Abstract

An effective maintenance strategy to cut back maintenance costs and production loss with assured product quality has always been a major concern for industries. The Industry 4.0 era has built a wide acceptance for the predictive maintenance techniques in the remaining useful life (RUL) estimation of critical industrial systems. In this paper, long short-term memory (LSTM) and bidirectional-LSTM (bi-LSTM) deep neural architecture-based predictive algorithms are proposed for the RUL estimation of the lathe spindle unit. The deep learning algorithm is embedded within a Bayesian optimization algorithm for the self-optimization of its network structure and hyperparameters. The proposed deep learning algorithm is trained using lathe spindle health degradation data collected from an experimental accelerated run-to-failure test rig to evolve an RUL prediction model. The vibration signals representing lathe spindle health degradation from the health to faulty state are analyzed to extract time, frequency, and time-frequency domain features, which are then subjected to a neighborhood component analysis (NCA) based feature selection criteria. Finally, the selected relevant features are used to train the optimized LSTM/bi-LSTM network for RUL estimation. A comparison of the prediction results for Bayesian optimized LSTM/bi-LSTM network architectures and other prominent data-driven approaches are performed. The Bayesian optimized LSTM + bi-LSTM deep network architecture is observed to have the highest prediction accuracy for lathe spindle RUL estimation.

References

1.
Lee
,
G. Y.
,
Kim
,
M.
,
Quan
,
Y. J.
,
Kim
,
M. S.
,
Kim
,
T. J. Y.
,
Yoon
,
H. S.
,
Min
,
S.
, et al
,
2018
, “
Machine Health Management in Smart Factory: A Review
,”
J. Mech. Sci. Technol.
,
32
(
3
), pp.
987
1009
.
2.
Yao
,
X.
,
Zhou
,
J.
,
Lin
,
Y.
,
Li
,
Y.
,
Yu
,
H.
, and
Liu
,
Y.
,
2019
, “
Smart Manufacturing Based on Cyber-Physical Systems and Beyond
,”
J. Intell. Manuf.
,
30
(
8
), pp.
2805
2817
.
3.
Liu
,
C.
,
Vengayil
,
H.
,
Zhong
,
R. Y.
, and
Xu
,
X.
,
2018
, “
A Systematic Development Method for Cyber-Physical Machine Tools
,”
J. Manuf. Syst.
,
48
(
Part C
), pp.
13
24
. .
Special Issue
.
4.
Laloix
,
T.
,
Iung
,
B.
,
Voisin
,
A.
, and
Romagne
,
E.
,
2019
, “
Parameter Identification of Health Indicator Aggregation for Decision-Making in Predictive Maintenance: Application to Machine Tool
,”
CIRP Ann.
,
68
(
1
), pp.
483
486
.
5.
Guo
,
J.
,
Yang
,
Z.
,
Chen
,
C.
,
Luo
,
W.
, and
Hu
,
W.
,
2021
, “
Real-Time Prediction of Remaining Useful Life and Preventive Maintenance Strategy Based on Digital Twin
,”
ASME J. Comput. Inf. Sci. Eng.
,
21
(
3
), p.
031003
.
6.
Liu
,
C.
,
Vengayil
,
H.
,
Lu
,
Y.
, and
Xu
,
X.
,
2019
, “
A Cyber-Physical Machine Tools Platform Using OPC UA and MTConnect
,”
J. Manuf. Syst.
,
51
, pp.
61
74
.
7.
Feng
,
S. C.
,
Bernstein
,
W. Z.
,
Hedberg
T.
, Jr.
, and
Feeney
,
A. B.
,
2017
, “
Toward Knowledge Management for Smart Manufacturing
,”
ASME J. Comput. Inf. Sci. Eng.
,
17
(
3
), p.
031016
.
8.
Gopalakrishnan
,
M.
, and
Skoogh
,
A.
