Energies: Failure Detection by Signal Similarity Measurement of Brushless DC Motors
Energies: Failure Detection by Signal Similarity Measurement of Brushless DC Motors
Energies: Failure Detection by Signal Similarity Measurement of Brushless DC Motors
Article
Failure Detection by Signal Similarity Measurement
of Brushless DC Motors
Vito Mario Fico 1, * , Antonio Leopoldo Rodríguez Vázquez 1 , María Ángeles Martín Prats 2
and Franco Bernelli-Zazzera 3
1 Skylife Engineering, 41092 Seville, Spain; antoniorv@skylife-eng.com
2 Escuela Técnica Superior de Ingeniería, Electronics Engineering Department, Universidad de Sevilla,
41092 Seville, Spain; mmprats@us.es
3 Department of Aerospace Science and Technology, Politecnico di Milano, 20156 Milan, Italy;
franco.bernelli@polimi.it
* Correspondence: vito.fico@skylife-eng.com
Received: 18 March 2019; Accepted: 4 April 2019; Published: 9 April 2019
Abstract: In recent years, Brushless DC (BLDC) motors have been gaining popularity as a solution for
providing mechanical power, starting from low cost mobility solutions like the electric bikes, to high
performance and high reliability aeronautical Electro-Mechanical Actuator (EMA). In this framework,
the availability of fault detection tools suited to these types of machines appears necessary. There is
already a vast literature on this topic, but only a small percentage of the proposed techniques have
been developed to a sufficiently high Technology Readiness Level (TRL) to be implementable in
industrial applications. The investigation on the state of the art carried out during the first phase of
the present work, tried to collect the techniques which are closest to possible implementation. To fill
a gap identified in the current techniques, a partial demagnetisation detection method is proposed in
this paper. This technique takes advantage of the asymmetries generated in the current by the missing
magnetic flux to detect the failure. Simulations and laboratory experiments have been carried out to
validate the idea, showing the potential and the easy implementation of the method. The results have
been examined in detail and satisfactory conclusions have been drawn.
Keywords: failure; PMSM; detection; diagnosis; BLDC; brushless; phase voltage similarity
1. Introduction
With the increasing dependence on electrical devices, condition monitoring of electrical machines
is becoming increasingly important, in particular when aerospace applications are involved, since
safety becomes one key design driver.
Performance of flight actuators on a damaged aircraft is not as important as ensuring that the
remaining actuators continue in operation until the aircraft can land safely. An adequate level of
reliability can be reached only by using diagnostic tools. The term diagnosis indicates the process
of determining by examination the nature and circumstances of a non-nominal condition. It can
be performed collecting information provided by on-line sensors and extracting from them the
characteristics that show the current condition of equipment or a process.
This instrument has great importance because an accurate and efficient means of condition
monitoring and machine fault diagnosis can drastically improve reliability and stability of the plant
as well as reducing costs, leading to a system with virtually no need for programmed maintenance.
Statistical studies show that expected reliability can be improved by up to 5–6 percentage points with
the use of monitoring [1]. An ideal diagnostic procedure should take the minimum measurements
necessary from a machine and by analysis, extract a diagnosis, so that its condition can be inferred to
give a clear indication of incipient failure modes in the minimum time.
Before developing monitoring techniques, several studies must be carried out to understand the
failure mechanism in an electrical machine. Broadly, the failure in electrical machine could be classified
as electrical or mechanical in nature depending upon the root cause of failures. To characterise the
nature of the failure in electrical machines, suitable signature acquisition and processing are required.
