Fault Detection and Diagnostics for Rooftop Air Conditioners

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1. Simplifies fault detection from a high-D problem to n 1-D ones. Equation (1-2)
boils down to following n 1-D equations,
sign _ stat ( zi
) =
2
⎪⎧
( zi
− µ
i,
normal)
− ⎪⎫
2 1
⎨
> ( χ ) {(1
− α),1} 2
⎬
⎪⎩ σ
i,
normal
⎪⎭
(1-8A)
or
=
⎪⎧
zi
− µ
⎨
σ ⎪⎩
i,
i,
normal
normal
>
N
−1
⎪⎫
⎬
⎪⎭
{(1
− α ),0,1}
(1-8B)
where,
i = 1,2,
L,
n .
2. Automatically achieves fault diagnosis without any extra computation immediately
after fault detection is finished, because equations (1-8) have obtained what
equation (1-7) needs. So the fault diagnosis classifier is not required.
sign _ stat(
z ) sign(
zi
) sign _ stat(
zi
)
i
x
i sign
=
=
= sign _ stat ( z
sign(
λ )
sign(
λ )
, i
i
i
3. Overcomes the drawback of the SRB diagnosis method and handles multiplesimultaneous
faults diagnosis.
4. Becomes more generic and system-independent and does not require complicated
rules, which depend on the system.
)
1.1.2.3 Unilateral Decoupling Case
The above methodology based on full decoupling can handle multiplesimultaneous
faults easily, but the criterion of full decoupling is not a necessary condition
and can be lowered. To lower the full decoupling criterion has practical application.
Although a physically decoupled feature vector Z can be found for a fault vector X ,
some features may be too expensive to be used for a non-critical FDD application. For
example, condenser air flow rate, which is independent of any other faults, could be the
decoupled feature for condenser fouling, but its measurement is too expensive. An
alternative way to obtain this kind of feature is to estimate it using a virtual sensor, which
may be corrupted by other faults. In other words, although fault i may not impact other
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