senso s
A icle
Anomaly De ec ions o Manu ac u ing
Sys ems Based on Senso Da a—Insigh s in o
Two Challenging Real-Wo ld P oduc ion Se ings
Klaus Kamme e 1,* , Bu kha d Hoppens ed 1, Rüdige P yss 2, S e en S ökle 3,
Johannes Allgaie 4and Man ed Reiche 1
1Ins i u e o Da abases and In o ma ion Sys em, Uni e si y o Ulm, 89081 Ulm, Ge many;
[email p o ec ed] (B.H.); man [email p o ec ed] (M.R.)
2Ins i u e o Clinical Epidemiology and Biome y, Uni e si y o Wü zbu g, 97080 Wü zbu g, Ge many;
uedige [email p o ec ed]
3Uhlmann Pac-Sys eme GmbH & Co. KG, 88471 Laupheim, Ge many; s oekle [email p o ec ed]
4ATR So wa e GmbH, 89231 Neu-Ulm, Ge many; [email p o ec ed]
*Co espondence: klaus.kamme [email p o ec ed]
Recei ed: 9 Oc obe 2019; Accep ed: 2 Decembe 2019; Published: 5 Decembe 2019

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Abs ac :
To build, un, and main ain eliable manu ac u ing machines, he condi ion o hei
componen s has o be con inuously moni o ed. When ollowing a ine-g ained moni o ing o hese
machines, challenges eme ge pe aining o he (1) eeding p ocedu e o la ge amoun s o senso
da a o downs eam p ocessing componen s and he (2) meaning ul analysis o he p oduced da a.
Rega ding he la e aspec , mani old pu poses a e add essed by p ac i ione s and esea che s.
Two analyses o eal-wo ld da ase s ha we e gene a ed in p oduc ion se ings a e discussed in his
pape . Mo e speci ically, he analyses had he goals (1) o de ec senso da a anomalies o u he
analyses o a pha ma packaging scena io and (2) o p edic un a o able empe a u e alues o a 3D
p in ing machine en i onmen . Based on he esul s o he analyses, i will be shown ha a p ope
managemen o machines and hei componen s in indus ial manu ac u ing en i onmen s can be
e icien ly suppo ed by he de ec ion o anomalies. The la e shall help o suppo he echnical
e angelis s o he p oduc ion companies mo e p ope ly.
Keywo ds: anomaly de ec ion; senso da a; machine lea ning; p oduc ion machines
1. In oduc ion
Fo manu ac u ing companies, he managemen o machine ailu es is becoming inc easingly
impo an . Due o he inc easing complexi y o he machines, down imes o any kind can a ec he
o e all success o a company. Buying a spa e sys em is no an al e na i e solu ion as he acquisi ion
cos s no mally su pass he bene i o he spa e sys em. In addi ion, mos eplacemen sys ems also
equi e egula main enance, e en i hey a e no used. Tha is why companies a e looking o new
ways o manage machine ailu es cos -e ec i ely. P edic i e Main enance is one opical subjec , among
o he s, which is a p omising di ec ion o ackle machine ailu es be o e hey ac ually occu . Howe e ,
he selec ion o app op ia e echniques om he ield o P edic i e Main enance is challenging as
nume ous aspec s ha e o be conside ed [
1
]. In addi ion o P edic i e Main enance, he e a e many
o he app oaches in his con ex o coping wi h machine ailu es such as Condi ion Moni o ing o
Con inuous Imp o emen s. Mo eo e , he end owa ds machine lea ning aises he ques ion o
whe he machine ailu es can be easily p edic ed. Al hough echnical de elopmen s ha e imp o ed
he possibili ies o manu ac u ing companies o cope wi h machine b eakdowns, hei p ac ical
applica ion is s ill a challenging ask o many easons. On he basis o hese conside a ions, he
Senso s 2019,19, 5370; doi:10.3390/s19245370 www.mdpi.com/jou nal/senso s
Senso s 2019,19, 5370 2 o 18
wo k a hand p esen s wo eal-wo ld cases ha we e ca ied ou in coope a ion wi h manu ac u ing
companies. Fo hese companies, he de ec ion o sys em e o s is o u mos impo ance. In his
con ex , i was shown ha he de ec ion o anomalies [
2
] o a machine is c ucial o hese companies.