Noninvasive monitoring is achieved by relying on easily measured electrical or mechanical quantities,
such as current, voltage, flux, torque, and speed. The reliable identification and isolation of faults is
still, however, under investigation as there are some current issues [2]:
• Definition of a single diagnostic procedure for identification and isolation of any type of faults
• Insensitivity to operating conditions
• Reliable fault detection for position, speed and torque controlled drives
• Reliable fault detection for drives in time-varying conditions
• Quantitative fault detection in order to state an absolute fault threshold, independent of
operating conditions
The first step in a research work is certainly the literature review, created in order to understand
not only the current state of affairs, but also the evolution. In the field of engineering, and in particular
for aerospace engineering, traditional (or narrative) revision is preferred, in which the authors decide
the papers to be included in the review based on their deep knowledge and experience and giving a
personal vision and interpretation of the topic. The systematic literature review (Systematic Literature
Review (SLR)) is a very rigorous and reliable standardised scientific methodology, mainly characterised
by its objectivity. This is used with excellent results in many areas, including psychology, medicine and
in recent years also in software engineering. In this paper, a SLR on high TRL fault detection techniques
for Brushless DC (BLDC) motors is presented. The guidelines presented in [3] for software engineering
and [4] for systems and automation engineering have been taken into account; in particular from the
latter many ideas and suggestions have been followed when using and evaluating the protocol.
The aim of this review is to indicate which techniques are currently being used successfully for
motor fault detection and diagnosis, also to provide the industry with tested techniques and a high
level of preparedness. In this view, many of the inclusion (Table 1) and exclusion criteria (Table 2)
have been formulated to focus the research on those techniques with demonstrated failure detection
performance at various operational points and can be easily automatable or is already automated.
In particular, PhD theses have been excluded because they do not pass under a peer review
process. Another reason is that normally, the PhD theses result in one or more papers.
Another important point considered in the review has been the possibility to embed the hardware
needed for fault detection within the body of the motor. Indeed, although most papers focused on the
detection by using variables already measured (current, voltage and speed) some authors developed
failure detection techniques by analysing images from external cameras or very sensitive (and bulky)
accelerometers. These techniques are suitable to be implemented only in very particular applications.
2746 papers were initially selected and a first screening to eliminate the duplicates allowed the
deletion of 1111 papers from the list; this was due to the fact that many databases share the same
publishers. Once the duplicates had been removed, it was necessary to eliminate the grey literature
(reviews, books, PhD theses) as it was not possible to exclude it during the research for some databases.
The following step consists in reading the title, abstracts and keywords and in applying the
inclusion and exclusion criteria to the remaining 1635 articles. Often, by only reading these fields it
was not clear whether the article met all the specified requirements or not, so it was necessary to read
the whole article. This problem is mainly due to the fact that in the field of engineering in general,
there is no normalisation of rules to create these fields, as exists in other sectors such as medicine
or psychology.
Energies 2019, 12, 1364 3 of 23
Num. Description
Will propose at least one detection technique (a parameter or an index that clearly and
1
uniquely identifies the failure or an automatic detection algorithm).
The proposed technique should not be restricted to a particular machine type
2
(number of phases, etc.).
To be applied the technique will not need special equipment, configurations, loads or
3
motor operation.
The technique shall have been tested at various operation points (different speed or
4
loads or a combination of both)
Characteristics of the detection and diagnosis algorithm:
a. The algorithm (or index) will have been tested with success for at least one of the
following cases:
b. The paper shall demonstrate that the algorithm is capable to discern between
healthy and faulty
Num. Description
1 Grey literature and secondary studies (reviews, books, PhD theses)
2 Non English written papers
3 Duplicated studies
4 Full paper not available
5 Does not present tests or simulations
6 Uses big sensors, which cannot be embedded in the motor (such as cameras or similar)
7 Does not concentrate on the topic
In conclusion, from the total set of papers, 307 of have been accepted for full paper review and
finally, only 37 papers met the selected criteria for being included in the revision.
In the following subsection the synthesis of such research is reported. It considers statistical data
of the distribution of failures, failure detection techniques and more in general about the chosen topic.
1.1. Discussion
A considerable amount of papers (1635) has been found by consulting several databases adopting
the aforementioned inclusion and exclusion criteria. The research community is very interested in
this topic and the interest has been growing very rapidly over the last few years. Figure 1 shows this
trend by representing the number of papers per year published on this topic (results from 1990 to 2017)
emerged from the research and after the first screening has been passed.
The trend is probably due to the recent surge in developing high reliability electric vehicles and
aircraft, but also to the availability of new techniques and more powerful processors which paved the
way to innovative applications.
In this view it is interesting to see how the number of papers based on certain techniques according
to the year of publication is distributed (Figure 2).
