PMID- 35336579
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220329
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 22
IP  - 6
DP  - 2022 Mar 21
TI  - Performance Degradation Prediction Using LSTM with Optimized Parameters.
LID - 10.3390/s22062407 [doi]
LID - 2407
AB  - Predicting the degradation of mechanical components, such as rolling bearings is 
      critical to the proper monitoring of the condition of mechanical equipment. A new 
      method, based on a long short-term memory network (LSTM) algorithm, has been 
      developed to improve the accuracy of degradation prediction. The model parameters 
      are optimized via improved particle swarm optimization (IPSO). Regarding how this 
      applies to the rolling bearings, firstly, multi-dimension feature parameters are 
      extracted from the bearing's vibration signals and fused into responsive features 
      by using the kernel joint approximate diagonalization of eigen-matrices (KJADE) 
      method. Then, the between-class and within-class scatter (SS) are calculated to 
      develop performance degradation indicators. Since network model parameters 
      influence the predictive accuracy of the LSTM model, an IPSO algorithm is used to 
      obtain the optimal prediction model via the LSTM model parameters' optimization. 
      Finally, the LSTM model, with said optimal parameters, was used to predict the 
      degradation trend of the bearing's performance. The experiment's results show 
      that the proposed method can effectively identify the trends of degradation and 
      performance. Moreover, the predictive accuracy of this proposed method is greater 
      than that of the extreme learning machine (ELM) and support vector regression 
      (SVR), which are the algorithms conventionally used in degradation modeling.
FAU - Hu, Yawei
AU  - Hu Y
AD  - College of Electrical Engineering and Automation, Anhui University, Hefei 230601, 
      China.
FAU - Wei, Ran
AU  - Wei R
AD  - Anhui NARI Jiyuan Electric Co., Ltd., Hefei 230601, China.
FAU - Yang, Yang
AU  - Yang Y
AD  - China North Vehicle Research Institute, Beijing 100071, China.
FAU - Li, Xuanlin
AU  - Li X
AD  - College of Electrical Engineering and Automation, Anhui University, Hefei 230601, 
      China.
FAU - Huang, Zhifu
AU  - Huang Z
AD  - College of Electrical Engineering and Automation, Anhui University, Hefei 230601, 
      China.
FAU - Liu, Yongbin
AU  - Liu Y
AUID- ORCID: 0000-0002-3420-3784
AD  - College of Electrical Engineering and Automation, Anhui University, Hefei 230601, 
      China.
FAU - He, Changbo
AU  - He C
AD  - College of Electrical Engineering and Automation, Anhui University, Hefei 230601, 
      China.
FAU - Lu, Huitian
AU  - Lu H
AD  - JJL College of Engineering, South Dakota State University, Brookings, SD 57007, 
      USA.
LA  - eng
GR  - 52075001, 52105082, 52105040, 52075002/National Natural Science Foundation of 
      China/
GR  - MKF20210008/the Key Basic Research Project/
PT  - Journal Article
DEP - 20220321
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC8949053
OTO - NOTNLM
OT  - IPSO
OT  - KJADE
OT  - LSTM
OT  - degradation prediction
OT  - performance degradation
OT  - rolling bearing
COIS- The authors declare no conflict of interest.
EDAT- 2022/03/27 06:00
MHDA- 2022/03/27 06:01
PMCR- 2022/03/21
CRDT- 2022/03/26 01:06
PHST- 2022/02/05 00:00 [received]
PHST- 2022/03/01 00:00 [revised]
PHST- 2022/03/10 00:00 [accepted]
PHST- 2022/03/26 01:06 [entrez]
PHST- 2022/03/27 06:00 [pubmed]
PHST- 2022/03/27 06:01 [medline]
PHST- 2022/03/21 00:00 [pmc-release]
AID - s22062407 [pii]
AID - sensors-22-02407 [pii]
AID - 10.3390/s22062407 [doi]
PST - epublish
SO  - Sensors (Basel). 2022 Mar 21;22(6):2407. doi: 10.3390/s22062407.