Information Technology Reference
In-Depth Information
2.2 Developed Algorithm
The developed FDD procedure based on KDE consists of two stages: training and
FDD monitoring. In the
first, a KDE model is computed for each motor condition,
in order to have one KDE model in the case of healthy motor and one for each
faulty case. The training steps are summarized below:
T1. Stator current signals for each motor condition are acquired;
T2. Data are normalized;
T3. PCA transform (
4
) is applied to stator current signals, which are projected
into the two-dimensional principal component space;
T4. The matrices P and
are stored;
T5. KDE is performed on the lower-dimensional principal components space (
4
)
using a grid of n
grid
points and a bandwidth h for the Gaussian kernel function (
7
);
T6. PDFs are estimated by KDE (
7
) and stored.
In diagnosis step, the models previously obtained are compared with the new
data and a fault index is calculated. The diagnosis steps are summarized below:
l
D1. Stator current signals are acquired;
D2. Data are normalized;
D3. The matrices P and
, previously computed (T4), are applied to signals;
D4. KDE is performed on the lower-dimensional principal component space (
4
)
using the same points grid in
grid
and bandwidth h used in the training step (T5);
D5. Symmetric K
l
L divergence (
11
) is computed between the estimated PDF by
KDE (
7
) using the acquired current signals, and those stored in the training step
(one for each condition) (T6);
D6. Diagnosis is evaluated using Eq. (
12
).
-
ed using Eq. (
12
) where f
X
is the PDF, estimated by KDE, in
the PCs space of the oncoming current measurements and fi
i
is the ith PDF related to
each motor condition. K
Faults are identi
L divergence is used as an input for fault decision
algorithm allowing to take decision automatically on the operating state and con-
dition of the machine and detecting any abnormal operating condition.
The next Section introduces the FDD experimental results of induction motors in
order to show the proposed method performances.
-
3 Electric Motor FDD by MCSA: Results
In order to verify the effectiveness of the proposed methodology several simulations
are carried out using one benchmark and some experimentations using real asyn-
chronous motors. The benchmark uses a Time Stepping Coupled Finite Element-
State Space modelling (FEM) approach to generate current signals for induction
motors as described in Bangura et al. (
2003
). The simulation dataset consists of
Search WWH ::
Custom Search