Energies 2019, 12, 1364 4 of 23
30 30 28
25 23
20 17 17 18
14
10
6 7
1 1 1 1 2 1 2
0
92
96
19 8
20 9
20 0
01
20 3
20 4
20 5
20 6
20 7
20 8
20 9
20 0
20 1
20 2
20 3
20 4
20 5
20 6
17
9
9
0
0
0
0
0
0
0
0
1
1
1
1
1
1
1
19
19
19
20
Figure 1. Distribution of papers per Year.
07
08
10
11
13
14
15
16
17
18
20
20
20
20
20
20
20
20
20
20
20
Year
Even if the graph in Figure 2 is not representative of the entire literature, it is possible to draw some
conclusions. First of all, it appears evident that during recent years the techniques based on models,
parameters estimation and Artificial Intelligence (AI) are being used increasingly, often in conjunction
with more established methods like the Motor Current Signature Analysis (MCSA) as classifiers.
Also the number of papers discussing techniques based on MCSA has drastically reduced,
probably because this is an already been a very well studied technique. The graph in Figure 3 shows
the overall distribution of the papers according to the technique used.
The MCSA is still the most used technique, followed by the AI algorithms and the techniques
based on parameter estimation. It has to be said that often the techniques based on AI and Neural
Network (NN) are used just as classificators in conjunction with other, already established, methods
for failure detection. This association has been demonstrated to be of great help in improving the
detection rate and extending the use of the technique for a wider range of speeds and loads.
Another important aspect is the presence of a good portion of papers discussing techniques not
previously classified and grouped under the tag other. Among them it is possible to find innovative
techniques based on High Frequency (HF) Injection [5] or hall effect sensors measurements [6].
Based on the selected papers, the state of the art of BLDC motors fault detection can be outlined
as below.
Energies 2019, 12, 1364 5 of 23
37%
Electromagnetic field monitoring
2% Noise and vibration monitoring
4% MCSA
Model, AI, NN-based techniques
8% Parameters Estimation
31% Other (Specify)
18%
Current In the majority of cases the current is already measured by the motor controller and there
exist a huge quantity of failure detection algorithms based on this variable.
Voltage The voltage also is often already measured by the motor controller. It can be used to extract
the back-ElectroMotive Force (EMF).
Vibrations The motor vibration level is detected by mean of accelerometers, which need to be placed
close to the item to be monitored. The algorithms based on vibrations analysis could present
problems when used in moving systems, like aircraft, due to the coupling of external and
unpredictable vibrations.
Energies 2019, 12, 1364 6 of 23
Output Torque The output torque is measured with torque-meters and can provide very useful
information, but this type of sensors are often big and expensive and are maybe better suited for
critical applications.
Magnetic Flux The magnetic flux can give a deep insight on how the motor is working. In order to
be measured it, it is necessary to include in the motor winding so called search coils, i.e., some
additional windings not connected to the phases. The inclusion of these additional coils is not
common and, although being a simple procedure, it necessitates the motor to be opened and
rewound and to extract from the interior pairs of wires for as many search coils as are inserted.
Estimated Quantities: The techniques based on parameters estimation can detect failures by
estimating the changes in the measured motor parameters as well as quantifying variables which are
not directly measurable, such as:
• Winding resistance,
• Winding inductance,
• Back-EMF,
• Magnetic flux.
Estimation is a powerful tool which permits the use of variables directly related to the fault and
otherwise not measurable, but it depends on models which could be limited to specific situations and
on different parameters which might vary.
10.0 5 1
1
7.5
1 3
5.0 3 2
2
2.5 2 2
1 1 1
1 1 1 1 1 1
0.0
Armature Fault Armature Fault, Armature Fault, Mechanical Faults Permanent Magnetic Permanent Magnetic
Mechanical Faults Permanent Magnetic (bearing failure and Faults (partial or Faults (partial or
(bearing failure and Faults (partial or eccentricity) complete) complete),
eccentricity) complete), Mechanical Faults
Mechanical Faults (bearing failure and
(bearing failure and eccentricity)
eccentricity)
Figure 5. Distribution of the papers according the type of failure and the used technique.
