Sensor signals acquired during the manufacturing process contain rich information that can be used to facilitate effective monitoring of operational quality, early detection of system anomalies, and quick diagnosis of fault root causes. This paper develops a method for effective monitoring and diagnosis of multisensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multistream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus, preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The developed method is demonstrated with both simulated and real data from ultrasonic metal welding.

References

References
1.
Woodall
,
W. H.
,
Spitzner
,
D. J.
,
Montgomery
,
D. C.
, and
Gupta
,
S.
,
2004
, “
Using Control Charts to Monitor Process and Product Quality Profiles
,”
J. Qual. Technol.
,
36
(
3
), pp.
309
320
.
2.
Woodall
,
W. H.
,
2007
, “
Current Research on Profile Monitoring
,”
Produção
,
17
(
3
), pp.
420
425
.
3.
Lee
,
S. S.
,
Shao
,
C.
,
Kim
,
T. H.
,
Hu
,
S. J.
,
Kannatey-Asibu
,
E.
,
Cai
,
W. W.
,
Spicer
,
J. P.
,
Wang
,
H.
, and
Abell
,
J. A.
,
2014
, “
Characterization of Ultrasonic Metal Welding by Correlating Online Sensor Signals With Weld Attributes
,”
ASME J. Manuf. Sci. Eng.
,
136
(
5
), p.
051019
.
4.
Lee
,
S. S.
,
Kim
,
T. H.
,
Hu
,
S. J.
,
Cai
,
W. W.
, and
Abell
,
J. A.
,
2010
, “
Joining Technologies for Automotive Lithium-Ion Battery Manufacturing—A Review
,”
Proceedings of ASME 2010 International Manufacturing Science and Engineering Conference
,
Erie, PA
,
Oct. 12–15
, pp.
541
549
.
5.
Kalpakjian
,
S.
, and
Schmid
,
S. R.
,
2008
,
Manufacturing Processes for Engineering Materials
,
Pearson Education
,
Upper Saddle River, NJ
.
6.
Kim
,
T. H.
,
Yum
,
J.
,
Hu
,
S. J.
,
Spicer
,
J. P.
, and
Abell
,
J. A.
,
2011
, “
Process Robustness of Single Lap Ultrasonic Welding of Thin Dissimilar Materials
,”
CIRP Ann. Manuf. Technol.
,
60
(
1
), pp.
17
20
.
7.
Shao
,
C.
,
Kim
,
T. H.
,
Hu
,
S. J.
,
Jin
,
J.
,
Abell
,
J. A.
, and
Spicer
,
J. P.
,
2015
, “
Tool Wear Monitoring for Ultrasonic Metal Welding of Lithium-Ion Batteries
,”
ASME J. Manuf. Sci. Eng.
,
138
(
5
), p.
051005
.
8.
Guo
,
W.
,
Shao
,
C.
,
Kim
,
T. H.
,
Hu
,
S. J.
,
Jin
,
J.
,
Spicer
,
J. P.
, and
Wang
,
H.
,
2016
, “
Online Process Monitoring With Near-Zero Misdetection for Ultrasonic Welding of Lithium-Ion Batteries: An Integration of Univariate and Multivariate Methods
,”
J. Manuf. Syst.
,
38
, pp.
141
150
.
9.
Kuljanic
,
E.
,
Totis
,
G.
, and
Sortino
,
M.
,
2009
, “
Development of an Intelligent Multisensor Chatter Detection System in Milling
,”
Mech. Syst. Signal Process.
,
23
(
5
), pp.
1704
1718
.
10.
Cho
,
S.
,
Binsaeid
,
S.
, and
Asfour
,
S.
,
2010
, “
Design of Multisensor Fusion-Based Tool Condition Monitoring System in End Milling
,”
Int. J. Adv. Manuf. Technol.
,
46
, pp.
681
694
.
11.
Noorossana
,
R.
,
Saghaei
,
A.
, and
Amiri
,
A.
,
2012
,
Statistical Analysis of Profile Monitoring
,
Wiley
,
New York
.
12.
Grasso
,
M.
,
Albertelli
,
P.
, and
Colosimo
,
B. M.
,
2013
, “
An Adaptive SPC Approach for Multi-Sensor Fusion and Monitoring of Time-Varying Processes
,”
Proc. CIRP
,
12
, pp.
61
66
.
13.
Basir
,
O.
, and
Yuan
,
X.
,
2007
, “
Engine Fault Diagnosis Based on Multisensory Information Fusion Using Dempster–Shafer Evidence Theory
,”
Inf. Fusion
,
8
(
4
), pp.
379
386
.
14.
Kim
,
J.
,
Huang
,
Q.
,
Shi
,
J.
, and
Chang
,
T.-S.
,
2006
, “
Online Multichannel Forging Tonnage Monitoring and Fault Pattern Discrimination Using Principal Curve
,”
ASME J. Manuf. Sci. Eng.
,
128
(
4
), pp.
944
950
.
15.
Amiri
,
A.
,
Zou
,
C.
