A Data-Driven LSTM Soft Sensor Model Based on Bayesian Optimization for Hydraulic Pressure Measurement of Excavator-Reference-Cited by-同舟云学术

A Data-Driven LSTM Soft Sensor Model Based on Bayesian Optimization for Hydraulic Pressure Measurement of Excavator

Published:2023-11-01 Issue:21 Volume:23 Page:25749-25759
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Ma Wei¹,Tan Lin¹,Feng Hao¹^ORCID,Ma Shoulei¹,Cao Donghui²,Yin Chenbo¹^ORCID

Affiliation:

1. School of Mechanical and Power Engineering, United Institute of Excavator Key Technology and the Institute of Automobile and Construction Machinery, Nanjing Tech University, Nanjing, China

2. Sany Heavy Machinery Company Ltd., Suzhou, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Natural Science Foundation General Project of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10302460/10233209.pdf?arnumber=10233209

Reference34 articles.

1. Design of inferential sensors in the process industry: A review of Bayesian methods

2. Robust Inferential Sensors Based on Ensemble of Predictors Generated by Genetic Programming

3. Attention-LSTM architecture combined with Bayesian hyperparameter optimization for indoor temperature prediction

4. Sampling-Interval-Aware LSTM for Industrial Process Soft Sensing of Dynamic Time Sequences With Irregular Sampling Measurements

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