Energies 2019, 12, 1364 7 of 23
is necessary to measure signals over a period of time, which may be obtainable with large industrial
machines working at constant load but rarely so in aeronautic actuators.
The magnetic symmetry of the motor when perturbed, results in the following consequences:
Unbalanced back-EMF throughout one rotor revolution: Even if, for the case of multi-pole, multi-coil
motor, the back-EMF change is not so straightforward to imagine, magnet defect causes
non-uniformity in the distribution of the air-gap flux, resulting in a reduction of the induced
voltage. The decrease in back-EMF translates into an increment of the phase current, due both to
the decrease of its own back-EMF or of the alternative phase’s back-EMF.
Unbalanced and asymmetrical Magnetic Pull: In a healthy motor the ferromagnetic rotor body is
attracted by the magnets with equal strength from every direction. If a magnet is missing, this
equilibrium is broken, generating a net pull force in the direction opposed to the missing magnet.
Increased Cogging Torque: In fractional slot motors such as the the one used here, each magnet
appears in a different position relative to the stator slots. As a result, the cogging torques created
by each magnet is out of phase with the others, therefor the net cogging effect is reduced since
the cogging torque from each magnet sums together and at least partially cancels the cogging
torque from other magnets [19]. Due to the missing magnet, the distribution of the cogging
torque around the physical turn revolution is uneven and, on average, increased.
Abnormal Torque Ripples produced around the mechanical revolution: If the current distribution
around the mechanical revolution has its motion perturbed, the produced torque is consequently
disturbed, producing abnormal torque ripples around the mechanical revolution. It generates
more vibrations and localised accelerations and of the rotor.
Unbalanced Rotor: The rotor itself can be mechanically unbalanced depending if the magnet is
missing (partial or total disintegration) or just demagnetised. In the case of a missing magnet
there is a strong increase in the vibration level.
In addition, the correct phase switching is disturbed during one electrical turn due to the partial
demagnetisation. This effect is variable and depends on the technique used for switching (hall sensors,
resolver or sensor-less).
Energies 2019, 12, 1364 10 of 23
All those effects cause the voltages and currents in the various electrical turns to be different.
In this work, the effects will be investigated by means of FEM analysis and experiments. Also, two fault
indicators are proposed and compared as a means for fast detection of the partial demagnetisation.
1 +∞
!
1
∑ σe σe (e Ai [m] − ē Ai )(e Aj [m + k] − ē Aj )
ij ∗
Ixc = max (1)
n m=− ∞ Ai Aj
with:
This operation generates a symmetric matrix with dimension depending on the number of
pole-pairs pp with ones along the diagonal.
If all the magnets are healthy, the normalised cross-correlation value should be very close to 1.
On the other hand, the higher the demagnetisation of one pole, the greater the difference will be in the
back-EMF signals and the lower the correlation value.
The same matrix can be built-up for each phase. The three matrices obtained can be averaged or
summed. The sum would amplify the correlation difference between the electric turns, but also any
possible spurious data. Conversely, by averaging the matrices, both quantities are attenuated. In this
work, the sum has been chosen due to the fact that experimental data can be accurately filtered and
adjusted before computing the cross-correlation.
The second indicator is the normalised average of the difference between the back-EMF signals of
the electric turns. This is indicated by the symbol Idi f f .
+∞
1 (e − e )
Ai Aj
∑
ij
Idi f f =
(2)
n m=−∞ ( e Ai − e Aj )
This indicator should have a value close to zero for a healthy motor and should increase
proportionally to the magnet failure entity. The normalisation is executed to reduce the dependence on
the motor speed contained in the back-EMF signal.
Also in this case, the indicator generates a symmetric matrix with dimension pp × pp, but with
zeros (as a convention, because the norm also would be zero) on the diagonal.
Even in this case, it has been decided to sum the matrices relative to each phase in order to amplify
any differences.
Once obtained the two matrices of Ixc and Idi f f , the upper triangular terms of each matrix are
summed together to obtain two global indicators, as specified in Formulas (3) and (4):
and
idi f f = ∑ triu( Idi f f ) (4)
Energies 2019, 12, 1364 11 of 23
( pp − 1) · ( pp)
imax
xc = ·3 (5)
2
So for example, a healthy motor with 4 pole-pairs motor should have an ideal value of i xc = 18.
