ene gies
Re iew
High Technology Readiness Le el Techniques o
B ushless Di ec Cu en Mo o s Failu es De ec ion:
A Sys ema ic Re iew
Vi o Ma io Fico 1,* , Ma ía Ángeles Ma ín P a s 2and Ca melina Ie a di 3
1Skyli e Enginee ing, 41092 Se ille, Spain
2Escuela Técnica Supe io de Ingenie ía, Elec onics Enginee ing Depa men , Uni e sidad de Se illa,
41092 Se ille, Spain; [email p o ec ed]
3Enginee ing Depa men , Uni e sidad Loyola Andalucía, 41704 Se ille, Spain; cie a [email protected]
*Co espondence: [email p o ec ed]
Recei ed: 3 Feb ua y 2020; Accep ed: 18 Ma ch 2020; Published: 1 Ap il 2020
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
Abs ac :
Many pape s ela ed o his opic can be ound in he bibliog aphy; howe e , jus a modes
pe cen age o he in oduced echniques a e de eloped o a Technology Readiness Le el (TRL)
su icien ly high o be implemen able in indus ial applica ions. This pape is ocused p ecisely on
he e iew o his speci ic opic. The in es iga ion on he s a e o he a has been ca ied ou as
a sys ema ic e iew, a e y igo ous and eliable s anda dised scien i ic me hodology, and ies o
collec he a icles which a e close o a possible implemen a ion. This selec ion has been ca e ully
done wi h he de ini ion o a se ies o ules, d awn o ep esen he adequa e le el o eadiness o
aul de ec ion echniques which he a ious a icles p opose.
Keywo ds: ailu e; PMSM; de ec ion; diagnosis; BLDC; b ushless; sys ema ic e iew
1. In oduc ion
The opic o he p esen e iew is subjec ed o a g owing in e es , bo h om he academic and he
indus ial wo lds, due o he pa allel inc ease o he usage o elec ic machines o high eliabili y asks
as mo o ing o elec ic ehicles and ac ua ion o ligh su ace o he u u e Mo e Elec ic Ai c a s.
Abo e all, when ae ospace applica ions a e in ol ed, eliabili y becomes o i al impo ance;
indeed, he pe o mance o ligh ac ua o s on a damaged ai c a is no as impo an as ensu ing ha
he emaining ac ua o s con inue ope a ion un il he ai c a can land sa ely. In mos cases, an adequa e
le el o eliabili y can be eached only by using diagnos ic ools [1].
The a ailabili y o an accu a e and e icien mean o condi ion moni o ing and machine aul
diagnosis can be o pa amoun impo ance, as i imp o es he eliabili y and s abili y o he plan
and a he same ime i educes cos s, ideally leading o a sys em wi hou p og ammed main enance.
S a is ical s udies [
2
] show ha expec ed eliabili y can be imp o ed up o 5–6 pe cen age poin s wi h
he use o moni o ing.
In his con ex a ises he need o p ecisely know he e olu ion and he cu en s a e o he li e a u e
abou he aul de ec ion and diagnosis echniques o B ushless DC (BLDC) and in pa icula which
echniques a e close o a possible implemen a ion, i.e., which echniques ha e he highes Technology
Readiness Le el (TRL).
In he a ea o enginee ing, and especially o ae ospace enginee ing, he na a i e e ision
is usually p e e ed. In his kind o e iew, he au ho s decide which pape s include in he su ey,
based on hei wide knowledge and expe ience and o e ing a pe sonal poin o iew and in e p e a ion
o he chosen heme.
Ene gies 2020,13, 1573; doi:10.3390/en13071573 www.mdpi.com/jou nal/ene gies
Ene gies 2020,13, 1573 2 o 24
The SR is a igo ous s anda dised scien i ic me hodology, used o p oduce eliable li e a u e
e iews, mainly ecognised by i s objec i i y. I is employed wi h excep ional esul s in many
a eas, including bio-science [
3
,
4
], compu e science [
5
] and in ecen yea s pa icula ly in so wa e
enginee ing [
6
,
7
]. In pa icula , hese las pape s and he wo k p oposed by [
8
] ha e been used as
guides o unde ake he sys ema ic e iew in his wo k.
In ac , he i s objec i e o his wo k is p ecisely o adap he guidelines men ioned abo e o
ou ield, while he second objec i e is o apply he sys ema ic e iew o a speci ic opic: high TRL
echniques o BLDC mo o s ailu es de ec ion.
To be mo e p ecise, he au ho s a e in e es ed in hose de ec ion echniques ha a e no es ic ed o
a pa icula machine o wi h special se -ups, con igu a ions, loads o mo o manoeu es. Fu he mo e
he echniques shall ha e been es ed a a ious ope a ion poin and he algo i hm shall ha e es ed
wi h success o a leas one o some cases la e desc ibed in he inclusion c i e ia. Concluding, in o de
o be accep ed in he sys ema ic e iew, he pape shall demons a e ha he p oposed algo i hm is
capable o disce n be ween heal hy and aul y mo o . These cons i u e, mainly, he inclusion c i e ia
o he s udies appea ing in he e iew.
The undamen al scope o his e iew is indeed o de ec which echniques a e p esen ly being
p o i ably used o mo o aul de ec ion and diagnosis and o p o ide he indus y wi h some high
eadiness le el and es ed echniques. In his p ospec , mos o he inclusion and exclusion c i e ia
ha e been de ined o ocus he in es iga ion on hose echniques wi h demons a ed aul de ec ion
pe o mances a a ious ope a ion poin s and easily au oma able o al eady au oma ed.
An addi ional ques ion conside ed in his SR has been he possibili y o embed in he mo o
body, he ha dwa e needed o aul de ec ion. Al hough mos a icles a e ocused on he de ec ion
by u ilising commonly measu ed a iables (mainly speed, cu en , ol age) some au ho s ha e
elabo a ed aul de ec ion echniques based on he analysis o images om ex e nal came as o
sensi i e accele ome e s. Those echniques a e app op ia e o be implemen ed only in pa icula
applica ions [1] and ha e been disca ded om he scope o he p esen wo k.
