Instrumentation & Measurement Magazine 25-5 - 27

UAV Embedded OCM
Instrument Based
on Heterogeneous
Computing
Fig. 4. Structure of the stacked LSTM model.
second LSTM layer only outputs the hidden state at the last
moment. The dense layer is a full connect layer.
To get the prediction of the condition monitoring data at the
next moment, the perceived data X RNd
able to be predicted Y RNd
Considering that the intelligent
assessment model
based on the stacked LSTM
has high time complexity,
and the data perceived by
airborne sensors are data
stream that has the characteristics
of a small data
sample size, heterogeneous
SoC is used as the edge
computing platform to develop
the UAV embedded
OCM instrument. Generally,
the control cycle of a
typical UAV flight control
system is 20 ms. Therefore,
the condition monitoring
and the target varishould
be reconstructed with a
sliding window. Assuming the length of the sliding window
is L, this means using the past L condition monitoring samples
to predict the target variable at the next moment. After
reconstruction, the input and target pairs of the stacked LSTM
model are obtained as:
  { ,
t


R
t
The output R
tion results of R
X xx xtL
YR  { ,yy ytL
t , , }, R
11
11




xt 
yt 
Y of the stacked LSTM model fLSTM
Y .

YXfR= ()LSTM R
Then, the prediction residual e can be calculated as:
RR - ˆ
e =YY
(8)
is the predict
, , }, RLd

(7)
(9)
With this residual e, the fault detection of UAV can be achieved
by:
Fe
eT
i  

 1, eTi 
0,
i 
,
i e
(10)
where Fi refers to the fault index, 1 represents fault and 0 represents
normal. T is the fault detection threshold. When the
absolute value of the prediction residual is greater than the
threshold T, it is considered that the detected UAV parameter
deviates from its original condition. Then, a fault alarm should
be given.
August 2022
of a UAV needs to complete the fault detection of multiple
flight parameters (target variables) within 20 ms. This means
that multiple intelligent assessment models for fault detection
of different flight parameters need to be run in 20 ms. It
brings challenges to heterogeneous SoC-based OCM instruments
with limited computing resources. To realize the online
operation of multiple fault detection tasks in the embedded
OCM instrument, it is necessary to improve the operation
efficiency of multiple fault detection tasks from the perspectives
of model reduction and customizable computing. The
optimization method in the embedded OCM instrument is introduced
as follows:
◗ Model reduction refers to neural network pruning.
Pruning is a method to reduce the size of the model
and the number of model parameters under the condition
of controlling the loss in detection accuracy, so as to
speed up the efficiency of model inference [14]. In this
case study, the redundant nodes in the stacked LSTM
model are pruned to reduce the model size and improve
the reasoning efficiency. At the same time, pruning the
network nodes in the LSTM can maintain a structured
computing architecture for customizable computing.
◗ Customizable computing refers to using the parallel and
pipelined computing advantages of the FPGA to design a
special computing architecture for the intelligent assessment
model [15]. Since the main computing load of the
stacked LSTM model comes from matrix-vector multiplication,
the computing efficiency can be improved by
designing a parallel and pipelined hardware computing
architecture in FPGA [16]. After the optimization by
customizable computing, a customized computing unit
in the FPGA that can quickly infer the stacked LSTM
model is obtained.
For demonstration, the UAV embedded OCM instrument is
designed to achieve online fault detection of airborne sensors
IEEE Instrumentation & Measurement Magazine
27

Instrumentation & Measurement Magazine 25-5

Table of Contents for the Digital Edition of Instrumentation & Measurement Magazine 25-5

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