3. FEM Analysis
In order to obtain an accurate FEM model, a specimen motor has been completely dismantled.
During the procedure, most of the motor data necessary for the modelling have been directly observed.
The basic model of the motor has been realised in the RMxprt package of Ansys Electromagnetic
Suite and then exported to Maxwell 2D to perform a transient FEM analysis. Time variation of the
stator currents, torque and speed can be calculated, as well as the induced voltages on stator winding.
The model has been modified to simulate a partial motor demagnetisation. The partial
demagnetisation has been reproduced by simply changing the magnetic properties of one magnet.
In detail, the magnet coercitivity magnitude has been reduced from −560,000 Am to −1000 Am.
Figure 8a displays the magnitude of the magnetic flux established inside the different motor parts,
in the nominal condition, while Figure 8b shows the results of the simulation in the case of a damaged
magnet. A remarkable flux reduction is clearly visible, corresponding to the damaged magnet and a
resulting asymmetry in the magnetic field distribution.
Before the execution of the experimental tests, the simulated data has been computed on the
indicators. First, the phase voltages have been simulated for a complete mechanical revolution. Then,
they have been split into 7 parts, i.e., the number of motor’s polepairs. Each chunk represents a
mechanical revolution.
At this point there are 3 groups, one for each phase, containing the phase voltage signals of the
7 electrical revolutions.
(a) (b)
Figure 8. Comparison of Intensity of the Magnetic Flux Density in healthy and damaged motors.
(a) Intensity of the Magnetic Flux Density in the healthy motor; (b) Intensity of the Magnetic Flux
Density in the motor with a demagnetised magnet.
By comparing the set of figures in Figure 9, it is possible to observe that, while for the healthy
case the phase voltages of the electrical rotations are completely overlapping, for the demagnetised
motor they are different during the non-conducting steps.
Energies 2019, 12, 1364 13 of 23
Voltage [V]
0 0
5 5
10 10
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Time [s] Time [s]
(a) (b)
Figure 9. Comparison of the Phase A Voltages of the healthy and damaged motor—12,500 RPM.
(a) Phase A Voltage—Healthy; (b) Phase A Voltage—Damaged.
It is now possible to compute the indicators i xc and idi f f , by following the procedure summarised
in Figure 7, obtaining the results in Table 4.
The indicators correctly show that the signals relative to electrical turns of the demagnetised motor
are more dissimilar between them. This result will be checked in section with the experimental tests.
4. Experimental Results
The experiments have been carried out by using the P-NUCLEO-IHM001, a development kit
composed by a control board, a power board and a brushless motor.The voltage signals have been
obtained by measuring with the oscilloscope at the pins corresponding to the signals BEMF1, BEMF2
and BEMF3 of the P-NUCLEO board. The primary role of this circuit is to measure the motor back-EMF,
but in this work the entire phase-phase voltage will be acquired and used. The voltage signals are
clamped to 3.3 V in order to be directly read by the microcontroller’s Analog to Digital Converter
(ADC). The motor was fastened to the lab table through the four dedicated fixing screws visible in
Figure 10a. The laboratory temperature was maintained between 20–25 Celsius degrees throughout.
(a) (b)
Figure 10. Demagnetisation process: rotor with a missing magnet (a), highlighted by the yellow circle,
and rotor with the replacement piece of inert material (b), easily recognisable for the red glue.
Voltage [V]
2
0.5
0 0.0
BEMF2[V] 1.0 BEMF2[V]
Voltage [V]
Voltage [V]
2
0.5
0
0.0
BEMF3[V]
Voltage [V]
Voltage [V]
2 1.0
BEMF3[V]
0.5
0
0.0
05 10 15 20 25 30 05 10 15 20 25 30
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Time[s] Time[s]
(a) (b)
Figure 11. Comparison of the measured phase voltage, before and after the filtering. (a) Oscilloscope
acquisition—3000 RPM; (b) Filtered oscilloscope acquisition—3000 RPM.