A e he sc eening o mo e han 3000 possible pape s, only 44 p ima y s udies ha e been ound o
sa is y he a o emen ioned c i e ia. The au ho s ha e ca e ully e ised hose pape s and ha e collec ed
he ollowing da a: he ype o aul de ec ed, he echnique ha was used o he de ec ion and he
senso s used, he inclusion o expe imen s o simula ions, i he echnique has been es ed a di e en
ope a ion poin s (di e se speed o loads o in he bes case a combina ion o bo h), he wo king
condi ion (s a iona y o no ) and some o he limi a ions/ad an ages.
This in o ma ion is hen ga he ed in a ea u e able, which is an use ul s a egy o ge a comple e,
igo ous and objec i e iew o he chosen opic. A he end o he p ocess, he esea ch ques ions
ini ially o mula ed a e answe ed, p o iding a ull pe spec i e o he opic [8].
The SR consis s o h ee sequen ial phases, each o which is subdi ided in u n in o sub-phases,
as de ailed below [7]:
1. Planning he e iew
•iden i ica ion o he need
• esea ch ques ions
• e iew p o ocol
•e alua ing p o ocol
2. Conduc ing he e iew
•selec ion o p ima y s udies
•s udy quali y assessmen
•ex ac ion and syn hesis o da a
3. Repo ing he e iew
•speci ying dissemina ion mechanisms
• o ma ing he main epo
Ene gies 2020,13, 1573 3 o 24
•e alua ing he epo
The es o he pape is o ganised as ollows.. Sec ion 2p esen s he desc ip ion and he adap a ion
o ou case o planning phase o he SR. Conduc ing and epo ing o he sys ema ic e iew a e gi en
in Sec ions 3and 4 espec i ely. Finally, he conclusions a e d awn in Sec ion 5. Addi ionally, in
Appendix Ais gi en a de ini ion o he main e minology used along he documen .
2. Planning
The i s s ep o he sys ema ic e iew consis s in planning, which is he ounda ion o he en i e
e ision. I is a his s age ha he main ools a e de eloped, such as he Boolean unc ion, he inclusion
and exclusion c i e ia, he choice o he di e en da abases in which o ca y ou he esea ch and
abo e all he de elopmen and e alua ion o a p o ocol ha egula es all he phases.
The need o unde ake a sys ema ic e iew, a ises i s o all because he esea ch opic is e y
wide and a igo ous me hod was needed o co ec ly ex ac he needed in o ma ion. As said, in he
enginee ing ield his ype o me hodology is no usual because, e en by being scien i ic and igo ous,
i is di icul and complica ed o ca y ou . Cu en ly he e is no sys ema ic e iew on he aul de ec ion
echniques o b ushless DC mo o s, and in eali y he e is no e en a adi ional e ision so de ailed on
he chosen heme (Should be cla i ied ha his sen ence is e e ed o he e iews aking in o accoun
he eadiness le el o he echnique.).
2.1. Resea ch Ques ions
Once he conc e e opic has been iden i ied, he e a e some c i e ia ha help o clea ly o mula e
esea ch ques ions. Among he mos used c i e ia in o he sec o s he e a e he c i e ia called PICOC
(Popula ion, In e en ion, Compa ison, Ou come, Con ex ). In his wo k we ha e conside ed hose
p esen ed in [8] and adap ed o ou case.
In his case, jus some o hese c i e ia ha e been used o o mula e and p ocess he ques ions ha
his SR is ying o answe . In he inal sec ion o he e iew, de ined as epo ing, he e is a sub-sec ion
called Discussion (Sec ion 4.1) whe e he ela i e answe s a e discussed and analysed.
The ques ions o mula ed o he p esen wo k a e lis ed below:
RQ.1: Which a e he mos common aul s o BLDC mo o s?
RQ.2: Which pa ame e s a e used o aul de ec ion in BLDC mo o s?
RQ.3: Which ype o ailu e can be de ec ed by each echnique?
RQ.4: Which echnique equi es less compu a ional powe ?
RQ.5: Which echnique equi es less senso s?
RQ.6: Which echnique gi es he bes esul s o each ype o ailu e?
2.2. Re iew P o ocol
The e ision p o ocol is no hing mo e han a se o ules and c i e ia o be ollowed du ing all
he s ages, in o de o educe he bias and make he SR as objec i e as possible. In he bioscience ield,
he p o ocol is some imes eco ded in a p ospec i e egis e , such as PROSPERO (h ps://www.c d.
yo k.ac.uk/p ospe o/). Un o una ely, hese ype o egis e s do no exis in he ae ospace ield.
A e y impo an aspec o be conside ed o he SR is he cla i y whe ewi h he p o ocol is
exposed and elabo a ed, as a leas wo pe sons a e in ol ed in he e iew d a ing. A common,
bu e y ime consuming, app oach consis s in he implemen a ion o he SR by wo independen
pe sons, who ca y ou he pa o he conduc ing and epo ing sepa a ely and hen compa e and
discuss he ob ained esul s. Ano he me hod, ha is he one used in his wo k, is ha a pe son
pe o ms all he phases indi idually and a second pe son andomly checks some da a, as o example,
some o he ows o he ea u es ables (Tables 1–5).
Ene gies 2020,13, 1573 4 o 24
Table 1. Fea u es able (a).
Ci e Yea Faul Type Technique Used Senso s Used Expe imen s o
Simula ions
Va ious
Speed/Loads
Wo king
Condi ion
Limi a ions/
Ad an ages
[9] 2019 A ma u e
aul s
Pa ame e s
Es ima ion
Vol age,
Cu en and
Posi ion
senso s
Bo h Bo h S a iona y
condi ions
I p opose indica o s deduced om symme ical componen o
phase cu en s in he e e ence ame. The me hod has been
alida e a a ious speed, loads and sho ci cui esis ance
magni ude o ITSC and a cons an speed, load, esis ance
magni ude o PPSC. The algo i hm compu a ional load is
nos speci ied.
[10] 2019
Pe manen
Magne ic
aul s
Model, AI and
neu al-ne wo k-based
echniques
Vol age and
Cu en
senso s
Bo h
Va ious Speed
S a iona y
condi ions
Expe imen ally es ed wi h 5 mo o condi ions (1 heal hy, 4
aul y) wi h good de ec ion pe o mances. P oposes wo ailu e
ex ac ion me hods and compa es hem. T aining ime and
compu a ional load no speci ied.