,
2018
, “
Machine Criticality Based Maintenance Prioritization Identifying Productivity Improvement Potential
,”
Int. J. Product. Perform. Manag.
,
67
(
4
), pp.
654
672
.
9.
Thoppil
,
N. M.
,
Vasu
,
V.
, and
Rao
,
C. S. P.
,
2020
, “
On the Criticality Analysis of Computer Numerical Control Lathe Subsystems for Predictive Maintenance
,”
Arab. J Sci. Eng.
,
45
(
7
), pp.
5259
5271
.
10.
Xu
,
G.
,
Liu
,
M.
,
Wang
,
J.
,
Ma
,
Y.
,
Wang
,
J.
, and
Shen
,
W.
,
2019
, “
Data-Driven Fault Diagnostics and Prognostics for Predictive Maintenance: A Brief Overview
,”
2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
,
Vancouver, BC, Canada
,
Aug. 22–26
.
11.
Zhang
,
W.
,
Yang
,
D.
, and
Wang
,
H.
,
2019
, “
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey
,”
IEEE Syst. J.
,
13
(
3
), pp.
2213
2227
.
12.
Liao
,
L.
, and
Köttig
,
F.
,
2016
, “
A Hybrid Framework Combining Data-Driven and Model-Based Methods for System Remaining Useful Life Prediction
,”
Appl. Soft Comput. J.
,
44
, pp.
191
199
.
13.
Wu
,
J.
,
Hu
,
K.
,
Cheng
,
Y.
,
Zhu
,
H.
,
Shao
,
X.
, and
Wang
,
Y.
,
2020
, “
Data-Driven Remaining Useful Life Prediction via Multiple Sensor Signals and Deep Long Short-Term Memory Neural Network
,”
ISA Trans.
,
97
, pp.
241
250
.
14.
Si
,
X.
,
Wang
,
W.
,
Hu
,
C.
, and
Zhou
,
D.
,
2011
, “
Remaining Useful Life Estimation—A Review on the Statistical Data Driven Approaches
,”
Eur. J. Oper. Res.
,
213
(
1
), pp.
1
14
.
15.
Wang
,
J.
,
Ma
,
Y.
,
Zhang
,
L.
,
Gao
,
R. X.
, and
Wu
,
D.
,
2018
, “
Deep Learning for Smart Manufacturing: Methods and Applications
,”
J. Manuf. Syst.
,
48
(Part C), pp.
144
156
.
16.
Carvalho
,
T. P.
,
Soares
,
F. A. A. M. N.
,
Vita
,
R.
,
Francisco
,
R. d. P.
,
Basto
,
J. P.
, and
Alcalá
,
S. G. S.
,
2019
, “
A Systematic Literature Review of Machine Learning Methods Applied to Predictive Maintenance
,”
Comput. Ind. Eng.
,
137
, pp.
106024
.
17.
Thoppil
,
N. M.
,
Vasu
,
V.
, and
Rao
,
C. S. P.
,
2021
, “
Deep Learning Algorithms for Machinery Health Prognostics Using Time-Series Data: A Review
,”
J. Vib. Eng. Technol.
,
9
(
6
), pp.
1123
1145
.
18.
Yuan
,
M.
,
Wu
,
Y.
, and
Lin
,
L.
,
2016
, “
Fault Diagnosis and Remaining Useful Life Estimation of Aero Engine Using LSTM Neural Network
,”
2016 IEEE International Conference on Aircraft Utility Systems (AUS)
,
Beijing, China
,
Oct. 10–12
, pp.
135
140
.
19.
Zheng
,
S.
,
Ristovski
,
K.
,
Farahat
,
A.
, and
Gupta
,
C.
,
2017
, “
Long Short-Term Memory Network for Remaining Useful Life Estimation
,”
2017 IEEE International Conference on Prognostics and Health Management (ICPHM)
,
Dallas, TX
,
June 19–21
, pp.
88
95
. .
20.
Wu
,
Y.
,
Yuan
,
M.
,
Dong
,
S.
