Prognostics and health management: A review of vibration based bearing and gear health indicators
Prognostics and health management is an emerging discipline to scientifically manage the
health condition of engineering systems and their critical components. It mainly consists of …
health condition of engineering systems and their critical components. It mainly consists of …
Deep-convolution-based LSTM network for remaining useful life prediction
Accurate prediction of remaining useful life (RUL) has been a critical and challenging
problem in the field of prognostics and health management (PHM), which aims to make …
problem in the field of prognostics and health management (PHM), which aims to make …
Fault diagnosis of wind turbine based on Long Short-term memory networks
Time-series data is widely adopted in condition monitoring and fault diagnosis of wind
turbines as well as other energy systems, where long-term dependency is essential to form …
turbines as well as other energy systems, where long-term dependency is essential to form …
Performance prediction using high-order differential mathematical morphology gradient spectrum entropy and extreme learning machine
H Zhao, H Liu, J Xu, W Deng - IEEE transactions on …, 2019 - ieeexplore.ieee.org
Performance prediction is significant to monitor the health status of rolling bearings, which
can greatly reduce the loss caused by potential faults in the whole life cycle of rolling …
can greatly reduce the loss caused by potential faults in the whole life cycle of rolling …
Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing
S Haidong, C Junsheng, J Hongkai, Y Yu… - Knowledge-Based …, 2020 - Elsevier
Early fault prognosis of bearing is a very meaningful yet challenging task to improve the
security of rotating machinery. For this purpose, a novel method based on enhanced deep …
security of rotating machinery. For this purpose, a novel method based on enhanced deep …
VMD based trigonometric entropy measure: a simple and effective tool for dynamic degradation monitoring of rolling element bearing
A Kumar, CP Gandhi, G Vashishtha… - Measurement …, 2021 - iopscience.iop.org
Early identification of rolling element defects is always a topic of interest for researchers and
the industry. For early fault identification, a simple and effective dynamic degradation …
the industry. For early fault identification, a simple and effective dynamic degradation …
Deep coupling autoencoder for fault diagnosis with multimodal sensory data
Effective fault diagnosis of rotating machinery has multifarious benefits, such as improved
safety, enhanced reliability, and reduced maintenance cost, for complex engineered …
safety, enhanced reliability, and reduced maintenance cost, for complex engineered …
State-space modeling and novel entropy-based health indicator for dynamic degradation monitoring of rolling element bearing
A Kumar, C Parkash, G Vashishtha, H Tang… - Reliability Engineering & …, 2022 - Elsevier
This work is dedicated to the establishment of state-space modeling combined with a novel
probabilistic entropy-based health indicator (HI), needed to assess the dynamic degradation …
probabilistic entropy-based health indicator (HI), needed to assess the dynamic degradation …
Mahalanobis semi-supervised mapping and beetle antennae search based support vector machine for wind turbine rolling bearings fault diagnosis
Z Wang, L Yao, Y Cai, J Zhang - Renewable Energy, 2020 - Elsevier
Intelligent fault diagnosis of wind turbine rolling bearings is an important task to improve the
reliability of wind turbines and reduce maintenance costs. In this paper, a novel intelligent …
reliability of wind turbines and reduce maintenance costs. In this paper, a novel intelligent …
An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems
The machine fault diagnosis is being considered in a larger-scale complex system with
numerous measurements from diverse subsystems or components, where the collected data …
numerous measurements from diverse subsystems or components, where the collected data …