Development of the Sensory Network for the Vibration-based Fault

Detection and Isolation in the Multirotor UAV Propulsion System

Adam Bondyra, Przemysław Ga¸sior, Stanisław Gardecki and Andrzej Kasi´nski

Institute of Control, Robotics and Information Engineering,

Poznan University of Technology, Piotrowo 3A, Poznan, Poland

Keywords:

Multirotor UAV, Fault Detection, Propeller Damage, Vibration Analysis, Random Decision Trees.

Abstract:

In this paper, a fault detection and isolation (FDI) system for propeller impairments of the multirotor UAV

is presented. The algorithm is based on the processing of signal vectors from the set of vibration sensors

located close to the propulsion units. Axial and radial vibrations are measured using MEMS accelerometers

that provide data for the feature extraction based on the Fast Fourier Transform (FFT). Characteristic fault

signatures extracted from vibration signals are used to detect and localize damaged blades using the set of

random decision trees. A method was evaluated with data gathered during numerous test ﬂights and validated

in relation to signal acquisition time and number of classiﬁers in the ensemble. Results show over 95%

sensitivity in detecting and isolating faulty rotor states. The presented approach is an effective and low-cost

solution, very versatile to implement in the arbitrary UAV.

1 INTRODUCTION

An increasing popularity of micro multirotor un-

manned aerial vehicles leads to concerns regarding

the safety of their operation in the human-shared

environment (Clothier and Walker, 2006). Despite

many possible applications of the small-scale UAVs,

such as civil security control, trafﬁc supervision or

environmental management, a reliable safety mech-

anisms are essential for their ubiquitous operations

(Mohammed et al., 2014). A possible solution is an

introduction of the fault-tolerant control (FTC) tech-

niques that increase the reliability of the micro UAVs

by ensuring some minimal degree of system’s perfor-

mance during fault scenarios (Valavanis, 2017). Re-

cently, a problem of designing and developing ac-

tive FTC systems becomes particularly popular for

certain aerospace applications (Fekih, 2014). While

passive fault-tolerant control relies mainly on meth-

ods that are robust enough to withstand system faults,

an active approach consists of the Fault Detection

(FD, FDI) module, a system’s component that de-

tects, identiﬁes and localizes the fault. On this basis,

the re-arrangement of control strategy is introduced

(Witczak and Pazera, 2016), (Fekih, 2014). The ac-

tive approach to the FTC is the main principle of the

solution presented in this article. There are two most

common types of possible system faults in multirotor

UAVs: errors in the state estimation methods caused

by the faulty or inaccurate sensory system (Gardecki

et al., 2014) and impairment of the actuators. Propul-

sion faults include an improper operation of elec-

tric motors, faulty electronic speed controllers (ESCs)

and above others, physical impairment of rotor blades

(Gorospe and Kulkarni, 2017). This last type of sys-

tem degradation is exceptionally dangerous because

it leads to the loss of the thrust force, disturbed thrust

balance and increased power consumption. Moreover,

increased airframe vibrations degrade quality of the

state estimation as well (Qi et al., 2013). As most of

multirotor UAVs are underactuated systems, the loss

of a single motor-propeller unit may easily lead to a

crash (Valavanis, 2017). In addition, propeller blade

damages are very likely to occur in real-world scenar-

ios, especially during the ﬂight in closed spaces.

In this paper, a data-driven method for detecting

and isolating rotor impairments in the micro multiro-

tor UAV system is presented. An algorithm is based

on the analysis of vibrations signals obtained by the

network of sensors located in a few designated places

of the mechanical structure of the UAV. Information

about fault occurrence and its location is extracted

from the set of signal features with the following clas-

siﬁcation stage. This paper is organized as follows:

next section mentions related research and solutions.

The third part presents the problem statement and ini-

102

Bondyra, A., G ˛asior, P., Gardecki, S. and Kasi

nski, A.

Development of the Sensory Network for the Vibration-based Fault Detection and Isolation in the Multirotor UAV Propulsion System.

DOI: 10.5220/0006846801020109

In Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2018) - Volume 2, pages 102-109

ISBN: 978-989-758-321-6

tial assumptions made prior to the development of the

presented method. Sections 4-6 present in details the

principles of operation of the fault diagnosis system,

both in terms of hardware structure and software al-

gorithms. The seventh section describes the experi-

mental setup and shows the results and performance

of fault diagnosis evaluated during the series of test

ﬂights. A ﬁnal chapter summarizes the whole article

and points some drawbacks of the method as well as

possible future endeavors.

