Player is loading...
Interpretation of End-to-end one Dimension Convolutional Neural Network for Fault Diagnosis on a Planetary Gearbox
Sooho Kim, UniGe
Friday, May 3, 2019 · 12:41 p.m. · 14m 38s
Abstract: This research proposes the end-to-end one dimension convolutional neural network for fault diagnosis of a planetary gearbox, and interprets the latent feature spaces with signal and system analysis. A synthetic and experiment vibration data acquired from a planetary gearbox under various health states are used for the demonstration of the proposed method. The interpretation shows that the feature engineering process in CNN is identical with domain-knowledge based diagnosis by concentrating on physical properties of vibration. From this result, it is expected that the usage of CNN on health diagnosis of industrial system acquires its adequacy by explaining that CNN is trained to focus on physics corresponding to previous domain-knowledge.
Embed
Transcriptions
Note: this content has been automatically generated.
00:00:01
well
00:00:06
okay so let's start the last presentation maybe and hope for the lunch
00:00:11
so that today's topic is the interpretation of like the feature spaces for the want to meet in the combustion the network
00:00:17
and i using that by pressing on there for the part timers
00:00:19
things of the calls politically about which is used for the investor system
00:00:24
and these are the contents so first diary briefly introduced about the
00:00:27
what is the topic about and one of the many constant for this
00:00:31
application as a cannery just show you how to interpret
00:00:35
the one dimensional privacy than or lesser i briefly escapee
00:00:39
how to generate the payout were quite they access to the conductor thing and i've ever show you the research
00:00:45
so lester so the thing that i take your party you the for the application in the industry system
00:00:52
any deliver to rotate in power and the failure or currents and eat your costs all
00:00:57
curvy last obviously show an economy cost so the diagnoses for this matter is really important
00:01:03
in this propose are there lots of them what comes to adapt like physical domain work is commodity
00:01:09
for example there are lots of the attempts to using the
00:01:12
high frequency domain or something but it has not that good performance
00:01:17
in that rescind nowadays they are somewhat terms to use the motion morning for these
00:01:23
matter uh yeah there it tears you really good performance but there's no explanation why these are
00:01:30
these showed the really good performance for the by the most days
00:01:33
but the problem is that because it is see related with the decision
00:01:37
making rather replacing the components up the industry system or not as
00:01:42
a really high cost so explaining why this resources happen is really important
00:01:47
in this region i in this thirty i want to suppose
00:01:50
how to explain what the c. j.'s came from the network
00:01:54
and what is the result of that investigation and also for the television i use the two
00:01:59
different system up to date at the first noisy syntactically modelled as a common isn't really experimenter major
00:02:06
so uh let's see the basic idea is really simple cause i really using the one team is under signal or the vibration
00:02:13
cut and in that region hours us interpret this these them at the
00:02:17
entire neural nets up into the signal system analysis which means deprived because domain
00:02:22
suffers there's the commotion network i mean the compilation layer and d. as we
00:02:27
noted compilation layer can be interpreted as the a. t. i. f. fire fitter
00:02:32
which is just the to the the product autocorrelation cross correlation
00:02:37
and using that i think that the train the neural network can
00:02:41
be interpreted in the frequency domain which spend passes converse that we're not
00:02:46
a second there are some consternation about that when they are basically
00:02:49
for the image classification or something we use the mets play a lot
00:02:53
but for this matter we cannot use the max when you may be in polling because there are some random this
00:02:59
for the next building and mean falling there are some random is that which part of
00:03:04
the topic window is choosing is has a random and that and then is i think candle covered the
00:03:11
destruction of the signet essays on the singers so
00:03:15
in then resume i want to use the ovaries indicator
00:03:19
and over the pulling it says the composer with the two different part the movie lovers filter and the dismissive hated
00:03:24
and these two different parties it to the same procedure op
00:03:28
the until i think peter for the house simply and the distillation
00:03:33
and it is strong and here so maybe habit to different hyper parameter for
00:03:38
the movie over to the first one is try site as the commas colour side
00:03:42
and both are related with the uh last in band and the band test lesion
00:03:47
so we have to the allocated to decide what is the common size or
00:03:51
what is this trying side concerning the advice invent region and the convergence reason
00:03:58
and the last one is about the for the connecting the egg so for the for the for the quality
00:04:02