Howe e , he meaning ul de ec ion o such anomalies is e y complex. In e es ingly, so a , many
manu ac u ing companies o en employ a selec ed choice o echnical e angelis s ha a e only able
o de ec anomalies based on hei p ac ical expe iences o e ime. Such expe s, in u n, a e e y
expensi e by design. To elie e hem om manual decisions, his pape elabo a es on how machine
ailu es o hese scena ios can be managed by analyzing senso da a o he p oduc ion machines.
In pa icula , he wo examples will show ha di e en ypes o senso da a, as well as de ec ion
echniques, should be conside ed. The i s p esen ed eal-wo ld se ing is ela ed o pha ma packing
machines. The la e machines w ap able s in o indi idual packaging uni s (i.e., blis e s), and usually
comp ise se e al o he componen s. Fo example, a p oduc loade componen pushes blis e s and
lea le s in o ca ons. This p ocedu e, in u n, is p one o e o s. The e o e, he packaging p ocess needs
o be con inuously moni o ed o educe cos ly down imes as well as o comply wi h ede al egula ions.
The con inuous moni o ing p ocedu e, in u n, gene a es a la ge amoun o senso da a coming om
senso s ha a e ela ed o he se e al componen s o he packaging machine. In addi ion, he pha ma
packing machine can be indi idualized o each cus ome , which migh lead o many senso pa ame e
se ings o he same machine ype, including all obscu ed componen s.
The second p esen ed eal-wo ld se ing is ela ed o 3D p in ing machines in he ield o
op ical p oduc s. In con as o he i s example, in he second se ing, no only senso alues
om he machine i sel a e impo an , bu also senso s ha measu e he en i onmen o he machine.
No e ha his ac dis inguishes his scena io om he i s one. He e, o example, an inc ease in he
en i onmen al empe a u e o humidi y may ha e a signi ican in luence on he p oduc ion p ocess o
he manu ac u ing machine. Anomalies, such as oom empe a u e ha has nega i e e ec s on he
machines, should, he e o e, be a oided.
Rega ding he anomaly de ec ion in gene al, plen y o algo i hms we e p oposed ha add ess
di e en use cases [
3
]. The main objec i e o hese algo i hms is o analyze s eaming da a o de elop
models ha can be used wi h an app op ia e numbe o pa ame e s and ac oss applica ions, i.e., o
machines wi h di e en cha ac e is ics and pu poses. As in mos p ac ical cases, a e y la ge numbe
o senso alues mus be analyzed simul aneously, de ailed knowledge o he indi idual pa ame e s
is a leas mos ly o seconda y impo ance. Fo manu ac u e s, anomaly de ec ion algo i hms wi h
a minimum o pa ame e se ings should be easy o apply and sui able o s eaming analysis [
4
].
Consequen ly, his ype o algo i hms should ea u e ma hema ical ope a ions wi h low compu a ional
complexi y in o de o minimize la ency in long- unning s eaming analyses.
Based on he wo eal-wo ld examples and hei di e en cha ac e is ics o p edic anomalies o
p oduc ion machines, his wo k con ibu es o he ollowing majo insigh s (see also Figu e 1):
•
Fo he pha ma packaging machine scena io, senso s eaming da a was e alua ed wi h he goal
o p edic anomalies o one pa icula machine ype. Mo e p ecisely, machine-in e nal senso s (see
Figu e 1a) a e acqui ed and hei da a is ansmi ed o a senso s da a p ocessing se ice. The la e
hen e alua es he senso da a wi h a dis ance p o iling me hod (see Figu e 1b). Fo his use case,
i is shown ha he equi emen o e icien anomaly de ec ion me hods was ound, which is also
able o cope wi h he huge amoun o senso da a o he analyzed packaging machine. Following
his, he pha ma packaging company can add ess o he machine ypes as well.
•
Fo he 3D machine scena io, en i onmen al senso da a was e alua ed wi h he goal o p edic
anomalies ha may a ec he p oduc ion machines. Mo e p ecisely, empe a u e senso s (see
Figu e 1d) send da a o a message b oke ia he Message Queuing Teleme y T anspo p o ocol
(MQTT) (see Figu e 1c). A machine lea ning se ice (see Figu e 1e) hen p edic s empe a u e
alues ha may ha e nega i e e ec s. I a empe a u e anomaly is de ec ed, he senso da a
p ocessing and he machine lea ning se ices no i y a machine ope a o ia a se ice message
using he message b oke . In his use case, se e al in es iga ed machine lea ning app oaches a e
Senso s 2019,19, 5370 3 o 18
p esen ed, which had he goal o e icien ly de ec anomalies o empe a u e alues. Fu he mo e,
conside a ions on di e en sizes o aining and es da a a e discussed.