[11] 2019 Mechanical
aul s MCSA
Vol age and
Cu en
senso s
Bo h Bo h S a iona y
condi ions
Uses wa ele decomposi ion o he cu en signal and an adap i e
il e o es ima e and emo e he undamen al componen . Tes ed
using wo case s udies, i.e., b oken magne and eccen ici y aul ,
au oma ic aul classi ica ion wi h SVM and a e age accu acy o
96%. T aining ime and compu a ional load no speci ied.
[12] 2019
Pe manen
Magne ic
aul s
O he (Hall E ec
Senso s lux
analysis)
Hall E ec
Senso s Expe imen s
Va ious Speed
Non-S a iona y
condi ions
Me hod capable o de ec ing bea ing and pe manen magne s
aul s by analysing espec i ely he cascade DWT-CWT ans o m
o he speed signal and he ku osis index o he du y cycle signal
o he hall senso ou pu . Elec ically independen om he mo o .
In luence o load no speci ied. Compu a ional load no speci ied.
[1] 2019
Pe manen
Magne ic
aul s
O he (Signals
Simila i y
Analysis)
Vol age and
Cu en
senso s
Bo h
Va ious Speed
S a iona y
Condi ions
The me hod has been es ed wi h FEM simula ions and
expe imen ally wi h good esul s. The es e ec o o que on
he me hod has no benn e alua ed. The me hod can be used only
o mul ipole mo o s.
[13] 2019 A ma u e
aul s
Elec omagne ic
ield moni o ing
Tunneling
Magne o esis i e
senso s
Bo h Bo h S a iona y
Condi ions
The me hod is capable o de ec ing bo h loca ion and se e i y o
in e - u n sho -ci cui by sensing he s ay magne ic ield ou side
he s a o yoke. I needs he ins alla ion o TM senso s a ound he
mo o body. Compu a ional load no speci ied.
[14] 2018 Mechanical
aul s
O he (Angula
Resample)
Vol age and
Cu en
senso s
Expe imen s Bo h
Non-S a iona y
condi ions
Me hod based on he angula esample o speed ob ained wi h
a senso less obse e . Compu a ional load no speci ied. To que
a ia ion no speci ied.
[15] 2018
Pe manen
Magne ic
aul s
O he (Vol age
Angle)
Vol age and
Cu en
senso s
Bo h
Va ious Speed
S a iona y
Condi ions
The me hod akes ad an age om he a ia ions o he
ol age angle obse ed du ing demagne isa ion and in e - u ns
sho aul s o iden i y hei p esence. The me hod is
empe a u e-dependan . A clea de ec ion h eshold is no
de ined. Compu a ional load no speci ied.
Ene gies 2020,13, 1573 5 o 24
Table 2. Fea u es able (b).
Ci e Yea Faul Type Technique Used Senso s used Expe imen s o
Simula ions
Va ious
Speed/Loads
Wo king
Condi ion
Limi a ions/
Ad an ages
[16] 2018 A ma u e aul s
Pa ame e s
Es ima ion
Cu en and
Vol age
senso s
Bo h Bo h S a iona y
condi ion
The me hod is a ec ed by he magni ude o he s a o cu en ,
should be used in cons an o que condi ions.
[17] 2017 Mechanical aul s
Elec omagne ic
ield moni o ing,
sea ch coils, coils
wound a ound
mo o sha s
Sea ch coil Simula ions Va ious
Speeds
S a iona y
Condi ions
The me hod is independen om mo o a iables, bu needs he
sea ch coil o be ins alled on he s a o .
[18] 2017 A ma u e aul s
Model, AI,
and neu al-ne wo k
-based echniques,
Pa ame e s
Es ima ion
Cu en ,
Vol age and
Speed senso s
Expe imen s Bo h S a iona y
Condi ions
This me hod has been es ed expe imen ally on an ae onau ical
mo o , bu he expe imen se -up has no been p esen ed.
The algo i hm is e y as (
≈
15 msec), bu he compu a ion ime
o he ea u es is no aken in o accoun . Also i needs a la ge
da abase o aining he algo i hm.
[19] 2017
Pe manen
Magne ic and
Mechanical aul s
Model, AI,
and neu al
-ne wo k-based
echniques
Cu en
senso Expe imen s
Va ious Loads
S a iona y
Condi ions
Two ailu es in oduced on an expe imen al pla o m and a 10- old
alida ion o he algo i hm is execu ed. The algo i hm is as (30
msec), bu he aining ime is no speci ied.
[20] 2017 A ma u e aul s
Pa ame e s
Es ima ion
Cu en and
Vol age
senso s
Bo h Bo h
Non-S a iona y
Condi ions
The p oposed me hod is jus sligh ly dependen om he load
and speed. The au ho s also demons a ed obus ness agains
pa ame e s a ia ion and inaccu acies by in oducing a h eshold,
bu he allowed ole ance is no speci ied and his quan i y can
also depend on he mo o .
[21] 2017 A ma u e aul s MCSA
Cu en and
Vol age
senso s
Bo h Bo h S a iona y
Condi ions
The p oposed me hod is pa icula ised o in e mi en aul s.
The es se -up is no p esen ed. Tes s a a ious loads and speeds
ha e been execu ed, bu hei in luence on he me hod is no
speci ied.
[22] 2017 Mechanical aul s
O he (Hall E ec
Senso s lux
analysis)
Analogue o
Digi al Hall
senso s
Bo h
Va ious Loads
S a iona y
Condi ions
The me hod is independen om speed and demons a es only a
sligh dependence om loads. The bes accu acy is ob ained wi h
analogue Hall e ec senso which a e no common, e en i he
au ho s p o ide an al e na i e based on digi al Hall e ec senso s.
This me hod can be used only i he Hall senso s a e placed in he
adial di ec ion.
[23] 2017 Pe manen
Magne ic aul s
O he (To que
Ripple Analysis) To que senso Bo h
Va ious Loads
S a iona y
Condi ions
No in o ma ion is gi en abou how he mo o speed a ec s he
p oposed me hod. A o que ansduce is needed o apply he
algo i hm.