layer on the one dinner system the output up difficult to kill a year into the output mission like this
00:04:08
so basically here the ads which means that the final output op deconvolution that are
00:04:14
and the problem is the just the you know products are all the for the committee later i mean but it's
00:04:19
the y. is tied to the matter to the health status like
00:04:22
the old one rumour and this procedure can be expanded like this
00:04:27
so it yeah these are the kinds up is um changes using
00:04:31
the fury analysis where fourier series or something and basically doe final
00:04:37
because i'm on the finalise show that the fully connected it lay here can
00:04:40
be interpreted on the frequency domain again and they are looks like extracting stuff precursor
00:04:47
information on the specific weights weights
00:04:52
product i mean for example we can transform the
00:04:57
input data i mean the output op the convalescent network
00:05:01
like here into the frequency domain the largest uh we
00:05:05
waited a summation we use the weighty summation using the
00:05:08
wasteful before the committee lay here which is transforming due to t. free essays coefficient
00:05:14
and by someone some a summation op amp we can finally
00:05:19
circa what information easy safety from the for the committee layer
00:05:23
and they saw last i want to first show it b. p. r. called directories or is
00:05:28
simply think that it doesn't matter how that you how much the each comment on each in though
00:05:35
has an impact on making decision so we just the activate every corner and compared
00:05:41
the variation up the taiwan this is per performance and let's show you the data generation
00:05:50
these are the description about the how to be generated by person beat up the particular part
00:05:56
yeah it is set to make and everything are just prefer
00:05:58
skip and these id research topic generation and so basically though
00:06:04
generated to fire is a similar show like this so they're somewhat elation
00:06:08
and in the frequency domain they have some your big place which critic here mister frequency
00:06:14
yeah it's well known that that here is a frequency has a rape
00:06:19
oh i had the good indicator as they had they just for the cure politically about
00:06:25
so as you can see and hear there's three different present the first one is the the where cases a second laces
00:06:31
there's fold on the funk europe apply to get out and the third one is pulled on the politically about twenty here
00:06:37
and we can see that or and the g. m. f. area there are some fluctuation when the
00:06:42
all pro then the forties local and that looks station shape is very different hold on to his status
00:06:48
but the problem is in their your case there i disappeared house and visit and
00:06:52
they see their gaussian noise is just overall that this information on the free custom make
00:06:57
so we can at this thing is whether it is very it is no more
00:07:01
war forth with a real eye by comparing decide spends all around the general area
00:07:06
so this is samuel curse on the experimental data so these are the measurement top experimented a
00:07:14
recipe basically the vibrancy owner also should be clear high place on the g. m. f. area
00:07:19
well with the gaussian loses its just overlap so we cannot distinguish
00:07:24
so we hope that the our proposed to see and then we would discover the information from
00:07:30
the oval that'll easy to extract thing besides spend
00:07:33
information from that europe area there is our expectation
00:07:38
and these are the region that the result first it first of
00:07:42
all this is a a research topic a study about the pulling option
00:07:46
so as i mentioned before the collar side and the strain number and
00:07:50
the other is pulling beside the convergence area and the anti r. i. scenario
00:07:55
so as you can see in here if we increase the collar side then the overlapping
00:07:59
area i mean the converters erase become there were and this show the clear narrow lopez feature
00:08:06
and also if we increase this try size been the allies
00:08:09
in bandy really increase the beach candle clarity of icing problem
00:08:14
and as we can show in here about the performance if really is about other qualified
00:08:19
astride number for example if it is the common size eight reaches the yellow line here
00:08:25
and just try size to which is done till they spend here alas invented here
00:08:30
they and there is no interruption between allies in because the low pass filter is
00:08:34
enough narrower so it can be at s. b. and p. l. s. a. filter
00:08:38
and as a reserve they really show would be the height performance and convolutional network
00:08:44
and also after that we just compared the performance about that i mention is the key are
00:08:53
all along to the corner so there are three highs the purer corners and the last corner
00:08:59
and we can see that the decrease that raises a really very different
00:09:03
so the highest g. p. r. connors impact highly on the making decision and
00:09:11
if early
00:09:14
if we investigate the free can share free concert
00:09:20
uh in the frequency domain or condor then you can see that
00:09:26
the collar flu showed beehive sensitivity i'm you hide p. r. r.