•
Fo p oduc ion companies, he de ec ion o anomalies becomes inc easingly impo . As mo e
esea ch is equi ed o ge be e insigh s in o eal-wo ld scena ios and da ase s, his wo ks
con ibu es wi h he esul s o wo complex use cases. The selec ion o app op ia e algo i hms and
hei pa ame e iza ion is s ill challenging, which can be also seen om he ac ha less s anda d
so wa e is o e ed in his con ex .
B oke
Machine
Machine Lea ning
Se ice
s a /s op
Machine
Ope a o
En i onmen al
Senso s
Senso Da a
P ocessing Pipeline
In e nal Senso s
b
e
a
c
d
Machine Da a Use Case
En i onmen al Da a Use Case
P oduc ion En i onmen
ansmi
en i onmen al
senso
da a
subsc ibe
publish ale s
ansmi
machine
senso
da a
no i y
publish machine e en s
Figu e 1. Schema ic o e iew o he p esen ed use cases.
The emainde o his pape is o ganized as ollows. Fi s , ela ed wo ks a e discussed in Sec ion 2.
In Sec ion 3, he me hod o dis ance p o iling is applied o a eal-wo ld da ase o a pha ma packaging
machine, while Sec ion 4shows me hods o p edic anomalies o en i onmen al da a o a 3D machine’s
manu ac u ing oom. Sec ion 5discusses he esul s and deals wi h he e ealed limi a ions. Sec ion 6
concludes he wo k wi h a summa y and ou look.
2. Rela ed Wo k
Rega ding he
acquisi ion o senso da a
in manu ac u ing sys ems, which is an impo an
p e equisi e o his wo k, di e en ela ed wo ks exis . The au ho s o [
5
] discuss equi emen s o
da a acquisi ion o p oduc ion sys ems and in oduce an a chi ec u e based on he Open Pla o m
Communica ions Uni ied A chi ec u e (OPC UA) o da a ansmission and he p ecision ime p o ocol
(PTP) o ime synch oniza ion [
6
]. The au ho s o [
7
], in u n, p o ided an o e iew o me hods,
echnologies, and exchange p o ocols o enable dynamic da a acquisi ion o senso da a in indus ial
sys ems. Challenges ega ding he ep esen a ion and ans o ma ion o senso da a in cybe -physical
sys ems a e p esen ed in [
8
]. In he ield o semiconduc o manu ac u ing, a ious case s udies exis
o imp o e manu ac u ing p ocesses by analysing manu ac u ing senso da a [
9
]. Thus, Ad anced
P ocess Con ol (APC) me hods u ilize con ol s a egies and analyses o iden i y machine aul s and
hei causes [10]. The esul s a e hen used o, o example, op imize main enance schedules [11,12].
In gene al, di e en
anomaly de ec ion echniques
exis , which can be classi ied in o s a is ical
me hods, such as S a is ical P o iling [
13
], Pa ame ic S a is ical Modeling [
14
], and Machine Lea ning
App oaches [15].
Conce ning
use cases o anomaly de ec ion
in indus ial sys ems, he au ho s o [
5
] in oduce
a model-based app oach o he p edic ion o ene gy consump ion in p oduc ion plan s in o de o
de ec anomalies using he ANODA algo i hm [
16
]. [
17
] de ec ed anomalies by applying a Bayesian
ne wo k and sco ing he esul ing ea u es acco ding o a sco ing model. The au ho s o [
18
], in u n,
de eloped an assis ance sys em o da a acquisi ion, p ocess moni o ing, and anomaly de ec ion in
indus ial and ag icul u al p ocesses and e alua ed h ee use cases. Anomaly de ec ion was de eloped
Senso s 2019,19, 5370 4 o 18
indi idually o each use case and is based, o example, also on he dis ance p o iling app oach, wi h
a local ou lie ac o and PCA-based anomaly de ec ion [19,20].
Rega ding
s eam-based anomaly de ec ion
in gene al, di e en app oaches exis . [
21
] conduc ed
anomaly de ec ion based on an au o- eg essi e, da a-d i en model o he conside ed da a s eam.
They analyzed da a s eams o eal-wo ld wind speed measu emen s. The au ho s o [
22
], in u n,
p esen ed an app oach based on hal -space ees, which is a one-class anomaly de ec ion algo i hm.
I s ad an ages wi h espec o he compu a ional complexi y a e cons an ime and memo y oo p in s.