Ene gies 2020,13, 1573 6 o 24
Table 3. Fea u es able (c).
Ci e Yea Faul Type Technique Used Senso s Used Expe imen o
Simula ions
Va ious
Speed/Loads
Wo king
Condi ion
Limi a ions/
Ad an ages
[24] 2016 A ma u e
aul s
Pa ame e s
es ima ion
Cu en ,
Vol age and
Speed senso
Bo h Va ious
Speeds
Non-S a iona y
Condi ions
The me hod is based on a modi ied mo o model. I is based on
compu a ions in he o a ing ame (dq). No in o ma ion abou how
he load a ec s he de ec ion me hod is gi en.
[25] 2016 A ma u e
aul s
Pa ame e s
Es ima ion
Cu en and
Speed senso s Expe imen s Va ious
speeds
S a iona y
Condi ions
The p oposed aul index has a e y educed dependence om mo o
speed.
[26] 2016 Mechanical
aul s
MCSA, Model, AI
and NN-based
echniques
Cu en
Senso Expe imen s Va ious
Speeds
S a iona y
Condi ions
The implemen a ion is e y close o a eal scena io, bu in some
s udied condi ions he ail a e o he classi ie is ela i ely high.
[27] 2016
Pe manen
Magne ic
Faul s
Pa ame e s
Es ima ion
Cu en ,
Speed and
Angle Senso s
Bo h Bo h
Non-S a iona y
Condi ions
The me hod needs he knowledge o a ious mo o pa ame e s
and hei a ia ion (o inco ec ness) can esul in poo diagnosis
pe o mances. The au ho s demons a ed good pe o mances wi h
a ious demagne isa ion le els and wo king condi ions.
[28] 2016 A ma u e
aul s
O he (PWM
Ripple Cu en
Measu emen s)
Cu en and
Vol age
senso s
Bo h Bo h S a iona y
Condi ions
The me hod needs an elec ic model o he mo o alid o high
equencies. The au ho demons a ed good sensi i i y also a low
speed.
[29] 2015 Mechanical
aul s
Noise and
Vib a ion
Moni o ing,
Model, AI, NN
based echniques,
MCSA
Cu en
senso and
Accele ome e
Bo h Bo h
Non-S a iona y
Condi ions
The me hod is capable o de ec and dis inguish di e en bea ing
ailu es. Need an accele ome e o be placed close o he bea ing.
[30] 2015 Mechanical
aul s
Model, AI,
NN-based
echniques
Cu en
senso Expe imen s Bo h S a iona y
Condi ions
The au ho s pe o med an ex ensi e expe imen campaign wi h good
esul s. F om he images in he a icle, he damages ep oduced on
he bea ing appea o be conside able.
[31] 2015 A ma u e
aul s
Model, AI and
NN-based
echniques,
MCSA
Cu en
senso Expe imen s Bo h S a iona y
Condi ions
High de ec ion a io. The equency analysis is did wi h he FFT, his
means ha du ing he 12 s o signal acquisi ion he mo o speed and
load shall be cons an .
[32] 2015 A ma u e
aul s
Elec omagne ic
ield moni o ing,
Sea ch Coils,
Coils wound
a ound mo o
sha
Sea ch coil Bo h Va ious
Speeds
S a iona y
Condi ions
The p esen ed me hod is in asi e o an al eady buil mo o .
The de ec ion ime is e y sho (3–5) ms bu i seems be dependen
on mo o speed; u he mo e he ha dwa e used o compu a ions is
no p esen ed.
[33] 2015 A ma u e
aul s
Model, AI and
NN-based
echniques,
MCSA
Cu en
senso s Bo h Bo h S a iona y
Condi ions
The mo o used o expe imen s has an inhe en anomaly, bu he
algo i hm is capable o disce n i om he sho -ci cui . The p esen ed
me hod does no imply any p e ious knowledge on he mo o .
They uses FFT, i implies ha du ing he signal acquisi ion he
condi ions need o be s a iona y.
Ene gies 2020,13, 1573 7 o 24
Table 4. Fea u es able (d).
Ci e Yea Faul Type Technique Used Senso s Used Expe imen o
Simula ions
Va ious
Speed/Loads
Wo king
Condi ion
Limi a ions/
Ad an ages
[34] 2013 Pe manen
Magne s aul s
Model, AI and
NN-based
echniques,
Pa ame e s
Es ima ion
Cu en ,
Vol age and
Speed senso s
Expe imen s Va ious loads S a iona y
Condi ions
P oposes a me hod o demagne isa ion. Compa ison o he
p oposed me hod wi h a ious o he es ablished me hods.
[35] 2013
A ma u e,
Pe manen
Magne s and
Mechanical Faul s
Model, AI,
NN-based
echniques,
MCSA
Cu en
senso s Simula ions Bo h S a iona y
Condi ions
The pape p esen s a good a ie y o mo o s, aul s and wo king
condi ions. The de ec ion accu acy ob ained is e y high, bu i
can be due o he use o clean signals om simula ions.
[36] 2013 A ma u e aul s
Model, AI
and NN-based
echniques
Cu en and
Vol age
Senso s
Bo h Bo h S a iona y
Condi ions
The aining o he AI has been execu ed wi h da a om
bo h expe imen s and simula ions. The me hod uses cu en
measu emen s in ime domain wi h no need o equency domain
ans o ma ion. The me hod is capable o de ec ing aul se e i y
and loca ion.
[37] 2013 A ma u e aul s
Pa ame e s
es ima ion
Cu en ,
Vol age and
Speed senso s
Bo h Va ious loads S a iona y
Condi ions
The me hod compa es an es ima ed back-EMF wi h a e e ence
one o aul de ec ion. The e e ence is ob ained om a FEM
model o om an heal hy machine. This me hod can be e y
sensible o mo o pa ame e s change. The in luence o he load is
no discussed.
[38] 2013 A ma u e aul s MCSA
Cu en ,
Vol age and
Speed senso s
Bo h Bo h
Non-S a iona y
Condi ions
The p oposed me hod has a low compu a ional bu den, bu needs
access o he mo o neu al poin o be applied.
