Fault diagnosis of rolling bearings based on an improved stack autoencoder and support vector machine
M Cui, Y Wang, X Lin, M Zhong - IEEE Sensors Journal, 2020 - ieeexplore.ieee.org
In recent years, autoencoder has been widely used for the fault diagnosis of mechanical
equipment because of its excellent performance in feature extraction and dimension …
equipment because of its excellent performance in feature extraction and dimension …
A novel multivariate statistical process monitoring algorithm: Orthonormal subspace analysis
Partial least squares (PLS) and canonical correlation analysis (CCA) are two most popular
key performance indicators (KPI) monitoring algorithms, which have shortcomings in dealing …
key performance indicators (KPI) monitoring algorithms, which have shortcomings in dealing …
Recursive correlative statistical analysis method with sliding windows for incipient fault detection
Y Qin, Y Yan, H Ji, Y Wang - IEEE Transactions on Industrial …, 2021 - ieeexplore.ieee.org
This article proposes a new combination of a correlative statistical analysis and the sliding
window technique to detect incipient faults. Compared with the existing monitoring methods …
window technique to detect incipient faults. Compared with the existing monitoring methods …
Artificial neural correlation analysis for performance-indicator-related nonlinear process monitoring
In this article, a novel fault detection and process monitoring method referred to as artificial
neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are …
neural correlation analysis (ANCA) is proposed. Because nonlinear characteristics are …
[HTML][HTML] A review of data-driven intelligent monitoring for geological drilling processes
S Du, C Huang, X Ma, H Fan - Processes, 2024 - mdpi.com
The exploration and development of resources and energy are fundamental to human
survival and development, and geological drilling is a key method for deep resource and …
survival and development, and geological drilling is a key method for deep resource and …
Degradation state partition and compound fault diagnosis of rolling bearing based on personalized multilabel learning
X Ma, Y Hu, M Wang, F Li… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
The prognostic and health management (PHM) of rolling bearings has been a popular
research area. Since bearing fault is inevitable during degradation, how to improve the PHM …
research area. Since bearing fault is inevitable during degradation, how to improve the PHM …
Multiscale dynamic feature learning for quality prediction based on hierarchical sequential generative network
In industrial processes, long short-term memory (LSTM) is usually used for temporal
dynamic modeling of soft sensor. The process data usually have various temporal …
dynamic modeling of soft sensor. The process data usually have various temporal …