Finally, [
23
] in oduced an anomaly de ec ion me hod based on he Hie a chical Tempo al Memo y
(HTM), which is a s eam-based sequence memo y algo i hm. Fu he mo e, hey in oduced da ase s
con aining eal-wo ld da a s eams wi h labeled anomalies o enable benchma ks o s eam-based
anomaly de ec ion.
Fo
ime se ies p edic ion
, plen y o ela ed wo ks exis . In [
24
], an algo i hm called Ul a Fas
Fo es T ee (UFFT) was in es iga ed ha examines he beha io o an ensemble o eg ession ees
on s eaming da a. The wo k in oduces a hyb id adap i e sys em o he induc ion o andom
o es s om s eaming da a. The UFFT sys em is an inc emen al algo i hm ha poses a cons an ime
complexi y o p ocess each ins ance, wo ks online, and uses he Hoe ding bound o decide when
a spli es is ins alled on a lea leading o a decision node [25]. The algo i hm uses a con inuous da a
s eam and du ing he aining phase, sho - e m memo y is used. This me hod, in u n, is es ic ed
o bina y classi ica ion. Howe e , i can be ex ended o a mul iple classi ie by inc easing he numbe
o classi ie s and building a andom o es o bina y ees. Al hough UFFT is no be e in p edic ion,
i is signi ican ly as e han he C4.5 algo i hm [
26
]. Howe e , UFFT is no able o de ec li le o ab up
concep d i s like [
27
], who in es iga ed d i de ec ion. They p esen an algo i hm, which c ea es
Reg ession T ees (RTs), ins ead o Random Fo es T ees, based on da a s eams in he p esence o
concep d i s. RTs a e as e in lea ning, bu a e p one o ou lie s, as hey do no wo k as an ensemble.
The Fas Inc emen al Reg ession T ee-D i De ec ion (FIRT-DD) algo i hm allows o model adop ion a
any ime and is able o deal wi h local concep d i s and adap s locally. By doing so, global model
adop ion is a oided o gain e iciency. The change de ec ion algo i hm is based on change de ec ion
uni s (CDUs) ha moni o he g owing p ocess. CDUs equi e ew memo y spaces pe node and
a small, cons an amoun o ime complexi y o each sample.
Re . [
27
] de ined concep d i s as a change o he unde lying join p obabili y, i.e., a change
o
P(Y|X)
, and dis inguished be ween h ee main app oaches o concep d i s: (1) me hods ha
explici ly de ec concep d i s, (2) me hods ha use ensembles o decision models, and, (3) me hods
ha a e based on da a managemen using a sliding window (simila o ou p esen ed app oach).
Fas Inc emen al Model T ees wi h D i De ec ions (FIMT-DD), he successo o FIRT-DD, is based
on andomized model ees and combines hem o ensembles, which leads o a andom o es
eg ession [
28
]. Each lea and node o a ee is buil on andomly and independen ly chosen a ibu es.
The au ho s addi ionally c ea ed an Online Reg ession Fo es (ORF), i.e., ou o en FIMT-DD, wi h
a ee dep h o i e. Howe e , he e is no s iking ou pe o mance o one o he p esen ed algo i hms
when compa ing a single online ee, including op ional spli s wi h a andom o es consis ing o en
ees [
29
]. While pe o mance, in gene al, depends on he da ase , empi ical analyses showed ha
a single online op ion ees wi h a e aging i s bes o mos compa ed da ase s.
Al oge he , he combina ion o (1) how anomaly de ec ion me hods ha e been in eg a ed in o
echnical se ings o eal-wo ld examples o p oduc ion machines and (2) applica ion examples o how
anomaly de ec ion o senso da a can be pe o med in a da a-d i en manne , wi hou majo pa ame e
op imiza ions, has no been p esen ed by he discussed o he wo ks so a as done in his pape .
3. Pha ma Packing Use Case—Machine Senso Da a
The ollowing use case p esen s he applica ion o dis ance p o iling o a eal-li e da ase o a pha ma
packaging machine wi h he goal o de ec anomalies o a machine componen . In he i s s ep, ele an
senso da a was collec ed. Then, he ob ained senso da a we e p ocessed by a da a p ocessing pipeline.
Senso s 2019,19, 5370 5 o 18
P ac ical esul s show ha dis ance p o iling can be a aluable me hod o de ec he anomalies o he
pha ma packaging machine componen s.
Use Case Desc ip ion:
Uhlmann Pac-Sys eme GmbH & Co. KG is a mechanical enginee ing
company headqua e ed in Laupheim, Ge many. Uhlmann is a supplie o pha maceu ical w apping
packaging machines. The blis e machines o Uhlmann pu able s in o indi idual packaging uni s.
