, for a given realization<br />

<strong>of</strong> the test statistic (û 0:k , ŷ 0:k ), the decision d k = V (û 0:k , ŷ 0:k ) is a random variable.<br />

Define V to be set <strong>of</strong> all deterministic and randomized hypothesis tests, and define W k<br />

to be the set <strong>of</strong> all performance points (α,β) ∈ [0,1] 2 that are achieved by some test V ∈ V ,<br />

at time k. The following example shows how to derive randomized tests from the class <strong>of</strong><br />

30

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!