[39] 2011 Mechanical aul s
Model, AI
and NN-based
echniques
Cu en ,
Speed and
To que
senso s,
Simula ions Bo h
Non-S a iona y
Condi ions
The me hod ies o de ec mechanical aul s by es ima ing he
bea ing heal h s a us. A o que senso is used, which is no usually
moun ed in mo o s and he alida ion is ca ied ou by simula ion
wi hou added noise.
[40] 2011 A ma u e aul s
Pa ame e s
es ima ion
Cu en ,
Vol age and
Speed senso s
Bo h Bo h
Non-S a iona y
Condi ions
The back-EMF is es ima ed when he machine is heal hy and hen
ozen, which causes dependence on mo o pa ame e s changes.
The e is a model o compensa e he in e e losses compensa ion.
[41] 2011 A ma u e aul s
O he (High
F equency
Injec ion)
Cu en and
Angula
Posi ion
senso s
Bo h Va ious loads S a iona y
Condi ions
The me hod has a e y good esolu ion, bu he de ec ion is based
on a look-up able. This makes he algo i hm igno e all he ailu es
(i any) p esen be o e he able c ea ion.
[42] 2011 A ma u e aul s MCSA Cu en
senso s Expe imen s Bo h S a iona y
Condi ions
Capable o de ec ing wo ailu es. Use he FFT o he equency
analysis, bu de ec pe iods o s a iona i y o he mo o . Use linea
in e pola ion o de ine he heal hy compa ison e m.
Ene gies 2020,13, 1573 8 o 24
Table 5. Fea u es able (e).
Ci e Yea Faul Type Technique Used Senso s Used Expe imen o
Simula ions
Va ious
Speed/Loads
Wo king
Condi ion
Limi a ions/
Ad an ages
[43] 2011
A ma u e and
Mechanical
aul s
Model, AI
NN-based
echniques
Cu en and
Vol age seno s Expe imen s
Va ious Loads
S a iona y
Condi ions
Pe o m an in e es ing mul i-class classi ica ion based on se en
pa ame e s. The algo i hm does no seem capable o classi y
ailu es no p esen he aining se .
[44] 2010 Mechanical
aul s
Model, AI,
NN-based
echniques,
MCSA
Cu en senso s Bo h
Va ious loads
(Load
independence
demons a ed
analy ically)
S a iona y
Condi ions S udy on he impac o SNR.
[45] 2010 A ma u e
aul s
Model, AI,
NN-based
echniques,
MCSA
Cu en senso s Bo h
Va ious loads
(Load
independence
demons a ed
analy ically)
S a iona y
Condi ions
S udy on he impac o SNR. Two ailu es s udied, wi h aul
se e i y es ima ion.
[46] 2008 A ma u e
aul s
Model, AI
and NN-based
echniques
Cu en , Vol age
and Speed
senso s
Expe imen s
Va ious Loads
Non-S a iona y
Condi ions
T aining o a neu al-ne wo k o p edic cu en and include ini ial
asymme ies. The p edic ed alue o he cu en is used as a
e e ence o de ec ailu es unde load a ia ions.
[47] 2007 Mechanical
aul s MCSA Cu en and
Vol age senso s Bo h Bo h
Non-S a iona y
Condi ions
The expe imen al se -up is no desc ibed. The e is a compa ison
be ween h ee echnique o ime- equency analysis and ela i e
aul de ec ion.
[48] 2007 Mechanical
aul s MCSA Cu en and
Vol age senso s Bo h Bo h
Non-S a iona y
Condi ions
The expe imen al se -up is no desc ibed.
[49] 2007 Mechanical
aul s MCSA Cu en and
Vol age senso s Bo h Va ious
Speeds
Non-S a iona y
Condi ions
The in luence o load is no aken in o accoun . The expe imen al
se -up is no desc ibed.
[50] 2006
A ma u e,
Pe manen
Magne s and
Mechanical
aul s
MCSA Cu en and
Speed senso s Expe imen s Bo h
Non-S a iona y
Condi ions
Me hod o acking he aul equencies du ing a iable speed
ope a ions. The es se -up is no desc ibed. Va ious aul s ha e
been implemen ed.
[51] 2006 Mechanical
aul s MCSA Cu en and
Speed senso s Bo h Bo h
Non-S a iona y
Condi ions
Two me hods p esen ed based on di e en equency acking
algo i hms. Real- ime implemen a ion wi h p ocesso execu ion
ime is also included.
Ene gies 2020,13, 1573 9 o 24
One o he basic s eps o he p o ocol is he c ea ion o a Boolean unc ion ha comp ehensi ely
includes all he e ms ela ed o he chosen heme, including all he synonyms and e ms ha may
be ela ed o he wo ds o in e es o he opic. To ca y ou his esea ch based on keywo ds, i is
app op ia e o deeply ead abou he heme o de ec which wo ds a e mos equen ly used by
he au ho s.
The a icula e Boolean unc ion c ea ed o his wo k is as ollows:
((("b ushless DC" OR "pe manen magne elec ical") AND (mo o OR
machine)) OR BLDC OR PMSM)
AND
(((condi ion OR heal h) AND moni o ing) OR ((diagnosis OR de ec ion)
AND ( aul OR ailu e)))
The i s pa o he Boolean unc ion de ines he ype o mo o , while he second one de ines he
de ec ion o he de ec .
A di icul y encoun e ed du ing he esea ch is ha he di e en bibliog aphic da abases a e no
p epa ed o his kind o e ision, as hey do no allow ce ain esea ches o o sea ch in ce ain ields
o he pape s. Indeed, he Boolean unc ion based esea ch was ca ied ou in he i le, abs ac and
keywo ds o he pape s.
Due o he esea ch es ic ions o he da abases, as speci ied in [
52
], and hanks o he good
co e age o he edi o ials ob ained shown in Table 6, he ollowing da abases ha e been used:
•IEEE Xplo e Digi al Lib a y
•Scopus
•ACM Digi al Lib a y
•Science Di ec
•Web o Science
Table 6.
Da abases co e age wi h espec o he con en o he publishe s: IE = IEEE, IT = IET, PE =
Pegamon-Else ie , ES = Else ie Science, WB = Wiley Blackwell, TF = Taylo & F ancis, SP = Sp inge ,
SI = SIAM Publica ions, OX = Ox o d Uni e si y P ess, KO = Ko ean Ins . Elec ical Eng., SA = Sage
Publica ions, AS = ASME, MP = Mic o ome Publica ions [52].