Fo example, he o e ed machines o m blis e s wi h indi idual cou s o able s om a plas ic o
aluminum oil s and (see Figu e 2). Table s, in u n, a e ed and so ed in o blis e cou s. The la e a e
comple ed wi h a co e shee , while inished blis e s a e inally punched ou by he Uhlmann machines.
Figu e 2. Uhlmann blis e machine B1440i.
As all Uhlmann packaging machines a e used in he pha maceu ical indus y, coun y-speci ic
laws and egula ions mus be conside ed and ul illed. These legal equi emen s a e ela ed o he
alida ion o machines, including he p o ision o de ailed documen a ion o all p ocess s eps ha a e
pe o med du ing d ug packaging. No ably, e e y packaging machine deli e s senso da a, which can
be con inuously moni o ed in o de o de ec anomalies. As an impo an p elimina y echnical s ep
o he de ec ion o anomalies, he acquisi ion o senso da a and he gene a ion o ac ua o signals
mus be p o ided by p og ammable logic con olle s (PLC) [
30
]. Along wi h he example o a p oduc
loade , his p elimina y s ep will be sho ly delinea ed, as i shows he cha ac e iza ion o he esul ing
senso da a.
The p oduc loade s a ion o an Uhlmann blis e machine loads blis e s and lea le s in o a ca on.
The p oduc loade consis s o se e al senso s and se omo o s. Speci ically, wo unc ional assemblies
wi h se omo o s a e unning in pa allel, while he senso s o each single se omo o gene a e ou
signals ha ep esen he
1. mechanical posi ion (MP) o he assembly (P oduc Loade .Posi ion),
2. he di e ence o a e e ence alue and he ac ual alue o he MP (P oduc Loade .D e ),
3. he powe consump ion o he assembly (P oduc Loade .Cu en ),
4. and he e e ence signal o he assembly (P oduc Loade .Re e ence).
O e all, eigh signals pe p oduc loade can be acqui ed. The physical p ocess o p oduc loading
and p oduc eleasing is execu ed in con inuous cycles. No e ha he packaging pe o mance o
a machine is he e o e exp essed in cycles pe minu e. Du ing hese cycles, di e en anomalies may
occu . A machine componen can lock, which leads o a p oduc ion hal . This can be caused, in u n,
by a aul y eeding o he packaging box. Fu he mo e, wea and ea o he ball bea ings can lead
o inc eased ic ional esis ance o a comple e ailu e o he ball bea ing lub ica ion. Bo h anomalies
a e epo ed by he echnical e angelis s o be de ec able by analyzing he powe consump ion o he
p oduc loade .
Fundamen als:
To de ec anomalies in ime se ies da a, dis ance p o iling can be applied [
31
].
A dis ance p o ile, in u n, is a ec o
D
o he Euclidean dis ances be ween a gi en ime se ies pa e n
pand e e y possible subsequence siin he espec i e ime se ies s(see Figu e 3).

Senso s 2019,19, 5370 6 o 18
Pa e n p
Subsequence
nm
Dis ance
P o ile D=
Time Se ies s
dp,1 dp,2 dp,3 dp,n-m+1
sn-m+1
...
Figu e 3. Dis ance p o ile o a signal.
In his pape , Mueen’s Algo i hm o Simila i y Sea ch (MASS) was used o calcula e he dis ance
p o ile [
32
]. When calcula ing he dis ance p o ile, he esul ing ime complexi y is
O(nm)
. MASS uses
a con olu ion-based me hod o calcula e he dis ance p o ile o a pa e n in
O(n×log n)
. Fu he mo e,
a z-no maliza ion is applied o he gene a ed dis ance p o ile ec o du ing he calcula ion as well.
I he Euclidean dis ance be ween a pa e n
p
and a subsequence
si
is smalle han he alue o
a h eshold, hen
p
and
si
a e conside ed o be simila .
Senso Da a Acquisi ion:
When p ocessing
senso da a o he pha ma packing machines, di e en s eps ha e o be pe o med [
33
]. Fi s , senso
da a has o be collec ed om a PLC and ans e ed o a collec ion componen . We de eloped a bina y
ansmission p o ocol o ans e senso da a di ec ly om he PLC o a collec ion componen ia TCP
socke s. The p o ocol o e s di e en ame ypes o ime synch oniza ion, signal me ada a desc ip ion,
and senso da a poin ansmission.