00:09:30
show the clear condor dish or on the band pass filter around each enough area
00:09:35
bert uh but that the kinetic which showed the voiced c. p. r. r. b. f. data conversion see
00:09:42
so as on here we can say that okay then if we train to c. n. n. l.
00:09:48
and they will convert some of them converts into the area around the chimera
00:09:53
and so on our tail to convert it and they are highly impacts on the
00:09:57
making decision so these are the results about the on last season the compilation that yeah
00:10:04
and finally this is about the for the committee layer if i mention before all the way so before the
00:10:09
committee layer can transformed into the frequency domain again and
00:10:13
as we can see and hear these three different uh huh
00:10:17
the way so long to the highest here are colours as
00:10:22
say a little study ways along to the voiced p. r. canal
00:10:27
i'm sure they sure really senior shape on the frequency domain although the pair are so different
00:10:33
so from here we can just the shrink the hour and maybe kansas
00:10:38
estimate our previous occasion into here so from now we don't need to consider about the
00:10:44
the conrail where they're they showed the high high p. r. or loci are
00:10:50
and the result is as follow so overdone rumour cases and doesn't give full employee i think you'll fall
00:10:57
they should be clear saying formation on the frequency domain
00:11:00
along to the fulton will i mean the classifier what
00:11:04
and is equally saver do real time and the engine or tong all the way
00:11:09
so from here we can conclude that the full equality layer
00:11:14
in the frequent for the collectively here to always try to extract the feature information and we keep feature
00:11:21
from the frequency domain along to the baltimore and days show the different shape along to the fall to more
00:11:28
so these are the conclusion of the for the quick earlier and this thing fundamental yes the cost that is
00:11:34
the investigative eon be experimental data so for the these
00:11:40
are the result of the comparison all the p. r.
00:11:43
and these are the rules thing or on the vibration so
00:11:47
these are the no more and this sunday afford at twenty fourth
00:11:52
and the issue to cornell shape on the frequency domain and here as you can
00:11:57
see the canal to show the highs p. r. r. article the converted to degenerate area
00:12:03
but dope connor to show the last p. r. r. also fail to be
00:12:07
converted into d. g. m. f. area in full art as a previous action the
00:12:15
the output stop i mean the frequency responses stop the for the committee layer
00:12:20
also you call the shape whether readable they stop e. p. r. i. so these are
00:12:25
the most as a pure uh this uh the list of lists is the v. p. right
00:12:29
and they show the same frequency response along to the
00:12:33
classifier mold on the near term and the user tumbles
00:12:37
so we can conclude that okay so the first the pulling here
00:12:42
but if we use the order is probably pretty one division nurses them then we can consider the hyper parameter
00:12:48
onto the commerce size and the stress that beaches along to the convergence area in the hour until i sing band
00:12:55
and second the convolution layer and interpretive it in the frequency domain
00:13:00
again maybe shoot that the corner which you should be high stick
00:13:04
p. r. conversion in teaching the fairy exactly but the connor to show the listed
00:13:10
p. r. is fair to convert g. e. or around each interfere yeah
00:13:14
and so are the response of the product conductive layer is converted into d.