IE IT PE ES WB TF SP SI OX KO SA AS MP
IEEEX
ACM
Scopus
WoS
SD
Once he esea ch ques ions ha e been iden i ied and he ela i e Boolean unc ion c ea ed, i mus
be in oduced in he di e en bibliog aphic da abases, adap ing i acco ding o he sea ch language
o each da abase. In his wo k, he esea ch has been ca ied ou by sea ching only in he abs ac ,
i le and keywo ds o he pape s, ob aining a o al o 3167 i ems un il o No embe 2019, as de ailed in
Table 7.
Ene gies 2020,13, 1573 16 o 24
Figu e 4is no ep esen a i e o he whole li e a u e, bu i is possible o use i o in es iga e
he p og esses on he opic. Du ing he las yea s, he echniques based on a i icial in elligence,
pa ame e s es ima ion and models, a e being u ilised wi h inc easing equency, o en as classi ie s,
in conjunc ion wi h es ablished me hods like he MCSA. On he o he hand, he numbe o a icles
p esen ing de ec ion echniques based on MCSA has d as ically educed, p obably because hese
echniques ha e been in ensi ely s udied in he pas yea s and he e is less space le o inno a ions.
S a ing om 2016 echniques agged wi h O he , i.e., he echniques no classi iable in he p e iously
de ined ca ego ies, ha e s eadily inc eased in numbe . This indica es ha p e iously unexplo ed
phenomena a e being used o BLDC aul de ec ion and ou lines ha he esea ch on he chosen opic
is in u moil.
Figu e 5shows he o e all dis ibu ion o he pape s acco ding o he used echnique.
The ollowing echniques ha e been omi ed om he g aph because hey ha e no been ound:
• adio- equency emissions moni o ing,
• empe a u e measu emen s,
•in a ed ecogni ion,
•chemical analysis.
6%
2%
2%
40%
32% 6%
12%
Elec omagne ic ield moni o ing
Radio- equency (RF) emissions moni o ing
Noise and ib a ion moni o ing
MCSA
Model, AI, NN-based echniques
Pa ame e s Es ima ion
O he
Figu e 5. Dis ibu ion a icles acco ding o he used echniques
The MCSA is he mos used echnique, ollowed by he AI algo i hms. I is impo an o poin ou
ha equen ly he echniques based on A i icial In elligence a e used as classi ie s o esul s ob ained
wi h o he , al eady es ablished, me hods o ailu e de ec ion. This associa ion demons a ed o be
ha e a g ea impac in imp o ing he de ec ion a e and in ex ending he use o he echnique o a
wide ange o bo h speed and load.
One o he key aspec in he g aph, is he p esence o a good amoun o pape s using echniques
which we e no p e iously classi ied (g ouped unde he ag o he ). Be ween hem i is possible o ind
inno a i e echniques based on High F equency Injec ion [
41
], hall e ec senso s measu emen s [
22
]
o inno a i e mo o signals analysis [1,14,15].
An in e es ing al e na i e o he echniques based on Elec omagne ic ield moni o ing is
ep esen ed by [
13
]. The au ho s use ex e nal senso o sense he s ay magne ic ields ou side
he s a o o de ec a ma u e ailu es se e i y and loca ion, sol ing one o he bigges d awback o his
powe ul echniques ca ego y, i.e., he in asi e p ocedu e o placing addi ional windings inside he
s a o co e.
The nex pa ag aphs a e dedica ed o answe o he p e iously o mula ed esea ch ques ions by
using he selec ed pape s.

Ene gies 2020,13, 1573 17 o 24
4.1.1. Rq.1: Mos Common Failu es o Bldc Mo o s
Figu e 6shows he dis ibu ion o he pape s in ela ion o he ype o ailu e discussed. The esul s
a e in acco dance wi h he ailu e dis ibu ion p esen ed in a ious pape s [
29
,
31
,
36
,
54
], and, in u n,
his means ha he esea ch e o s a e consis en wi h he ailu es occu ence.
A ma u e Faul
48%
Pe manen Magne ic
Faul s (pa ial o
comple e)
21%
Mechanical Faul s
(bea ing ailu e and
eccen ici y)
31%
Figu e 6. Dis ibu ion o he pape s acco ding he ype o ailu e
4.1.2. Rq.2: Pa ame e s Used o Failu e De ec ion in Bldc Mo o s
Due o he in ense esea ch in his ield, many o he mo o pa ame e s ha e been used o aul
de ec ion pu poses. In he ollowing, he a iables used will be lis ed, di iding hem be ween hose
di ec ly measu able and hose es ima ed.
Di ec ly measu able quan i ies
The quan i ies lis ed below a e di ec ly measu able by using speci ic senso s.
Ou pu o que
To que-me e s shall be used o measu e his a iable and i can p o ide e y use ul
in o ma ion. The p oblem esides in he ac ha his ype o senso s a e o en big and expensi e.
Cu en
The cu en is always al eady measu ed by he mo o con olle and he e a e an immense
quan i y o ailu e de ec ion algo i hms based on his a iable.
Vol age The ol age is also commonly measu ed by he mo o con olle .
Vib a ions
By placing accele ome e s on he mo o , i is possible o measu e i s ib a ion le el.
The algo i hms based on ib a ion analysis could p esen p oblems when used in mo ing
sys ems, like ai c a , due o he coupling o ex e nal and unp edic able ib a ions.
Magne ic lux
The magne ic lux gi es a deep insigh on how he mo o is wo king. In o de o
measu e i , i is usually necessa y o include in he mo o winding so called sea ch coils, i.e., some
addi ional windings no connec ed o he phases. The inclusion o hese addi ional coils is no
common and, al hough being a simple p ocedu e, i need o unmoun he mo o , ewound i
and o ex ac om he in e io as many pai s o wi es as many sea ch coils as a e inse ed.
An al e na i e o his p ocedu e is o place ex e nal magne ic senso s on he s a o o sense he
s ay magne ic ields.