, and
Doroudyan
,
M. H.
,
2013
, “
Monitoring Correlated Profile and Multivariate Quality Characteristics
,”
Qual. Reliab. Eng. Int.
,
30
(
1
), pp.
133
142
.
16.
Chou
,
S. H.
,
Chang
,
S. I.
, and
Tsai
,
T. R.
,
2014
, “
On Monitoring of Multiple Non-Linear Profiles
,”
Int. J. Prod. Res.
,
52
(
11
), pp.
3209
3224
.
17.
Paynabar
,
K.
,
Jin
,
J.
, and
Pacella
,
M.
,
2013
, “
Monitoring and Diagnosis of Multichannel Nonlinear Profile Variations Using Uncorrelated Multilinear Principal Component Analysis
,”
IIE Trans.
,
45
(
11
), pp.
1235
1247
.
18.
Lu
,
H.
,
Plataniotis
,
K. N.
, and
Venetsanopoulos
,
A. N.
,
2009
, “
Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning
,”
IEEE Trans. Neural Netw.
,
20
(
11
), pp.
1820
1836
.
19.
Grasso
,
M.
,
Colosimo
,
B. M.
, and
Pacella
,
M.
,
2014
, “
Profile Monitoring Via Sensor Fusion: The Use of PCA Methods for Multi-Channel Data
,”
Int. J. Prod. Res.
,
52
(
20
), pp.
6110
6135
.
20.
Lu
,
H.
,
Plataniotis
,
K. N.
, and
Venetsanopoulos
,
A. N.
,
2008
, “
MPCA: Multilinear Principal Component Analysis of Tensor Objects
,”
IEEE Trans. Neural Netw.
,
19
(
1
), pp.
18
39
.
21.
Lu
,
H.
,
Plataniotis
,
K. N.
, and
Venetsanopoulos
,
A. N.
,
2009
, “
Uncorrelated Multilinear Discriminant Analysis With Regularization and Aggregation for Tensor Object Recognition
,”
IEEE Trans. Neural Netw.
,
20
(
1
), pp.
103
123
.
22.
De Lathauwer
,
L.
,
De Moor
,
B.
, and
Vandewalle
,
J.
,
2000
, “
A Multilinear Singular Value Decomposition
,”
SIAM J. Matrix Anal. Appl.
,
21
(
4
), pp.
1253
1278
.
23.
Kolda
,
T. G.
, and
Bader
,
B. W.
,
2009
, “
Tensor Decompositions and Applications
,”
SIAM Rev.
,
51
(
3
), pp.
455
500
.
24.
Acar
,
E.
, and
Yener
,
B.
,
2009
, “
Unsupervised Multiway Data Analysis: A Literature Survey
,”
IEEE Trans. Knowl. Data Eng.
,
21
(
1
), pp.
6
20
.
25.
Kiers
,
H. A. L.
,
2000
, “
Towards a Standardized Notation and Terminology in Multiway Analysis
,”
J. Chemom.
,
14
(
3
), pp.
105
122
.
26.
Jin
,
Z.
,
Yang
,
J. Y.
,
Hu
,
Z. S.
, and
Lou
,
Z.
,
2001
, “
Face Recognition Based on the Uncorrelated Discriminant Transformation
,”
Pattern Recognit.
,
34
(
7
), pp.
1405
1416
.
27.
Duda
,
R. O.
,
Hart
,
P. E.
, and
Stork
,
D. G.
,
2012
,
Pattern Classification
,
John Wiley & Sons
,
New York
.
28.
Donoho
,
D. L.
, and
Johnstone
,
I. M.
,
1994
, “
Ideal Spatial Adaptation by Wavelet Shrinkage
,”
Biometrika
,
81
(
3
), pp.
425
455
.
29.
Ye
,
J.
,
2005
, “
Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems
,” ,
6
, pp.
483
502
.
30.
Ye
,
J.
,
Xiong
,
T.
,
Li
,
Q.
,
Janardan
,
R.
,
Bi
,
J.
,
Cherkassky
,
V.
, and
Kambhamettu
,
C.
,
2006
, “
Efficient Model Selection for Regularized Linear Discriminant Analysis
,”
Proceedings of the 15th ACM International Conference on Information and Knowledge Management
,
Arlington, VA
,
Nov. 6–11
, pp.
532
539
.
31.
Ho
,
T. K.
,
1998
, “
The Random Subspace Method for Constructing Decision Forests
,”
IEEE Trans. Pattern Anal. Mach. Intell.
,
20
(
8
), pp.
832
844
.
32.
Hastie
,
T.
,
Tibshirani
,
R.
, and
Friedman
,
J.
,
2008
,
The Elements of Statistical Learning
,
2nd ed.
,
Springer
,
New York
.
33.
BRANSON Ultrasonics Corporation
,
2007
, “
BRANSON, BRANSON Ultraweld® L20
,”
BRANSON Ultrasonics Corporation
.
34.
Hu
,
S. J.
,
2011
,
Technical Report: On-Line Quality Monitoring System for Ultrasonic Battery Tab Welding
,
General Motors Collaborative Research Lab at the University of Michigan
,
Ann Arbor, MI
.
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