These phase-phase voltage signals contain information about both the conduction component and
the back-EMF. The flux asymmetry throughout the mechanical revolution, generated by the missing
magnet, is expected to be reflected in these phase voltage as a dissimilarity between the electrical turns.
The measured voltage signals are then filtered by a Butterworth low-pass filter with the cut-off
frequency set to the current angular speed multiplied by the number of pole-pairs. The signals resulting
from filtering the signals in Figure 11a are shown in Figure 11b.
Table 5 summarises the operating conditions, and the relative data, for which data have been
measured for both the healthy and the partially demagnetised motor at different speeds. The duration
of the acquisition period has been reduced according to the speed, to preserve a similar resolution in
the period.
The measurements refer to the same motor, i.e., the first set of voltage measurements were taken
on the motor before the demagnetisation procedure. Then the motor was unmounted, the magnet
substituted and re-mounted to perform the measurements in the faulty state. This way, the inevitable
presence of differences caused by the use of two motors has been avoided.
Energies 2019, 12, 1364 15 of 23
Acquisition
PhaseA
PhaseB
PhaseC
Splitting
PhaseA
PhaseB
PhaseC
Grouping
PhaseA PhaseB PhaseC
Mechanical Turn 1 Mechanical Turn 1 Mechanical Turn 1
Indicators
PhaseA PhaseB PhaseC
Mechanical Turn 1 Mechanical Turn 1 Mechanical Turn 1
ixc ixc ixc
idiff idiff idiff
Figure 13 shows the result of the electrical cycle splitting and corresponding grouping operations.
Each curve represents an electrical turn of the same mechanical revolution and phase, obtained for
the healthy and for the demagnetised motor. By comparing the two graphs, it will be noticed that the
phase curves of the demagnetised motor are much more dissimilar between them. This dissimilarity is
caused by the partial demagnetisation and it will be used to detect the failure. Although this picture
refers to a specific motor speed, the same dissimilarity is also observed (with different magnitude) at
different motor speeds.
Energies 2019, 12, 1364 16 of 23
Healthy - 3000RPM - Mechanical Turn 1, BEMF3[V] Damaged - 3000RPM - Mechanical Turn 1, BEMF3[V]
el_turn1 1.4 el_turn1
el_turn2 el_turn2
1.2 el_turn3
el_turn3 1.2 el_turn4
el_turn4 el_turn5
1.0 el_turn5
el_turn6 1.0 el_turn6
el_turn7 el_turn7
Voltage [V]
0.8
Voltage [V]
0.8
0.6
0.6
0.4 0.4
0.2 0.2
0 250 500 750 1000 1250 1500 1750 0 250 500 750 1000 1250 1500 1750
Time [us] Time [us]
(a) (b)
Figure 13. Superposition of the 7 electrical turns relative to the same mechanical turn, of a healthy and
damaged motor running at 3000 RPM. (a) Healthy motor; (b) Damaged motor.
Figure 14 shows instead the same electrical turn compared over various mechanical turn with
the motor running at different speeds. For example, in this case the electrical turn no.1 of Phase A
and the electrical turn no. 5 of the Phase C have been used, over 4 different mechanical turns with the
motor running respectively at 3000 RPM and 9000 RPM. By comparing the healthy and faulty motor,
no appreciable differences can be found. This suggests that the dissimilarity seen in Figure 13 is not
random, but instead that the flux distribution is repeated for each mechanical turn.
1.2
1.2
1.0 1.0
0.8 0.8
0.6
0.6
0.4
0.4
0.2
0.2
(a) (b)
3.5 3.5
3.0 3.0
2.5 2.5
2.0 2.0
1.5 1.5
1.0 1.0
0.5 0.5
0.0 0.0
0.5 0.5
(c) (d)
Figure 14. Comparison of the same electrical turn voltage over 4 mechanical turns for different speeds
and phases. (a) Phase A—Electrical turn n.1—3000 RPM—healthy motor; (b) Phase A—Electrical turn
n.1—3000 RPM—partially demagnetised motor; (c) Phase C—Electrical turn n.5—9000 RPM—healthy
motor; (d) Phase C—Electrical turn n.5—9000 RPM—partially demagnetised motor.
