Analysing Recent Breakthroughs in Fault Diagnosis through Sensor: A Comprehensive Overview.
S Chauhan, G Vashishtha… - … -Computer Modeling in …, 2024 - search.ebscohost.com
Sensors, vital elements in data acquisition systems, play a crucial role in various industries.
However, their exposure to harsh operating conditions makes them vulnerable to faults that …
However, their exposure to harsh operating conditions makes them vulnerable to faults that …
Reserving embedding space for new fault types: A new continual learning method for bearing fault diagnosis
In complex operating environments, rotating equipment may continually generate new fault
categories, affecting the safety of equipment operation, and the number of collected fault …
categories, affecting the safety of equipment operation, and the number of collected fault …
Domain expansion fusion single-domain generalization framework for mechanical fault diagnosis under unknown working conditions
X Li, J Tang, Y Pu, C Wang, H Cao, X Ding… - … Applications of Artificial …, 2024 - Elsevier
In real industrial scenarios, mechanical systems often adjust working conditions based on
specific tasks, leading to challenges in collecting data for all possible machine states in …
specific tasks, leading to challenges in collecting data for all possible machine states in …
A Review on Incremental Learning-based Fault Diagnosis of Dynamic Systems
Effective fault diagnosis methods for dynamic systems are crucial in various industrial
applications. As systems become increasingly complex, traditional diagnostic frameworks …
applications. As systems become increasingly complex, traditional diagnostic frameworks …
Pseudo-label assisted semi-supervised adversarial enhancement learning for fault diagnosis of gearbox degradation with limited data
The gearbox plays a crucial role in the transmission system of mechanical equipment, yet its
failure is frequent due to complex operational environments. Existing data-driven methods …
failure is frequent due to complex operational environments. Existing data-driven methods …
Multi-timescale attention residual shrinkage network with adaptive global-local denoising for rolling-bearing fault diagnosis
H Gao, X Zhang, X Gao, F Li, H Han - Knowledge-Based Systems, 2024 - Elsevier
In actual engineering scenarios, bearing fault signals are inevitably overwhelmed by strong
background noise from various sources. However, most deep-learning-based diagnostic …
background noise from various sources. However, most deep-learning-based diagnostic …
Prototype space boundary alignment network for bearing continuous fault diagnosis under class-incremental scenarios
Z He, J Shi, W Huang, Z Zhu, J Wang… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
In recent years, the fault diagnosis method based on continuous learning has been widely
used in the field of health monitoring of key components of rotating machinery. Most existing …
used in the field of health monitoring of key components of rotating machinery. Most existing …