Senso Da a P ocessing:
The ans e ed aw da a s eam has o be spli in o windows and
p e-p ocessed i i ea u es dis u bing noise. A e he p e-p ocessing s ep, he da a is p ocessed h ough
as -Fou ie ans o ma ions and inally s o ed i no u he p ocessing s eps a e equi ed. In his
s ep, we adjus ed he al eady exis ing echnical de elopmen o enable he in eg a ion o anomaly
de ec ion app oaches a e he p e-p ocessing s ep. To enable his, he u ilized p ocessing pipeline mus
p o ide a high a iabili y o e e y senso signal, which means ha he pipeline is gene ally di icul
o manage and con igu e. To add ess hese issues, we ha e de eloped a Senso Da a P ocessing (SDP)
amewo k in C# o Mic oso . NET o collec ing, p ocessing, s o ing, and isualizing aw senso da a
in a con inuous p ocessing chain, acco ding o he da a s eam p ocessing model [
34
]. The SDP de ines
a g aph-based p ocessing model, in which p ocessing nodes a e connec ed o each o he o handle all
a o emen ioned and equi ed pipeline s eps in a con olled way (see Figu e 4).
Figu e 4. Schema o he senso da a p ocessing pipeline.
Senso Da a Analysis:
The da ase used in his wo k was gene a ed by an Uhlmann endu ance
es a angemen in he Uhlmann echnical cen e in Laupheim, Ge many. To be mo e speci ic,
an isola ed p oduc loade s a ion was unning in a con inuous ope a ion mode. Fo ou p ac ical
e alua ion, senso da a we e collec ed o e 10 days by he SDP and s o ed in a MongoDB da abase.
In o al, 1,127,790 imes amps pe signal (i.e., ime synch oniza ion ames) and 18,736,022,320 da a
poin s (i.e., loa ing-poin numbe s ep esen ing he measu ed senso da a) we e eco ded du ing he
men ioned ime pe iod, including NULL alues o he signal gaps. In e ms o disk space, he eco ded
alues co espond o oughly 22 GB. Fu he no e ha he da ase comp ises a ew signal gaps due o
connec i i y losses be ween he PLC and he s o age componen . Impo an ly, he da ase con ained
eco ds o bea ing damage, which e en ually led o he ailu e o he machine. Apa om ha ,
he da ase can be conside ed as being heal hy, as he endu ance es and an e alua ion o he log
Senso s 2019,19, 5370 7 o 18
da a gene a ed by he es execu ion sys em showed no o he abno mali ies. Figu e 5shows he
mechanical posi ion and powe consump ion alues o i e a bi a y p oduc loade cycles (
x1
o
x5
) o
he conside ed da ase . Du ing a cycle, he mechanical posi ion alues inc ease mono onously, while
a p oduc is loaded in o a ca on (x-axis). The powe consump ion (y-axis) shows posi i e alues i he
p oduc loade is inc easing in i s speed, while nega i e alues show b eaking ac ions. When a new
cycle s a s, he posi ion is se back o ze o.
Figu e 5. Powe consump ion (blue) and e e ence signal (o ange) o an assembly.
Dis ance P o iling:
Fo his p ac ical use case, he SDP p ocessing pipeline was adjus ed by
changing he ecei e node o ecei e BMTT da a, a windowing node (see below), and a p ocessing node
implemen ing he dis ance p o iling based on he MASS, and an ou pu node o publish MASS esul s
o a b oke (see Figu es 1c and 4). A p e-p ocessing node is no necessa y as he MASS p ocessing node
calcula es z-no maliza ion and dis ance p o iles simul aneously. As he SDP pipeline p ocesses da a
s eams, windowing had o be applied [
34
]. The e o e, co ela ion windows a e used. The la e a e
a specializa ion o session windows [
35
]. In con as o session windows, co ela ion windows a e
igge ed by one e en a he beginning o a new window. In he SDP, co ela ion windows may be
also gene a ed based on o he signals. He e, a window igge is execu ed based on he mas e encode ,
ep esen ed by a saw- oo h signal (see Figu e 5). Tha means, he egula clocking o he mas e encode
(abou
720°
) was used o di ide he ela ed signals in o uni o m windows by de ec ing i s alling slope
(da aF ame.posi ion[i+1] - da aF ame.posi ion[i])
<−
500). The MASS algo i hm was hen applied o
he da a o he powe consump ion signal o he p oduc loade , as i o e s he mos exp essi eness
o he mechanical p ocess and ea u es a e y high esolu ion, i.e., one da a poin pe 2 milliseconds.
