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Fault Detection and Diagnostics for Rooftop Air Conditioners

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As a result of these considerations, a new fault detection classifier, which does not<br />

require a faulty operation covariance matrix, was developed <strong>and</strong> is described in the next<br />

section.<br />

1.1.1.2 Normalized Distance <strong>Fault</strong> <strong>Detection</strong> Classifier<br />

The attached ASHRAE paper (Li & Braun, 2003) <strong>and</strong> Deliverables 2.1.3 & 2.1.4<br />

(2002) present details of a normalized distance fault detection classifier that can be used<br />

<strong>for</strong> both individual <strong>and</strong> multiple-simultaneous faults simply. The classifier evaluates the<br />

following inequality.<br />

ω1:<br />

Normal<br />

T −1<br />

≤ 2 −1<br />

( Y − M<br />

normal<br />

) Σnormal<br />

( Y − M<br />

normal)<br />

( χ ) ),<br />

><br />

−1<br />

normal<br />

T<br />

where ( Y − M ) Σ ( Y − M )<br />

normal<br />

normal<br />

ω2:<br />

<strong>Fault</strong>y<br />

{(1<br />

−α<br />

m}<br />

(1-2)<br />

2 −1<br />

is the normalized distance, ( χ ) {(1<br />

−α),m}<br />

2 −1<br />

is the threshold of normalized distance <strong>for</strong> normal operation, ( χ ) {, } is the inverse of<br />

the chi-square cumulative distribution function, α is the false alarm rate, <strong>and</strong> m is the<br />

degree of freedom or dimension which is equal to the number of chosen state variables.<br />

Due to modeling error M<br />

normal<br />

is not exactly zero, so equation (1-2) takes modeling error<br />

into account to statistically evaluate whether Y is zero or not.<br />

The above fault detection scheme can be illustrated using Figure 1-3. The residual<br />

distribution of normal operation can be characterized in terms of the covariance matrix<br />

Σ<br />

normal<br />

<strong>and</strong> mean vector M<br />

normal<br />

<strong>and</strong> depicted in the residual space plane as in Figure 1-3.<br />

In the residual space plane, any operating states (points) outside the normal operating<br />

region are classified as faulty while those inside the normal operation region are<br />

classified as normal. The normal operating ellipse is the fault detection boundary.<br />

Practically, normal operation in<strong>for</strong>mation, such as the mean <strong>and</strong> covariance<br />

matrix, is more accessible <strong>and</strong> more reliable, compared to faulty operation data. In<br />

addition, this scheme is intuitive in that the opposite of normal operation is abnormal<br />

operation. If the current operation point is not inside the normal operation region at a<br />

certain confidence according to reliable prior in<strong>for</strong>mation, it should be classified as faulty<br />

17

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