Instrumentation & Measurement Magazine 25-5 - 28

Fig. 5. UAV embedded OCM instrument based on heterogeneous computing.
in the UAV flight control system. After model reduction, the
number of LSTM nodes in different sensor fault detection
models is distinct. If all fault detection models are designed as
custom computing units of different scales, it will consume a
lot of FPGA computing resources, so that these custom computing
units cannot be deployed in one heterogeneous SoC.
Therefore, an embedded OCM instrument with parameter
configurable custom computing units is developed in this case
study. As shown in Fig. 5, the embedded OCM instrument
adopts the Zynq XC7Z045 as the heterogeneous SoC and has a
dual-channel RS-422 interface that can be used to receive perceived
data and transmit fault detection results online. Its size
is 8 cm×6.5 cm×3.5 cm, and its weight is about 90 g.
In the SoC, the ARM processor is mainly used for scheduling
multiple fault detection tasks and implementing fault
detection based on prediction residuals. An FPGA is used to
achieve stacked LSTM-based prediction, which mainly includes
parameter configurable custom computing units and
DMA controllers. In this design, fault detection models with
similar scales can share a parameter configurable custom computing
unit to realize more fault detection tasks within the
limited FPGA resources. The computing efficiency of multiple
detection tasks is guaranteed. The DMA controller is used
for high-speed transmission of model parameters to realize the
switching of different fault detection tasks.
In this UAV embedded OCM instrument, there are four
fault detection models for monitoring four different sensors.
The input feature dimension d of each fault detection model
is 19, the sliding window length L is 10, and the number of
LSTM network nodes k in the stacked LSTM is set to 40. After
model reduction, under the condition of controllable accuracy
loss, the number of LSTM network nodes in the four fault detection
models is 34, 29, 21, and 20 respectively. The total time
consumption of the four fault detection models when running
online is 1.128 ms in the designed OCM instrument, which
means that about 53 fault detection models can be run in the
flight control cycle (20 ms) when considering the data transmission
time consumption (about 5 ms). It is worth noting
that the power consumption of the designed OCM instrument
28
is only 3.615 watts. In addition, the detection performance of
the fault detection model in the embedded OCM instrument
is consistent with that obtained in the PC. Through the above
demonstration, the designed OCM instrument based on heterogeneous
computing presents the advantage of high energy
efficiency and is suitable for online condition monitoring of a
UAV.
It is worth noting that the embedded OCM instrument
technology demonstrated in this case study can provide a
reference for the embedded OCM instruments of other UAS
like UGV, USV, and UUV. By properly adjusting the intelligent
assessment model, and performing model reduction and
customizable computing optimization, the OCM of different
UAS can be achieved. Moreover, this study can also provide a
technical basis for the deployment of AI algorithms for other
purposes in embedded platforms.
Conclusions
UAS have been widely used in both civil and military fields.
Its excellent mission performance has greatly increased its deployment.
However, its high accident rate has attracted the
attention of researchers, and there is an urgent demand for
online condition monitoring to ensure the safety of UAS. To
meet this requirement, the guidelines for designing an embedded
OCM instrument are given, and the application scenarios
of different available onboard computing platforms are analyzed.
It shows that the heterogeneous SoC is suitable for
online analysis of the perceived data stream to realize UAS
OCM. Furthermore, a UAV embedded OCM instrument is designed
for demonstration. In this instance, four fault detection
tasks are designed to realize UAV OCM. The instrument uses
the RS-422 interface to receive the perceived data, runs four
fault detection models in a heterogeneous SoC, and outputs
the fault information of the UAV online. The time consumption
of the online operation of the four fault detection models
is 1.128 ms, which means that 53 fault detection tasks can be
run in the typical flight control cycle. Moreover, the power consumption
of the UAV embedded OCM instrument is only 3.615
watts. The results show that the designed UAV embedded
IEEE Instrumentation & Measurement Magazine
August 2022

