[HTML][HTML] A systematic review of data fusion techniques for optimized structural health monitoring
Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles
Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving
and transportation systems to digitize and synergize connected automated vehicles …
and transportation systems to digitize and synergize connected automated vehicles …
A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance
Collaborative fault diagnosis can be facilitated by multisensory fusion technologies, as these
can give more reliable results with a more complete data set. Although deep learning …
can give more reliable results with a more complete data set. Although deep learning …
Intelligent mechanical fault diagnosis using multisensor fusion and convolution neural network
Diagnosis of mechanical faults in manufacturing systems is critical for ensuring safety and
saving costs. With the development of data transmission and sensor technologies …
saving costs. With the development of data transmission and sensor technologies …
Deep learning for prognostics and health management: State of the art, challenges, and opportunities
B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …
various engineering fields, such as aerospace, nuclear energy, and water declination …
Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis
Gearboxes are widely used in rotating machinery and various industrial applications for
transmission of power and torque. They operate for prolong hours and under different …
transmission of power and torque. They operate for prolong hours and under different …
SuperGraph: Spatial-temporal graph-based feature extraction for rotating machinery diagnosis
Vibration signals always contain noise and irregularities, which makes spectrum analysis
difficult to extract high-level features. Recently, graph theory has been applied to spectrum …
difficult to extract high-level features. Recently, graph theory has been applied to spectrum …
Fault diagnosis of wind turbine bearing using a multi-scale convolutional neural network with bidirectional long short term memory and weighted majority voting for …
In order to solve the problems of insufficient extrapolation of intelligent models for the fault
diagnosis of bearings in real wind turbines, this study has developed a multi-scale …
diagnosis of bearings in real wind turbines, this study has developed a multi-scale …
Data preprocessing techniques in convolutional neural network based on fault diagnosis towards rotating machinery
S Tang, S Yuan, Y Zhu - IEEE Access, 2020 - ieeexplore.ieee.org
Rotating machinery plays a critical role in many significant fields. However, the
unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of …
unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of …
Partial transfer learning in machinery cross-domain fault diagnostics using class-weighted adversarial networks
Recently, transfer learning has been receiving growing interests in machinery fault
diagnosis due to its strong generalization across different industrial scenarios. The existing …
diagnosis due to its strong generalization across different industrial scenarios. The existing …