62 62
60
60
58
58
56
56
54
Damaged Damaged
Healthy Healthy
54 52
0 1 2 3 0 1 2 3
Turn Turn
(a) (b)
62 62
60 60
Damaged
Healthy
58 58
56 56
Damaged
Healthy
54 54
0 1 2 3 0 1 2 3
Turn Turn
(c) (d)
i_xc at 11000RPM
62
60
58
56
Damaged
Healthy
54
0 1 2 3
Turn
(e)
Figure 15. i xc indicator at various speed for healthy and faulty motors. (a) i xc —3000 RPM;
(b) i xc —5000 RPM; (c) i xc —7000 RPM; (d) i xc —9000 RPM; (e) i xc —11,000 RPM.
Energies 2019, 12, 1364 18 of 23
Two important aspects to consider when dealing with fault indicators appear from the graph:
Separation There is a good separation over all the mechanical turns between the indicators corresponding
to the healthy and to the demagnetised motor. In order to show this, in Figure 15, two dashed lines
have been included, one in orange and one in red, representing respectively the minimum ixc value
for all the acquisitions of the healthy motor and the maximum ixc value for all the acquisitions of
the damaged motor. It can be seen that the boundaries are never exceeded for any speed.
Consistency The indicators are consistent over the mechanical turns, i.e., there are no big oscillations
of the indicators. Above all the indicators relative to the healthy motor appear to be extremely
stable, also at different speeds. The maximum measured variation of i xc for the healthy motor has
been 1.11% with reference to the theoretical value of 63, while on average it varies only by 0.38%.
On the other hand, the i xc of the demagnetised motor varies largely according to the speed,
reducing the separation at higher values of angular velocity. This effect can be seen also (with a
reduced intensity) in the healthy motor and is probably due to the higher angular momentum of the
rotor which better compensates for the torque unbalance effects. Anyway this does not represent an
obstacle to the fault detection as the indicator values are still separated as can be seen from Figure 15.
The definition of the maximum theoretical value of the indicator is also an important characteristic
of i xc indicator; indeed it also permits the evaluation the behaviour of the healthy motor.
1.48
1.12
1.46
1.11
1.44
1.10 1.42
1.09 1.40
0 1 2 3 0 1 2 3
Turn Turn
(a) (b)
i_diff at 7000RPM i_diff at 9000RPM
1.7 1.26
1.6 1.25
1.5
1.24
1.4
1.23
1.3
1.22
1.2 Damaged Damaged
Healthy Healthy
0 1 2 3 0 1 2 3
Turn Turn
(c) (d)
Figure 16. Cont.
Energies 2019, 12, 1364 19 of 23
i_diff at 11000RPM
1.25
1.24
1.23
1.22
1.21
Damaged
1.20 Healthy
0 1 2 3
Turn
(e)
Figure 16. idi f f indicator at various speed for healthy and faulty motors. (a) idi f f —3000 RPM;
(b) idi f f —5000 RPM; (c) idi f f —7000 RPM; (d) idi f f —9000 RPM; (e) idi f f —11,000 RPM.
In this case, even if the indicators idi f f of the healthy and demagnetised motor are still separated,
it cannot be said that they present consistency and a good separation. The indicators appear to be more
unstable and they move in a much smaller interval, which makes it difficult to distinguish between
healthy and demagnetised. Also, the indicators values strongly depend on motor speed, with an
erroneous evaluation at 5000 RPM, which does not allow drawing the same thresholds as for the
indicator i xc . Anyway, this indicator can still be useful if used as a confirmation or with some type of
automatic classifier like a NN or an algorithm based on Support Vector Machines (SVMs).
Figure 17 shows the i xc indicator computed during the tests of Table 6. With reference to
Section 4.4.1 it can be observed that:
Separation The separation between the healthy and damaged motor indicators is still present, even if
it is no longer possible to draw a static threshold between the lines
Consistency For the variable speed tests the consistency of the i xc indicator is somewhat lost; indeed
it is also possible to observe non negligible variations of the indicator in the healthy case.