Instrumentation & Measurement Magazine 25-5

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

Instrumentation & Measurement Magazine 25-5 - Cover1
Instrumentation & Measurement Magazine 25-5 - Cover2
Instrumentation & Measurement Magazine 25-5 - 1
Instrumentation & Measurement Magazine 25-5 - 2
Instrumentation & Measurement Magazine 25-5 - 3
Instrumentation & Measurement Magazine 25-5 - 4
Instrumentation & Measurement Magazine 25-5 - 5
Instrumentation & Measurement Magazine 25-5 - 6
Instrumentation & Measurement Magazine 25-5 - 7
Instrumentation & Measurement Magazine 25-5 - 8
Instrumentation & Measurement Magazine 25-5 - 9
Instrumentation & Measurement Magazine 25-5 - 10
Instrumentation & Measurement Magazine 25-5 - 11
Instrumentation & Measurement Magazine 25-5 - 12
Instrumentation & Measurement Magazine 25-5 - 13
Instrumentation & Measurement Magazine 25-5 - 14
Instrumentation & Measurement Magazine 25-5 - 15
Instrumentation & Measurement Magazine 25-5 - 16
Instrumentation & Measurement Magazine 25-5 - 17
Instrumentation & Measurement Magazine 25-5 - 18
Instrumentation & Measurement Magazine 25-5 - 19
Instrumentation & Measurement Magazine 25-5 - 20
Instrumentation & Measurement Magazine 25-5 - 21
Instrumentation & Measurement Magazine 25-5 - 22
Instrumentation & Measurement Magazine 25-5 - 23
Instrumentation & Measurement Magazine 25-5 - 24
Instrumentation & Measurement Magazine 25-5 - 25
Instrumentation & Measurement Magazine 25-5 - 26
Instrumentation & Measurement Magazine 25-5 - 27
Instrumentation & Measurement Magazine 25-5 - 28
Instrumentation & Measurement Magazine 25-5 - 29
Instrumentation & Measurement Magazine 25-5 - 30
Instrumentation & Measurement Magazine 25-5 - 31
Instrumentation & Measurement Magazine 25-5 - 32
Instrumentation & Measurement Magazine 25-5 - 33
Instrumentation & Measurement Magazine 25-5 - 34
Instrumentation & Measurement Magazine 25-5 - 35
Instrumentation & Measurement Magazine 25-5 - 36
Instrumentation & Measurement Magazine 25-5 - 37
Instrumentation & Measurement Magazine 25-5 - 38
Instrumentation & Measurement Magazine 25-5 - 39
Instrumentation & Measurement Magazine 25-5 - 40
Instrumentation & Measurement Magazine 25-5 - 41
Instrumentation & Measurement Magazine 25-5 - 42
Instrumentation & Measurement Magazine 25-5 - 43
Instrumentation & Measurement Magazine 25-5 - 44
Instrumentation & Measurement Magazine 25-5 - 45
Instrumentation & Measurement Magazine 25-5 - 46
Instrumentation & Measurement Magazine 25-5 - 47
Instrumentation & Measurement Magazine 25-5 - 48
Instrumentation & Measurement Magazine 25-5 - 49
Instrumentation & Measurement Magazine 25-5 - 50
Instrumentation & Measurement Magazine 25-5 - 51
Instrumentation & Measurement Magazine 25-5 - 52
Instrumentation & Measurement Magazine 25-5 - 53
Instrumentation & Measurement Magazine 25-5 - 54
Instrumentation & Measurement Magazine 25-5 - 55
Instrumentation & Measurement Magazine 25-5 - 56
Instrumentation & Measurement Magazine 25-5 - 57
Instrumentation & Measurement Magazine 25-5 - 58
Instrumentation & Measurement Magazine 25-5 - 59
Instrumentation & Measurement Magazine 25-5 - 60
Instrumentation & Measurement Magazine 25-5 - 61
Instrumentation & Measurement Magazine 25-5 - 62
Instrumentation & Measurement Magazine 25-5 - 63
Instrumentation & Measurement Magazine 25-5 - 64
Instrumentation & Measurement Magazine 25-5 - 65
Instrumentation & Measurement Magazine 25-5 - 66
Instrumentation & Measurement Magazine 25-5 - 67
Instrumentation & Measurement Magazine 25-5 - Cover3
Instrumentation & Measurement Magazine 25-5 - Cover4
https://www.nxtbook.com/allen/iamm/26-6
https://www.nxtbook.com/allen/iamm/26-5
https://www.nxtbook.com/allen/iamm/26-4
https://www.nxtbook.com/allen/iamm/26-3
https://www.nxtbook.com/allen/iamm/26-2
https://www.nxtbook.com/allen/iamm/26-1
https://www.nxtbook.com/allen/iamm/25-9
https://www.nxtbook.com/allen/iamm/25-8
https://www.nxtbook.com/allen/iamm/25-7
https://www.nxtbook.com/allen/iamm/25-6
https://www.nxtbook.com/allen/iamm/25-5
https://www.nxtbook.com/allen/iamm/25-4
https://www.nxtbook.com/allen/iamm/25-3
https://www.nxtbook.com/allen/iamm/instrumentation-measurement-magazine-25-2
https://www.nxtbook.com/allen/iamm/25-1
https://www.nxtbook.com/allen/iamm/24-9
https://www.nxtbook.com/allen/iamm/24-7
https://www.nxtbook.com/allen/iamm/24-8
https://www.nxtbook.com/allen/iamm/24-6
https://www.nxtbook.com/allen/iamm/24-5
https://www.nxtbook.com/allen/iamm/24-4
https://www.nxtbook.com/allen/iamm/24-3
https://www.nxtbook.com/allen/iamm/24-2
https://www.nxtbook.com/allen/iamm/24-1
https://www.nxtbook.com/allen/iamm/23-9
https://www.nxtbook.com/allen/iamm/23-8
https://www.nxtbook.com/allen/iamm/23-6
https://www.nxtbook.com/allen/iamm/23-5
https://www.nxtbook.com/allen/iamm/23-2
https://www.nxtbook.com/allen/iamm/23-3
https://www.nxtbook.com/allen/iamm/23-4
https://www.nxtbookmedia.com