Es ima ed quan i ies
The p ocedu es based on pa ame e s es ima ion can iden i y ailu es by e alua ing he changes
wi hin he measu ed mo o pa ame e s as well as e alua ing ac o s which a e no s aigh o wa dly
quan i iable, such as:
Ene gies 2020,13, 1573 18 o 24
•Back-EMF,
•Magne ic lux,
•Winding esis ance,
•Winding induc ance.
Es ima ion could be a e ec i e ins umen which allows he use o a iables s aigh o wa dly
ela ed o he aul and some hing else no measu able. The d awback is ha i depends on models
which can be cons ained o pa icula wo king poin s and a ec ed by he shi o some pa ame e .
4.1.3. Rq.3: Type o Failu es De ec able by Each Technique
Figu e 7 ep esen s he dis ibu ion o he di e en pape s acco ding o he a ious echniques
p oposed o de ec ing di e en ypes o aul s. This allows o e alua e which echniques a e mos
sui able o de ec ing and dis inguishing be ween di e en ypes o ailu e o i some echniques a e
mo e sui able o de ec ing speci ic aul s o can be used as a b oad spec um analysis ool.
The echniques a e widely dis ibu ed among he ypes de ec ion me hods, wi h he excep ion o
ib a ion moni o ing which appea s limi ed o he de ec ion o mechanical aul s; howe e , his speci ic
i em can be biased due o he p esence o only one single pape in he e iew o his ca ego y.
A ma u e Faul A ma u e Faul ,
Mechanical Faul s
(bea ing ailu e and
eccen ici y)
A ma u e Faul ,
Pe manen Magne ic
Faul s (pa ial o
comple e),
Mechanical Faul s
(bea ing ailu e and
eccen ici y)
Mechanical Faul s
(bea ing ailu e and
eccen ici y)
Pe manen Magne ic
Faul s (pa ial o
comple e)
Pe manen Magne ic
Faul s (pa ial o
comple e),
Mechanical Faul s
(bea ing ailu e and
eccen ici y)
Pe manen Magne ic
Faul s (pa ial o
comple e), A ma u e
Faul
0
5
10
15
20
21
2
1
3
3
1
2
1
1
1
5
1
3
1
2
1
2
2
7
1
2
1
Elec omagne ic ield moni o ing
Model, AI, and NN-based echniques
Model, AI, and NN-based echniques, MCSA
Model, AI, and NN-based echniques, Pa ame e s Es ima ion
MCSA
O he
Pa ame e s Es ima ion
Noise and ib a ion moni o ing, Model, AI, and NN-based echniques, MCSA
Figu e 7. Dis ibu ion o he pape s acco ding he ype o ailu e and he used echnique.
4.1.4. Rq.4: Compu a ional Powe Needed o Each Technique
Only a ew pape s ([
18
,
19
,
55
]) o e ed a clea quan i ica ion o he compu a ional powe needed o
implemen he p oposed echnique, and he e o e his ques ion can only be answe ed in a quali a i e
manne . By wha has eme ged i can be seen ha by a heo e ical poin o iew he mos cos ly
s a egies a e hose ocused on models. Tha is because o he necessi y o unning he compu e model
pa allel o he machine i sel when compa ing he ou pu s.
In addi ion, he complexi y inc eases wi h he le el o de ail o he model, he pa ame e s in ol ed,
e c. The ollowing echniques in e ms o compu a ional cos a e hose based on he es ima ion o he
pa ame e and hen hose ha use he NNs. In any case, i mos ly depends on he way he algo i hms
a e implemen ed. The leas expensi e echniques a e he MCSA and o he echniques which di ec ly
analyse senso da a.
4.1.5. Rq.5: Senso s Needed o Each Technique
Gene ally he echniques ha equi e less senso s a e based on cu en o ol age analysis, such
as MCSA. The nea - o ali y o he e iewed me hods mus a leas measu e cu en consump ion and
mo o ol age, al hough his is no an issue as hese quan i ies a e al eady a ailable in mos d i e s.
Ene gies 2020,13, 1573 19 o 24
Many me hods also employ he mo o speed o diagnose he aul . I can be speed can be es ima ed,
om back-EMF measu emen s, o ob ained di ec ly om Hall e ec equency senso s o om a
esol e [1].
4.1.6. Rq.6: Bes De ec ion Resul s
Only some selec ed pape s ([
10
,
11
,
18
,
19
,
26
,
29
–
31
,
35
,
43
,
45
]) p o ide s a is ics on he a e o e o
de ec ion and a e mos ly based on he use o AI. I is e y complex o compa e all hese esul s, since
he es condi ions a e no uni o m.
This is conside ed a weak poin in his opic, which, al hough being e y ich in ideas and
p oposed echniques, lacks alida ion and e i ica ion o he same. A possible solu ion o his p oblem,
would be o p opose a minimum s anda d se o es s o be pe o med in o de o alida e a aul
de ec ion algo i hm and gene a e a se o minimum compa able ou comes [1].
4.2. Gene al Conside a ions Abou The Techniques
In Table 12 he main cha ac e is ics o he de ec ion me hods ha e been g ouped. As p e iously
men ioned, he echniques based on AI a e e y e ec i e. These can be used ei he as s and-alone
aul de ec o s o in combina ion wi h o he echniques o signi ican ly imp o e hei pe o mance in
de ec ion. I mus be no ed, in any case, ha hei success elies on an in ense p ocess o lea ning and
hey ake conside able ime be o e wo king p ope ly [1].
Re e ing o he s a is ics lis ed abo e, in pa icula Figu e 4, i is possible o no ice how aul
de ec ion echniques based on pa ame e es ima ion ha e also seen an inc ease in numbe .
Such echniques can p o ide con inuous access o o he wise unobse able a iables such as
back-EMF o magne ic lux, acili a e he ask o aul de ec ion o iden i y mo e explici ly obse able
aul indica o s. No ewo hy is hei cha ac e is ic o being able o wo king while he mo o is unning
in non-s eady-s a e condi ions o speed and/o o que. On he o he hand, he po en ial p oblem wi h
hese echniques is ha hey a e based on assump ions, models and measu emen s o mo o a iables
whose limi ed alidi y and inaccu acy could hinde aul de ec ion. Acco ding o he esul s o his
esea ch, abou one hi d o he selec ed a icles p opose a echnique able o wo k in non-s a iona y
condi ions and mos o hem a e based on AI, NN and pa ame e s es ima ion.