60
55
58
50
56
45 54
52 Damaged
40
Healthy
0 1 2 3 0 1 2 3
Turn Turn
(a) (b)
62 62
60 60
58 Damaged
58 Healthy
56
56
54
Damaged 54
Healthy
52
0 1 2 3 0 1 2 3
Turn Turn
(c) (d)
Figure 17. i xc indicator behaviour during speed variation for healthy and faulty motors.
(a) i xc —3000 RPM–4000 RPM; (b) i xc —5000 RPM–6000 RPM; (c) i xc —7000 RPM–8000 RPM; (d) i xc —
9000 RPM–10,000 RPM.
5. Discussion
A fault detection algorithm based on similarity measurement between the electrical turns
corresponding to the same mechanical turn has been elaborated according to the criteria of Table 1.
By analysing the indicator performance with experimental tests at various speed, the results seem
promising, primarily for what concerns the indicator i xc which presents good consistency over a wide
range of speeds. Further characteristics of the same are given in the next sections.
5.2. Advantages
The method of fault detection by using the proposed indicators has the main advantage of being
straightforwardly applicable with no need for extra hardware. The main technical advantage is the
simplicity, regarding both the theoretical formulation and the algorithm implementation, which leads
to a high execution speed and low computational burden. Its simplicity is also valuable from the
economical point of view, requiring a short implementation time and a few hardware resources, while
providing a valuable failure detection tool. Another important characteristic to be highlighted is that
the only previous knowledge needed of the motor is the number of pole-pairs. Also the intermediate
data are easy to understand as they represent physical variables of the motor in the time domain. Also,
due to to this, no domain transformations for frequency analysis are needed, saving computation time.
The algorithm to compute the indicators is composed of a few simple steps and is fast to
execute. Indeed the execution time for the PC implementation is already very low and an optimised
implementation in a lower level programming language could easily fit in a microcontroller and
be executed at even higher speed, permitting both real time monitoring and punctual testing
during maintenance.
Having a theoretical value for the indicator is also an important advantage, because it permits the
evaluation of a motor without previous knowledge of the same; indeed a healthy motor should have
an i xc value always very close to the maximum computed with the Formula (5).
Finally, although it is true that constant speed is required for a correct analysis, it is necessary for
just one mechanical turn, i.e., for a few milliseconds. For example if the motor is running at 3000 RPM,
a complete turn is executed in 20 ms.
• Experimental tests on BLDC motors with a different number of polepairs, geometry and power
• Tests with different load conditions
• Experimental tests with different types of failures
On this last point, it is necessary to say some more. When considering failures other than
demagnetisation, two possible scenarios appear:
In the first scenario the proposed method would detect and diagnose demagnetisation.
In the second scenario the proposed method would detect different failures but diagnosis must be
performed by further steps or techniques.
Another hint for a future work arose from the study of the state of the art, i.e., the standardisation
of the tests to be executed to assess the performance of the fault detection algorithms. Indeed one
difficulty encountered during the study of the state of the art has been the impossibility of comparing
the performance of the different proposed algorithms, due to the non-uniformity in tests, measured
characteristics and presentation of the results. A possible solution to this issue could be the introduction
of a standardised benchmark and a set of parameters to be presented in order to harmonise the
evaluation of the fault detection algorithms.
Author Contributions: Conceptualization, V.M.F.; Funding acquisition, A.L.R.V. and M.Á.M.P.; Investigation,
V.M.F.; Methodology, V.M.F. and F.B.-Z.; Software, V.M.F.; Supervision, A.L.R.V. and M.Á.M.P.; Validation, V.M.F.,
A.L.R.V. and F.B.-Z.; Writing—original draft, V.M.F.; Writing—review & editing, A.L.R.V., M.Á.M.P. and F.B.-Z.
Funding: This research was funded by Ministerio de Economia y Competitividad (MINECO) with the grant
Doctorados Industriales with reference DI-14-06896.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AI Artificial Intelligence
BLDC Brushless Direct Current
EMA Electro-Mechanical Actuator
EMF ElectroMotive Force
FEM Finite Element Method
HF High Frequency
MCSA Motor Current Signature Analysis
NN Neural Network
PWM Pulse Width Modulation
SLR Systematic Literature Review
SVM Support Vector Machine
TRL Technology Readiness Level
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