2 RELATED WORK

Fault detection methods for multirotor UAVs fall

into two general schemes of FD systems: model-

based and model-free approaches (Fekih, 2014). The

ﬁrst category of solutions require precise, parametric

model of the aerial vehicle and utilize different kinds

of state observers and Kalman ﬁltering techniques

to detect a variety of possible faults (Merheb et al.,

2014), (Zhaohui and Noura, 2013) and (Rago et al.,

1998). On the other hand, another class of methods

utilizes expert knowledge and machine learning in the

model-free approaches (Fekih, 2014). In some cases,

characteristic features and indicators of faulty system

states are obtained thanks to the signal processing al-

gorithms.

A vibration-based condition monitoring is a well-

known technique, especially when it comes to the

fault diagnosis in the machinery consisting of the ro-

tating parts (Nandi et al., 2005). However, there are

only a few attempts to use a signal-processing based

methods for the fault detection in the ﬁeld of mul-

tirotor UAVs (Jiang et al., 2015). A prior research

performed by authors resulted in a simple, signal-

processing based fault diagnosis solution based en-

tirely on the data acquired by the on-board AHRS (Al-

titude and Heading Reference) subsystem (Bondyra

et al., 2017). However, this method was unable to

localize faults, only to detect their occurrence and es-

timate their scale.

3 PERFORMANCE OF THE

PROPULSION SYSTEM WITH

IMPAIRED ROTORS

Prior to the development of the fault detection

method, authors tried to estimate the impact of ro-

tor blade damage to the performance of multirotor

UAV propulsion system. A series of stationary thrust

tests were performed using the custom-built test stand

Figure 1: The propulsion test stand and set of tested rotors

with different degrees of structural damage.

(Aszkowski et al., 2017). During the experiment, a

set of three typical 10-inch propellers was used. A

complete propulsion unit consisted of propeller, Elec-

tronic Speed Regulator (ESC) and MN3310 BLDC

electric motor. While the ﬁrst blade set was in a

brand-new condition, two other propellers were sub-

jects to different degrees of damage.

Measured parameters were thrust, angular veloc-

ity of propellers and power consumption in relation to

the whole range of the PWM control signal. Results

of performed tests are illustrated in the Fig. 2.

0 10 20 30 40 50 60 70 80 90 100

-5

Thrust [N]

0 10 20 30 40 50 60 70 80 90 100

5000

10000

Rotational speed [rpm]

0 10 20 30 40 50 60 70 80 90 100

Control signal (PWM duty cycle) [%]

-0.05

0.05

0.1

0.15

Efficiency [N/W]

Healthy rotor

Damaged blades 1

Damaged blades 2

Figure 2: Propulsion performance measurements with

healthy and damaged rotors.

Analysis of the operation of impaired rotors leads

to a couple of conclusions. While the ESC ensures

that the angular velocity of propellers remains un-

changed, the loss of generated thrust can be observed

even in the case of a slight blade damage. The power

efﬁciency of the propulsion unit, deﬁned as a thrust

generated with a single watt of electric power, de-

creases signiﬁcantly. In addition, a major disturbance

in thrust measurements can be observed in the middle

of the control signal range. Analysis of the remain-

ing data recorded by the 6-axis force/torque sensor

lead to the conclusion that mechanical resonance is

Development of the Sensory Network for the Vibration-based Fault Detection and Isolation in the Multirotor UAV Propulsion System

103

caused by the increased level of vibrations generated

with the rotating propeller. This observation suggests

that even slightly damaged rotors cause a signiﬁcant

and observable increase of structure vibrations, which

can be analyzed and processed in order to identify and

locate the fault.

4 SENSOR NETWORK

ARCHITECTURE

The approach presented in this article is based on the

development of a specialized embedded system that

serves as a real-time diagnostic module for an ar-

bitrary multirotor UAV. Aims of the design were to

provide a low-cost and versatile system, independent

from the applied ﬂight controller. Three different cat-

egories of components were used:

• Sensory modules, designed as miniature printed

circuit boards equipped with the MMA8452 low

cost, 3-axis accelerometers along with a few aux-

iliary components. Every single module is placed

close to the consecutive propulsion unit (the ESC-

motor-rotor set). The sensors’ reference frames

are set in an identical manner in relation to the

work plane of the closest rotor. The PCBs are

mounted in the 3D-printed plastic brackets to en-

sure proper propagation of vibrations from faulty

propellersto acceleration sensors. For the purpose

of this research, a set of four sensors was used

along with quadrotor UAV. However, the number

of sensors can be easily extended to ﬁt hexa- and

octo- copters, as long as there is one sensor for ev-

ery propulsion unit. Parameters and conﬁguration

settings of the MEMS sensors used in the project

are presented in the table 1. In the description be-

low, g denotes the earth’s gravitational accelera-

tion.

