An unsupervised feature extraction method for nonlinear deterioration process of complex equipment under multi dimensional no-label signals-Reference-Cited by-同舟云学术

An unsupervised feature extraction method for nonlinear deterioration process of complex equipment under multi dimensional no-label signals

Published:2018-01 Issue: Volume:269 Page:464-473
ISSN:0924-4247
Container-title:Sensors and Actuators A: Physical
language:en
Short-container-title:Sensors and Actuators A: Physical

Author:

Chen Gaige,Chen Jinglong,Zi Yanyang,Pan Jun,Han Wei

Funder

National Natural Science Foundation of China

National Natural Science Foundation of China for Innovation Research Group

National Key Research and Development Program of China

Shaanxi Industrial Science and Technology Project

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Metals and Alloys,Surfaces, Coatings and Films,Condensed Matter Physics,Instrumentation,Electronic, Optical and Magnetic Materials

Reference23 articles.

1. Prognostics and health management design for rotary machinery systems—reviews, methodology and applications;Lee;Mech. Syst. Signal Process.,2014

2. Non-contact crack detection of high-speed blades based on principal component analysis and euclidian angles using optical-fiber sensors;Chen;Sens. Actuators A: Phys.,2013

3. Touch sensor for social robots and interactive objects affective interaction;Mazzei;Sens. Actuators A: Phys.,2016

4. Simple and rapid differentiation of toxic gases using a quartz crystal microbalance sensor array coupled with principal component analysis;Su;Sens. Actuators A: Phys.,2017

5. Feature extraction for novelty detection as applied to fault detection in machinery;McBain;Pattern Recognit. Lett.,2011

Cited by 22 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Principal-feature-guided degradation trend prediction algorithm based on gear fault dynamics model;Engineering Failure Analysis;2024-09

2. A novel quality risk evaluation framework for complex equipment development integrating PHFS-QFD and grey clustering;Grey Systems: Theory and Application;2023-11-28

3. A health indicator construction method of rolling bearing based on vibration image features and deep autoencoder network;2023 5th International Conference on System Reliability and Safety Engineering (SRSE);2023-10-20

4. A Spatiotemporal Fusion Autoencoder-Based Health Indicator Automatic Construction Method for Rotating Machinery Considering Vibration Signal Expression;IEEE Sensors Journal;2023-10-15

5. A soft-target difference scaling network via relational knowledge distillation for fault detection of liquid rocket engine under multi-source trouble-free samples;Reliability Engineering & System Safety;2022-12