,
Lin
,
L.
, and
Liu
,
Y.
,
2018
, “
Remaining Useful Life Estimation of Engineered Systems Using Vanilla LSTM Neural Networks
,”
Neurocomputing
,
275
, pp.
167
179
.
21.
ElSaid
,
A. E. R.
,
El Jamiy
,
F.
,
Higgins
,
J.
,
Wild
,
B.
, and
Desell
,
T.
,
2018
, “
Optimizing Long Short-Term Memory Recurrent Neural Networks Using ant Colony Optimization to Predict Turbine Engine Vibration
,”
Appl. Soft Comput. J.
,
73
, pp.
969
991
.
22.
Bruneo
,
D.
, and
De Vita
,
F.
,
2019
, “
On the Use of LSTM Networks for Predictive Maintenance in Smart Industries
,”
2019 IEEE International Conference on Smart Computing (SMARTCOMP)
,
Washington, DC
,
June 12–15
, pp.
241
248
.
23.
Kayode
,
O.
, and
Tosun
,
A. S.
,
2019
, “
LIRUL: A Lightweight LSTM Based Model for Remaining Useful Life Estimation at the Edge
,”
2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC)
,
Milwaukee, WI
,
July 15–19
, pp.
177
182
.
24.
Zhao
,
S.
,
Zhang
,
Y.
,
Wang
,
S.
,
Zhou
,
B.
, and
Cheng
,
C.
,
2019
, “
A Recurrent Neural Network Approach for Remaining Useful Life Prediction Utilizing a Novel Trend Features Construction Method
,”
Meas. J. Int. Meas. Confed.
,
146
, pp.
279
288
.
25.
Wang
,
F.
,
Liu
,
X.
,
Deng
,
G.
,
Yu
,
X.
,
Li
,
H.
, and
Han
,
Q.
,
2019
, “
Remaining Life Prediction Method for Rolling Bearing Based on the Long Short-Term Memory Network
,”
Neural Process. Lett.
,
50
(
3
), pp.
2437
2454
.
26.
Zhang
,
B.
,
Zhang
,
S.
, and
Li
,
W.
,
2019
, “
Bearing Performance Degradation Assessment Using Long Short-Term Memory Recurrent Network
,”
Comput. Ind.
,
106
, pp.
14
29
.
27.
He
,
M.
,
Zhou
,
Y.
,
Li
,
Y.
,
Wu
,
G.
, and
Tang
,
G.
,
2020
, “
Long Short-Term Memory Network With Multi-Resolution Singular Value Decomposition for Prediction of Bearing Performance Degradation
,”
Meas. J. Int. Meas. Confed.
,
156
, pp.
107582
.
28.
Cabrera
,
D.
,
Guamán
,
A.
,
Zhang
,
S.
,
Cerrada
,
M.
,
Sánchez
,
R. V.
,
Cevallos
,
J.
,
Long
,
J.
, and
Li
,
C.
, et al
,
2020
, “
Bayesian Approach and Time Series Dimensionality Reduction to LSTM-Based Model-Building for Fault Diagnosis of a Reciprocating Compressor
,”
Neurocomputing
,
380
, pp.
51
66
.
29.
Zhou
,
J. T.
,
Zhao
,
X.
, and
Gao
,
J.
,
2019
, “
Tool Remaining Useful Life Prediction Method Based on LSTM Under Variable Working Conditions
,”
Int. J. Adv. Manuf. Technol.
,
104
(
9–12
), pp.
4715
4726
.
30.
Yan
,
H.
,
Qin
,
Y.
,
Xiang
,
S.
,
Wang
,
Y.
, and
Chen
,
H.
,
2020
, “
Long-Term Gear Life Prediction Based on Ordered Neurons LSTM Neural Networks
,”
Meas. J. Int. Meas. Confed.
,
165
, p.
108205
.
31.
Ji
,
S.
,
Han
,
X.
,
Hou
,
Y.