In con as , he mechanical posi ion o e s only abou 1000 di e en , mono onously inc easing alues
(see Figu e 5). Mo eo e , a compa ison be ween he indi idual pa e ns becomes possible. The e o e,
a new coun e alue is c ea ed o each ecognized new pa e n o a window. In o de o classi y a new
pa e n, he Euclidean Dis ance is used o de e mine he signals o exis ing pa e ns wi h he signals o
new pa e ns. In p ac ice, he coun e alue inc eases apidly a he beginning o an analysis se ies, as
well as in he ange o any anomaly and d ops below a ce ain limi he ea e . No e ha he numbe o
pa e ns caused by de ec ed anomalies a e g adually conside ed “no mal” by MASS, and a e he e o e
smoo hed. Con e sely, his means ha a e a la ge numbe o de ec ed pa e ns, i is no assumed ha
he anomaly has been disappea ed.
Resul s:
On he gi en 10-days da ase , he applica ion o he MASS wi h he de eloped SDP
p ocessing node ook a o al un ime o 60 min. As a esul , he MASS de ec ed ewe han 5 pa e ns pe
Senso s 2019,19, 5370 8 o 18
da a poin analysis a e 22 h in he da ase , i.e., he baseline was eached. In e es ingly, abou 14 h p io
o bea ing damage, he numbe o de ec ed pa e ns inc eased apidly (see Figu e 6).
A 10:00 a.m.,
he numbe o de ec ed pa e ns e u ned o 1 pa e n pe sample. A 01:30 p.m., he numbe o
de ec ed pa e ns inc eased again o 7, whe eas a 01:48 p.m., he machine inally s opped wo king
due o bea ing damage. Figu e 7a shows window-laye ed plo s o he powe consump ion in he
no mal condi ion ( ew de ec ed pa e ns), whe eas Figu e 7b in bad condi ion (many de ec ed pa e ns).
The x-axis ep esen s he e e ence signal o he p oduc loade in deg ee, while he y-axis ep esen s
he ac ual powe consump ion in milliampe e (mA) o he wo assemblies. In Figu e 7b, powe
consump ion shows a highe a iance. In e es ingly, he idle phase shows he highes a iance o
powe consump ion. One eason o his may be he con ol loop used as du ing slow mo emen s, he
ac ual posi ion is app oached o he e e ence posi ion wi h smalle speed changes,. A highe a iance
also means a lowe mo o p ecision o he mo emen s in he no mal s a e.
Figu e 6.
Numbe o de ec ed pa e ns (y-Axis) and ime (x-Axis) o a p oduc loade o he las
14 h collec ed.
Figu e 7.
Phase olded plo o (
a
) no mal si ua ion wi h less de ec ed pa e ns and (
b
) a de ec ed
anomaly wi h many pa e ns.
Summa y:
The da ase o he p oduc loade componen o a pha ma packaging machine was
analyzed o de ec anomalies in he collec ed da ase . The selec ion o a p ope anomaly de ec ion
algo i hm is challenging as many a ian s o a machine exis and he selec ion o pa ame e s o
Senso s 2019,19, 5370 9 o 18
each machine equi es high e o s. The e o e, he de elopmen o a pa ame e less algo i hm ha i s
in o he se ing o he pha ma packaging machine should be a majo goal. In his pape , a dis ance
p o ile anomaly de ec ion algo i hm was p esen ed and applied. Speci ically, he dis ance p o ile was
calcula ed wi h he MASS based on he z-Euclidean Dis ance. The la e o e s a ious ad an ages,
e.g., i equi es ew pa ame e s. Fo he senso da a acquisi ion, he de eloped senso da a p ocessing
amewo k (SDP) was used. The la e is based on a p ocessing pipeline model ha is con igu able
and ex ensible, i.e., o he anomaly de ec ion algo i hms can be lexibly in eg a ed. Al hough i is
a a he simple me hod, MASS showed p omising esul s on he p esen ed eal-wo ld da ase . I was
possible o de ec anomalies in a meaning ul manne , i.e., based on he numbe o de ec ed pa e ns,
13 h as well as 18 min be o e he damage. P ac ically, his means ha a echnical e angelis o he
pha ma packaging company can be e analyze he da a o decide whe he bea ing damage can be
a oided by s opping he machine o eplacing componen s be o e damage ac ually occu s. Howe e ,
i has also been shown ha he in eg a ion o he anomaly de ec ion me hod equi es conside able
echnical e o s.