The need o ope a e he mo o unde s eady-s a e condi ions can be a signi ican limi a ion,
abo e all i his is necessa y o measu e signals o e a long pe iod o ime. Such condi ion may be
achie ed wi h la ge indus ial machines wo king a cons an load bu a ely in ai c a ac ua o s [1].
Table 12. Techniques summa y [1].
Noise and Vib a ion
Moni o ing
Elec omagne ic
Field Moni o ing
Mo o Cu en
Signa u e Analysis
Model and AI based
echniques
Pa ame e s
Es ima ion
Ad an ages
Mos sui able me hod
o de ec ing
mechanical aul s,
as he accele ome e s
can be placed close o
he ib a ion sou ce
Can di ec ly measu e
he elec omagne ic
ield inside he mo o ,
does no need
complica ed
algo i hm o de ec
ailu es, can i ually
de ec all he
mo o ailu es
Does no need
addi ional senso s,
can de ec a la ge
a ie y o ailu es, is
he mos used
echnique
Can be used du ing
non-s a iona y mo o
ope a ion, can be
used in conjunc ion
wi h o he echniques
Can be used du ing
non-s a iona y mo o
ope a ion, can
i ually moni o
e e y
mo o pa ame e
Disad an ages
Need o ins all
accele ome e s on he
mo o , measu emen s
can be co up ed by
en i onmen al
ib a ions, di icul o
use in non-s a iona y
mo o ope a ion
Need o ewind he
s a o and o ex ac
as many addi ional
cables as many
coils inse ed
Need o ans o m
he signal in he
equency domain,
he mo o cu en
depend on he load,
canno be used
du ing
non-s a iona y
mo o ope a ion
Need ex ensi e
aining
The me hod depends
on he knowledge o
a ious mo o
pa ame e s and on
he accu acy o he
model, hei a ia ion
(o inco ec ness) can
esul in poo
diagnosis pe o mance
Ene gies 2020,13, 1573 20 o 24
5. Conclusions
This pape p esen s a sys ema ic e iew o high TRL echniques o BLDC mo o s ailu es
de ec ion, ha ha e been published in he pe iod o ime om he ea ly 1990s o No embe 2019.
In addi ion, he a icle i sel can be conside ed as a p oo o concep o applying he SR o a pa icula
s udy case in he ae ospace ield, in o de o demons a e i s easibili y.
The s udies p esen ed in his wo k, ha e been analysed o espond o he esea ch ques ions
posed, ha is, wha a e he echniques applied o aul de ec ion, he senso s used, he wo king
condi ion, wha a e hei ad an ages and limi a ions. These esul s ha e been included in mul iple
ables o illus a e he indings and ease he consul a ion.
The g ea es di icul y encoun e ed du ing his s udy has been he impossibili y o compa ing
he he di e en p oposed algo i hms in e ms o pe o mance, due o he lack o uni o mi y in es s,
ea u es measu emen and es ima ion and p esen a ion o he esul s. The au ho s would sugges , as a
possible solu ion o his issue, a s udy o in oduce a s anda dised benchma k and a se o pa ame e s
o be p esen ed in o de o ha monise he e alua ion o he aul de ec ion algo i hms.
Au ho Con ibu ions:
Concep ualisa ion, V.M.F.; Funding acquisi ion, M.Á.M.P.; In es iga ion, V.M.F. and
C.I.; Me hodology, V.M.F. and C.I.; Supe ision, M.Á.M.P.; Valida ion, C.I.; W i ing—o iginal d a , V.M.F.;
W i ing— e iew & edi ing, V.M.F. and C.I. All au ho s ha e ead and ag eed o he published e sion o
he manusc ip .
Funding:
The esea ch leading o hese esul s has been pa ly unded by he Eu opean Resea ch Council unde
he Eu opean Union’s Se en h F amewo k P og amme (FP7/2007-2013)/ ERC g an ag eemen n. 785332
Con lic s o In e es : The au ho s decla e no con lic o in e es .
Abb e ia ions
The ollowing abb e ia ions a e used in his manusc ip :
AI A i icial In elligence
BLDC B ushless Di ec Cu en
FEM Fini e Elemen Me hod
HF High F equency
MCSA Mo o Cu en Signa u e Analysis
NN Neu al Ne wo k
SR Sys ema ic Re iew
SVM Suppo Vec o Machine
TRL Technology Readiness Le el
Appendix A Nomencla u e
By going h ough he li e a u e, he e minology in his ield appea s non-uni o m. This is due
o he ac ha aul de ec ion and diagnosis is usually dis ibu ed o e many di e en disciplines.
The de ini ion o he ollowing e ms is speci ied in he glossa y sec ion and is based on [
1
,
56
].
This e minology will be used along he en i e documen .
Faul
: Unpe mi ed de ia ion o a leas one ea u e (cha ac e is ic p ope y) o he sys em ou o he
accep able s anda d condi ion h eshold. The aul is a s a e o he sys em and can be o a ious
ypes (manu ac u ing, assembly, main enance, so wa e, ope a o s, w ong ope a ion). I may no
a ec he co ec unc ioning o he o e all sys em
Failu e
: Pe manen in e up ion o a sys em’s abili y o pe o m a equi ed unc ion unde de e mined
ope a ing condi ions.
Mal unc ion
: In e mi en i egula i y in he ul ilmen o a sys em’s unc ion. I can a ise om one
o mo e aul s.
F om he desc ip ion i is possible o d aw he ela ionship be ween aul s, ailu es and
mal unc ions (Figu e A1).
Ene gies 2020,13, 1573 21 o 24
Fea u e
Time
e
No mal
S a e
Faul
Func ion
Time
1 e
0
Failu e
Func ion
Time
1 e
0
Mal unc ion
Figu e A1. Scheme o he ela ion be ween aul s, ailu es and mal unc ions [1].
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c
2020 by he au ho s. Licensee MDPI, Basel, Swi ze land. This a icle is an open access
a icle dis ibu ed unde he e ms and condi ions o he C ea i e Commons A ibu ion
(CC BY) license (h p://c ea i ecommons.o g/licenses/by/4.0/).