,
Song
,
Y.
, and
Du
,
Q.
,
2020
, “
Remaining Useful Life Prediction of Airplane Engine Based on PCA–BLSTM
,”
Sensors
,
20
(
16
), pp.
1
13
.
32.
Shi
,
Z.
, and
Chehade
,
A.
,
2021
, “
A Dual-LSTM Framework Combining Change Point Detection and Remaining Useful Life Prediction
,”
Reliab. Eng. Syst. Saf.
,
205
, p.
107257
.
33.
Chui
,
K. T.
,
Gupta
,
B. B.
, and
Vasant
,
P.
,
2021
, “
A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine
,”
Electronics
,
10
(
3
), pp.
1
15
.
34.
Song
,
Y.
,
Shi
,
G.
,
Chen
,
L.
,
Huang
,
X.
, and
Xia
,
T.
,
2018
, “
Remaining Useful Life Prediction of Turbofan Engine Using Hybrid Model Based on Autoencoder and Bidirectional Long Short-Term Memory
,”
J. Shanghai Jiaotong Univ.
,
23
(
S1
), pp.
85
94
.
35.
Zhang
,
J.
,
Wang
,
P.
,
Yan
,
R.
, and
Gao
,
R. X.
,
2018
, “
Long Short-Term Memory for Machine Remaining Life Prediction
,”
J. Manuf. Syst.
,
48
(
Part C
), pp.
78
86
.
36.
Elsheikh
,
A.
,
Yacout
,
S.
, and
Ouali
,
M. S.
,
2019
, “
Bidirectional Handshaking LSTM for Remaining Useful Life Prediction
,”
Neurocomputing
,
323
, pp.
148
156
.
37.
Wang
,
J.
,
Wen
,
G.
,
Yang
,
S.
, and
Liu
,
Y.
,
2019
, “
Remaining Useful Life Estimation in Prognostics Using Deep Bidirectional LSTM Neural Network
,”
Proc.—2018 Progn. Syst. Heal. Manag. Conf. PHM-Chongqing
,
Chongqing, China
,
Oct. 26–28
, pp.
1037
1042
.
38.
Essien
,
A. E.
, and
Giannetti
,
C.
,
2020
, “
A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders
,”
IEEE Trans. Ind. Informatics
,
16
(
9
) pp.
6069
6078
.
39.
Zhang
,
H.
,
Zhang
,
Q.
,
Shao
,
S.
,
Niu
,
T.
, and
Yang
,
X.
,
2017
, “
Attention-Based LSTM Network for Rotatory Machine Remaining Useful Life Prediction
,”
IEEE Access
,
8
, pp.
132188
132199
.
40.
Xia
,
T.
,
Song
,
Y.
,
Zheng
,
Y.
,
Pan
,
E.
, and
Xi
,
L.
,
2020
, “
An Ensemble Framework Based on Convolutional bi-Directional LSTM With Multiple Time Windows for Remaining Useful Life Estimation
,”
Comput. Ind.
,
115
, p.
103182
.
41.
Fawaz
,
H. I.
,
Forestier
,
G.
,
Weber
,
J.
, and
Idoumghar
,
L.
,
2019
, “
Deep Learning for Time Series Classification: A Review
,”
Data Min. Knowl. Discov.
,
33
(
4
), pp.
917
963
.
42.
Wang
,
Z.
,
Qu
,
J.
,
Fang
,
X.
,
Li
,
H.
,
Zhong
,
T.
, and
Ren
,
H.
,
2020
, “
Prediction of Early Stabilization Time of Electrolytic Capacitor Based on ARIMA-Bi_LSTM Hybrid Model
,”
Neurocomputing
,
403
, pp.
63
79
.
43.
Snoek
,
J.
,
Larochelle
,
H.
, and
Adams
,
R. P.
,
2012
, “
Practical Bayesian Optimization of Machine Learning Algorithms
,”
Adv. Neural. Inf. Process. Syst.