Table 1: Operational parameters of the MMA8452 ac-

celerometer.

Parameter Value

No of sensing axes 3 (cartesian)

Data rate 400Hz

Sensing range +-8g

Digital resolution 12-bit

Measurement resolution 3.9mg

Unit cost 2 EUR

• Data acquisition unit (DAQ), developed as a sin-

gle PCB equipped with a STM32F4-family micro-

controller. The module is located in the central

part of the UAV and delivers a supply voltage for

the rest of FDI system components. Sensor mod-

ules are connected using the high-speed I

C bus

with a serial bus clock set to 400kHz. The micro-

controller’s ﬁrmware polls periodically the set of

sensors and converts raw measurements into read-

able data frames. Each data packet contains vec-

tors with measurements of axial and radial vibra-

tions for every accelerometer and corresponding

propulsion unit. The data is collected with the rate

of 400Hz, which corresponds to the output data

rate of MMA8452 sensors.

• Data processing unit, used for the ﬁnal data ac-

quisition, storage and processing. Authors uti-

lized an easily available Raspberry Pi Zero minia-

ture, single-board computer. However, any ad-

vanced processing platform may be used, as long

as its weight meets the lift capabilities of the UAV.

The computer is running a Linux operating sys-

tem and acquires data packets from the DAQ via

the serial interface.

Figure 3: Hardware architecture of the developed FDI sys-

tem.

5 ANALYSIS OF VIBRATION

SIGNALS

Authors assumed that the main frequency of vibra-

tion signal will correspond to the angular velocity of

ICINCO 2018 - 15th International Conference on Informatics in Control, Automation and Robotics

104

the damaged propeller. Therefore, the initial signal

analysis focused on observing vibration spectra ob-

tained by the network of sensors. First experiments

were performed during steady hover ﬂight, with the

angular velocity of rotors as constant as possible. A

Falcon V5 drone (Bondyra et al., 2015) was selected

for the test platform along with the embedded system

described in the previous section. Fig. 4 presents the

UAV partial CAD model along with sensors place-

ment and propulsion units’ notation.

Figure 4: The partial CAD model of the Falcon V5 UAV

with locations of vibration sensors.

The aim of this approach was to determine some

dependencies in the vibration signals which could be

used for the fault classiﬁcation algorithm. Fig. 5

shows exemplary averaged spectra of radial and ax-

ial vibrations obtained during 10 seconds of the data

acquisition. In this case, all four rotors were undam-

aged.

Overview of recorded spectra shows a signiﬁcant

amount of vibrations of the mechanical structure of

the UAV. However, no indicator of actuator faults can

be seen. In another scenario, the fault occurred at the

propulsion unit A with the angular velocity of pro-

pellers varying between 550-600 rad/s. Spectra of

recorded vibration signals are shown in the Fig. 6.

Measurements of radial vibrations show clearly

the faulty state with the characteristic vibration fre-

quency of about 90 Hz. However, the spectral peak

Figure 5: Averaged spectra of vibration signals recorded

with undamaged rotors.

Figure 6: Averaged spectra of vibration signals recorded

during the fault occurrence at the unit A.

Development of the Sensory Network for the Vibration-based Fault Detection and Isolation in the Multirotor UAV Propulsion System

105

can be observed with the highest amplitude at the sen-

sors B and D. The spectrum obtained with the sensor

placed closest to the faulty actuator (A) shows mod-

erate frequency peak, while the sensor C, located at

the opposite end of the UAV mechanical structure in-

dicates the dampening of the fault frequency. These

observations lead to the following conclusions: due

to the nature of the vibration propagation along the

airframe of the multirotor, the fault indicators are not

necessarily shown by the closest accelerometer. Ob-

servations from all of the sensors are required to de-

termine fault location. However, the spectral patterns

are clearly visible for the fault detection itself. On

the other hand, characteristic fault frequency varies

during the ﬂight with regard to the angular velocity

of rotors. In addition, a variety of frequency compo-

nents from different vibration sources can be seen in

spectra. It leads to a conclusion, that simple fault fre-

quency detection methods are insufﬁcient and a ma-

chine learning mechanism can be introduced.