4. 3D P in ing Machine—Tempe a u e En i onmen Da a
The ollowing use case p esen s he applica ion o p edic ion models o a eal-li e da ase o
a measu ing oom o 3D p in ing machines wi h he goal o p edic anomalies o empe a u e alues.
In he i s s ep, ele an senso da a we e collec ed. Then, he ob ained senso da a a e p ocessed and
p omising ea u es a e selec ed. Finally, machine lea ning models p edic upcoming alues and wa n
echnical machine ope a o s abou possible anomalies.
Use Case Desc ip ion:
An indus ial company in he ield o 3D p oduc ion machines ope a es
a measu ing oom equipped wi h nine empe a u e senso s and addi ional senso s o ai humidi y
(%), ai p essu e (mBa ), and ai low (m/s). The oom con ains an a bi a y numbe o machines and
hei ope a o s en e and lea e he oom a a bi a y poin s in ime. Machines wi hin he oom a e
allowed o ope a e wi hin a empe a u e h eshold, which is de ined indi idually o each machine.
A empe a u e con ol uni ies o keep he empe a u e in he oom be ween he uppe and lowe
h eshold. The challenge o his p ojec is o igu e ou empe a u e anomalies o he measu ing
oom ha is solely based on he p o ided en i onmen al da a and does no con ain any con ex ual
in o ma ion o he ope a ed machines, such as he numbe o ope a o s o he ac ual numbe o
he machines in he oom. Fu he mo e, no s anda ds o accep able e o s we e se o he p ojec .
The models p esen ed below e u n a p edic ion e o on a basis o which he echnical e angelis s
can decide a e a no i ica ion whe he he e o a e is accep able o no .
Senso Da a Acquisi ion:
The measu ing oom is sending he cu en senso in o ma ion wi h
a ansmission a e o one alue pe 3 min. Figu e 8illus a es he used a chi ec u e. He eby, all alues
a e combined using he Ja aSc ip Objec No a ion (JSON). Nex , all sen messages a e ans e ed ia
he Message Queuing Teleme y T anspo p o ocol (MQTT), which is a publish/subsc ibe based message
p o ocol o machine- o-machine (M2M) communica ion. The machine lea ning code is implemen ed
as a sepa a e se ice using he sciki -lea n py hon lib a y [
36
]. The sciki se ice subsc ibes o he
en i onmen al alues and uses hem o ain a machine lea ning model. The model is cons an ly
e alua ed and, whene e he p edic ed empe a u e alue exceeds a de ined h eshold, he esponsible
machine ope a o s (i.e., he echnical e angelis s) a e ala med. In con as o he i s use case, he e, he
in eg a ion o he anomaly de ec ion me hod is echnically easie . Howe e , he selec ion o p ope
algo i hms is mo e challenging han o he i s use case.
Senso s 2019,19, 5370 16 o 18
min_samples_spli =2,
min_weigh _ ac ion_lea =0.0 ,
p eso =False ,
andom_s a e=0,
s p l i e = ’ bes ’ )
MLPReg esso ( a c i a ion = ’ elu ’ ,
alpha =0.0001 ,
ba ch_size= ’ au o ’ ,
be a_1 =0.9 ,
be a_2 =0.999 ,
ea ly_s opping=False ,
ep si lon =1e −08,
hidden_laye _sizes =(100 ,) ,
lea ning_ a e= ’ cons an ’ ,
l e a n i n g _ a e _ i n i =0. 001 ,
max_i e =200 ,
momen um=0.9 ,
n_i e _no_change =10 ,
nes e o s_momen um=T ue ,
powe _ =0.5 ,
andom_s a e=None ,
sh u l e=T ue ,
sol e = ’ l b gs ’ ,
o l = 0.0001 ,
a li d a i o n _ ac i o n =0.1 ,
e bose=T ue ,
wa m_s a =False )
RandomFo es Reg esso ( boo s ap=T ue ,
c i e i o n = ’mse ’ ,
max_dep h=10 ,
max_ ea u es= ’ au o ’ ,
max_lea _nodes=None ,
min_impu i y_dec ease =0.0 ,
min_impu i y_spli =None,
min_samples_lea =1 ,
min_samples_spli =2,
min_weigh _ ac ion_lea =0.0 ,
n_es ima o s =1000 ,
n_jobs=−1,
oob_sco e=False ,
andom_s a e=1,
e bose =0 ,
wa m_s a =False )
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2019 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/).