00:13:19
the feature extractor along to the same feature remote about along to be
00:13:25
we classify remote and yeah if worse as the the
00:13:30
distinguish the sink to the east feature extractor on the classifier
00:13:36
so the conclusion is that related features basis to you this in a using to to really interpretive using
00:13:42
the constable tuner system on this is and they are equal to the peace corps and all these that
00:13:48
that she information is converted origin if area and a commercially are are sort say
00:13:54
it to converge into the g. m. f. area if the tissue the highest p. r.
00:13:59
and if they shoot a list jar and then they they to convert your and h. m. f. area
00:14:03
and also the past perfectly layer is acting as the
00:14:07
feature extractor we chase different along t. d. s. classifier remote
00:14:12
and yeah the process of or is that they the using the fight the we're cases i mean these
00:14:18
payments are cases and this is the case and balls are show the clearly say reserved for the can present
00:14:26
yeah i think this is the record my presentation and cause i
Conference Program
Methods for Rule and Knowledge Extraction from Deep Neural Networks
Keynote speech: Prof. Pena Carlos Andrés, HEIG-VD
May 3, 2019 · 9:10 a.m.
1445 views
Q&A - Keynote speech: Prof. Pena Carlos Andrés
Keynote speech: Prof. Pena Carlos Andrés, HEIG-VD
May 3, 2019 · 10:08 a.m.
Visualizing and understanding raw speech modeling with convolutional neural networks
Hannah Muckenhirn, Idiap Research Institute
May 3, 2019 · 10:15 a.m.
369 views
Q&A - Hannah Muckenhirn
Hannah Muckenhirn, Idiap Research Institute
May 3, 2019 · 10:28 a.m.
239 views
Concept Measures to Explain Deep Learning Predictions in Medical Imaging
Mara Graziani, HES-SO Valais-Wallis
May 3, 2019 · 10:32 a.m.
457 views
What do neural network saliency maps encode?
Suraj Srinivas, Idiap Research Institute
May 3, 2019 · 10:53 a.m.
719 views
Transparency of rotation-equivariant CNNs via local geometric priors
Dr Vincent Andrearczyk, HES-SO Valais-Wallis
May 3, 2019 · 11:30 a.m.
458 views
Q&A - Dr Vincent Andrearczyk
Dr Vincent Andrearczyk, HES-SO Valais-Wallis
May 3, 2019 · 11:48 a.m.
103 views
Interpretable models of robot motion learned from few demonstrations
Dr Sylvain Calinon, Idiap Research Institute
May 3, 2019 · 11:50 a.m.
194 views
Q&A - Sylvain Calinon
Dr Sylvain Calinon, Idiap Research Institute
May 3, 2019 · 12:06 p.m.
125 views
The HyperBagGraph DataEdron: An Enriched Browsing Experience of Scientific Publication Databa
Xavier Ouvrard, University of Geneva / CERN
May 3, 2019 · 12:08 p.m.
106 views
Improving robustness to build more interpretable classifiers
Seyed Moosavi, Signal Processing Laboratory 4 (LTS4), EPFL
May 3, 2019 · 12:21 p.m.
132 views
Q&A - Seyed Moosavi
Seyed Moosavi, Signal Processing Laboratory 4 (LTS4), EPFL
May 3, 2019 · 12:34 p.m.
Interpretation of End-to-end one Dimension Convolutional Neural Network for Fault Diagnosis on a Planetary Gearbox
Sooho Kim, UniGe
May 3, 2019 · 12:41 p.m.
Recommended talks
Small Data, Number of features vs. number of observations
Olivier Crouzet
Sept. 5, 2019 · 9:47 a.m.
Deep Generative Models
Yoshua Bengio, University of Montreal, Canada
July 5, 2016 · 1:59 p.m.
2156 views