6 FAULT DETECTION METHOD

An algorithm for the fault detection and localization

comprises three main stages:

6.1 Data Acquisition

The vibration signals are acquired thanks to the set

of MEMS sensory modules and the DAQ. Then, some

basic operations are performed on the raw signal vec-

tors: the normalization and windowing of the samples

using the Hanning window. The length of the sam-

pling window (T

) is a parameter affecting the per-

formance of the diagnostic method. Its impact is pre-

sented in the section 7.

6.2 Feature Extraction

For pre-processed signal vectors, the single-sided am-

plitude spectrum is computed using the Fast Fourier

Transform (FFT). Then, for 16 predeﬁned frequency

ranges (frequency bins), the RMS of the spectrum

is computed. Calculated values create a vector of

features for single-axis, single-sensor measurements.

Frequency bins are linearly spaced and have an equal

width of 5Hz. However, lower and upper frequency

limits for the ﬁrst and last bin are parameters to be

tuned. Since the fault frequency corresponds directly

to the angular velocity of UAV rotors, the spectrum

analysis window is narrowed to ﬁt between 60 to 140

Hz. These frequencies resemble angular velocities

within the range of 370 to 880 rad/s, which are typ-

ical for most propulsion systems of micro multirotor

UAVs. The process of splitting the amplitude spec-

trum into frequency bins is summarized graphically

in the Fig. 7.

Figure 7: Principles of the feature extraction: single axis vi-

bration spectrum with corresponding set of calculated sig-

nal features.

With features extracted from the axial and ra-

dial measurements of every accelerometer, the feature

vector is formed. The data measured at the vertical

axis was omitted because further analysis has shown

the negligible signiﬁcance of obtained features. A

complete vector of vibration features for quadrotor

consists of 128 elements with axial and radial features

of four propulsion units.

6.3 Fault Signature Classiﬁcation

The ﬁnal stage of signal processing pipeline is a fea-

ture classiﬁcation. A set of random decision trees

(Breiman, 2001) was used for this task. In order to

detect and isolate actuator faults, a classiﬁer processes

computed vector of features and assigns an adequate

class based on the trained vibration patterns. Its prin-

ciple of operation is based on aggregating many weak

classiﬁer grouped in common ensemble (bag). A ran-

dom subset of predictors is chosen for each decision

split during the process of growing decision trees.

The classiﬁcation is based on the majority vote be-

tween ﬁnal outcomes.

The classiﬁer adjusted for quad-rotor UAV is

trained to recognize 11 different classes: all-healthy

rotors state, single faults in each propulsion unit and

double simultaneous impairments for every possible

pair of rotors.

ICINCO 2018 - 15th International Conference on Informatics in Control, Automation and Robotics

106

During the experimental stage (see sec. 7), a wide

dataset was acquired, with more than 13000 indepen-

dent data samples. The acquired data was split ran-

domly into 70 % of training dataset while remaining

30% was used for a validation. A summary of the

fault detection and isolation process is presented in

the Fig. 8.

Figure 8: Data ﬂow diagram of the fault detection and iso-

lation system.

7 PERFORMANCE EVALUATION

7.1 Experimental Setup

In order to gather the dataset and validate the perfor-

mance of developed fault detection and isolation sys-

tem, a series of test ﬂights was performed. The Falcon

V5 drone in the quad-rotor conﬁguration was used for

this task. During experiments, a variety of possible

actuator faults were tested. First few ﬂights were exe-

cuted with all rotors in a brand-new condition. Other

tests were performed with damaged propellers: all

cases of single unit faults and some double rotor im-

pairments, with adjacent and opposite pairs of dam-

aged rotors. For every case, over 300 seconds of the

in-ﬂight vibration data were recorded while the UAV

performed different ﬂight maneuvers. Then, the ac-

quired dataset was post-processed in order to train the

classiﬁer and validate the method. A complete FDI

system, consisting of vibration sensors, DAQ and data

processing unit was mounted on-board of the UAV.

Fig. 9 shows the Falcon V5 UAV during the ﬂight.