,
25
, pp.
1
12
.
44.
Malan
,
N. S.
, and
Sharma
,
S.
,
2019
, “
Feature Selection Using Regularized Neighbourhood Component Analysis to Enhance the Classification Performance of Motor Imagery Signals
,”
Comput. Biol. Med.
,
107
, pp.
118
126
.
45.
Yang
,
W.
,
Wang
,
K.
, and
Zuo
,
W.
,
2012
, “
Neighborhood Component Feature Selection for High-Dimensional Data
,”
J. Comput.
,
7
(
1
), pp.
162
168
.
46.
Hochreiter
,
S.
, and
Schmidhuber
,
J.
,
1997
, “
Long Short-Term Memory
,”
Neural Comput.
,
9
(
8
), pp.
1735
1780
.
47.
Houdt
,
G. V.
,
Mosquera
,
C.
, and
Nápoles
,
G.
,
2020
, “
A Review on the Long Short-Term Memory Model
,”
Artif. Intell. Rev.
,
53
(
8
), pp.
5929
5955
.
48.
Gers
,
F.
,
Schmidhuber
,
J.
, and
Cummins
,
F.
,
1999
, “
Learning to Forget: Continual Prediction With LSTM
,”
1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470)
,
Edinburgh, UK
,
Sept. 7–10
.
49.
Greff
,
K.
,
Srivastava
,
R. K.
,
Koutník
,
J.
,
Steunebrink
,
B. R.
, and
Schmidhuber
,
J.
,
2017
, “
LSTM: A Search Space Odyssey
,”
IEEE Trans. Neural Netw. Learn. Syst.
,
28
(
10
), pp.
2222
2232
.
50.
Li
,
J.
, and
He
,
D.
,
2020
, “
A Bayesian Optimization AdaBN-DCNN Method With Self-Optimized Structure and Hyperparameters for Domain Adaptation Remaining Useful Life Prediction
,”
IEEE Access
,
8
, pp.
41482
41501
.
51.
Tran
,
A.
,
Wildey
,
T.
, and
McCann
,
S.
,
2020
, “
sMF-BO-2CoGP: A Sequential Multi-Fidelity Constrained Bayesian Optimization Framework for Design Applications
,”
J. Comput. Inf. Sci. Eng.
,
20
(
3
), p.
031007
.
52.
Ghoreishi
,
S. F.
, and
Imani
,
M.
,
2021
, “
Bayesian Surrogate Learning for Uncertainty Analysis of Coupled Multidisciplinary Systems
,”
J. Comput. Inf. Sci. Eng.
,
21
(
4
), p.
041009
.
53.
Tran
,
A.
,
Eldred
,
M.
,
McCann
,
S.
, and
Wang
,
Y.
,
2020
, “
srMO-BO-3GP: A Sequential Regularized Multi-Objective Constrained Bayesian Optimization for Design Applications
,”
Proceedings of the ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 9: 40th Computers and Information in Engineering Conference (CIE)
,
Virtual, Online
,
Aug. 17–19
.
54.
Kumar
,
S.
, and
Singh
,
B.
,
2018
, “
Prediction of Tool Chatter and Metal Removal Rate in Turning Operation on Lathe Using a New Merged Technique
,”
J. Brazilian Soc. Mech. Sci. Eng.
,
40
(
2
), pp.
1
27
.
55.
Sharma
,
A.
,
Amarnath
,
M.
, and
Kankar
,
P. K.
,
2018
, “
Life Assessment and Health Monitoring of Rolling Element Bearings: An Experimental Study
,”
Life Cycle Reliab. Saf. Eng.
,
7
(
2
), pp.
97
114
.
56.
Abbasimehr
,
H.
,
Shabani
,
M.
, and
Yousefi
,
M.
,
2020
, “
An Optimized Model Using LSTM Network for Demand Forecasting
,”
Comput. Ind. Eng.
,
143
, p.
106435
.
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