7.2 Tuning the Classiﬁer

The processing of the experimental results has shown

that two parameters are essential to determine the per-

formance of detecting the fault occurrence and loca-

Figure 9: Falcon V5 UAV used for the performance evalua-

tion of the FDI system.

tion. Since the time of the acquisition of axial and

radial vibration signals is variable, the longer is the

time window, the more information and higher signif-

icance of signal features can be obtained. In addition,

an increased number of decision trees in the ensem-

ble may provide a higher accuracy. An analysis was

performed on the basis of an out-of-bag (OOB) er-

ror estimate chosen as a parameter to determine the

accuracy of the classiﬁcation. The OOB measure de-

livers the estimate of true classiﬁcation errors based

on testing the examples excluded during the training

stage(Banﬁeld et al., 2007). The dataset obtained dur-

ing the series of test ﬂights was evaluated in relation

to the window length and number of decision trees.

Results of the experiment are shown in the Fig. 10.

100

0.1

0.2

200

0.3

0.4

Window length [ms]

0.5

OOB error rate [%]

0.6

300

0.7

No of grown trees

100

0.8

150

400

200

500

250

0.2

0.3

0.4

0.5

0.6

0.7

Figure 10: Performance evaluation of the classiﬁer.

An initial performance evaluation lead to a con-

clusion, that vibration signals have to be acquired for

at least 0.2 seconds (T

= 200ms). This time period

is the most signiﬁcant factor inﬂuencing the fault de-

tection time lasting since the occurrence of the im-

pairment to the ﬁnal decision of the FDI system. A

number of decision trees required for the correct clas-

Development of the Sensory Network for the Vibration-based Fault Detection and Isolation in the Multirotor UAV Propulsion System

107

siﬁcation of fault signatures has a lesser impact since

bags of several hundreds trees are not computation-

ally demanding.

7.3 Experimental Results

After initial tuning of the classiﬁer parameters, a win-

dow length of 300ms was chosen for the exemplary

performance evaluation. The complete ensemble of

random decision trees consisted of 200 instances. The

fault detection and isolation system was validated

with remaining 30 % of the dataset.

Figure 11: Results of the fault detection and isolation exper-

iment - confusion matrix for T

= 300ms and the ensemble

of 200 decision trees.

According to the Fig. 11, the FDI system has

proven very high accuracy in detecting the fault oc-

currence itself. In over 99% of cases, a healthy state

of rotors was properly identiﬁed. In addition, sin-

gle rotor impairments were detected correctly in over

93.9% of cases. However, faults in units A and C are

recognized in more cases. It may lead to a conclu-

sion that the proper placement of the sensors may af-

fect the performance of the fault detection since every

vibration sensor is mounted in the same relation and

distance to the propulsion unit. Double rotor damages

are harder to detect, but they are rarely mistaken for

the healthy state. In most cases of the wrong classiﬁ-

cation, the localization of damaged units failed, while

the system indicated correctly that the dual fault oc-

curred. Especially the detection of two faulty adja-

cent actuators (the C-D pair) was harder to distinguish

from other fault classes.

7.4 Comparison of Results

Very few methods based on the signal processing

of airframe vibrations can be found in the litera-

ture. However, the performance of fault estimation

method was compared with some similar algorithms.

Sensitivity of presented method exceeds 95%, which

is similar to existing solutions (Jiang et al., 2015),

(Bondyra et al., 2017). Another performance factors,

such as rate of missed faults and false detection of

occurrence are comparable as well. However, these

methods lack the ability to isolate the fault and de-

liver its precise location, which is the main advantage

and novelty of presented approach.

8 CONCLUSIONS

Several advantages of the proposed solution can be

pointed out. The developed fault detection and iso-

lation system provides high detection rate. Fault oc-

currence is detected in more than 95 % of cases us-

ing quite a simple signal processing methods and not

computationally demanding classiﬁcation method. In

case of single actuator faults, implementation of the

network of vibration sensors allows to isolate and lo-

calize fault precisely. Clear indicators of double ro-

tor damages are provided. However, these categories

of faults are easily mistaken in terms of isolating the

speciﬁc faulty rotor pair. In addition, utilizing an ex-

ternal, signal processing based system is a very ver-

satile solution and can be easily implemented in the

arbitrary multirotor UAV with the small cost.

On the other hand, in case of implementing the

external sensor network, additional equipment of the

UAV is required. Moreover, the response time of the

FDI system is dependent mostly on the time of signal

acquisition. Hence, there is some signiﬁcant delay be-

tween fault occurrence and diagnostics information.

Application of the presented FDI system to the arbi-

trary multirotor requires a process of training of the

classiﬁer. Further research will focus on tuning the

method, decreasing the required data acquisition time

and implementation of the fault detection system with

the corresponding fault-tolerant control scheme.

ACKNOWLEDGEMENTS

This research was funded by the Poznan University of

Technology grant DSPB/0162.

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