Compound fault diagnosis for rotating machinery: State-of-the-art, challenges, and opportunities
Compound fault, as a primary failure leading to unexpected downtime of rotating machinery,
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …
dramatically increases the difficulty in fault diagnosis. To deal with the difficulty encountered …
[HTML][HTML] The advance of digital twin for predictive maintenance: The role and function of machine learning
The recent advance of digital twin (DT) has greatly facilitated the development of predictive
maintenance (PdM). DT for PdM enables accurate equipment status recognition and …
maintenance (PdM). DT for PdM enables accurate equipment status recognition and …
Neural-transformer: A brain-inspired lightweight mechanical fault diagnosis method under noise
Recently, as a representative of deep learning methods, Transformers have shown great
prowess in intelligent fault diagnosis, offering powerful feature extraction and modeling …
prowess in intelligent fault diagnosis, offering powerful feature extraction and modeling …
Physics informed neural networks for fault severity identification of axial piston pumps
Artificial intelligence (AI) has shown great potential in the maintenance stage of industrial
manufacturing. However, the existing data-driven methods often lack integration with …
manufacturing. However, the existing data-driven methods often lack integration with …
MJAR: A novel joint generalization-based diagnosis method for industrial robots with compound faults
Compound faults inevitably occur in multi-joint industrial robots resulting in excessive
vibration. Intelligent diagnosis for the occurrence and position of fault joints can efficiently …
vibration. Intelligent diagnosis for the occurrence and position of fault joints can efficiently …
Integrated decision-making with adaptive feature weighting adversarial network for multi-target domain compound fault diagnosis of machinery
X Zhang, J Wang, Z Zhang, B Han, H Bao… - Advanced Engineering …, 2024 - Elsevier
Due to the varying manufacturing demands placed upon mechanical equipment, it is often
operated under fluctuating loads and diverse working conditions over extended periods …
operated under fluctuating loads and diverse working conditions over extended periods …
Multirobot collaborative task dynamic scheduling based on multiagent reinforcement learning with heuristic graph convolution considering robot service performance
J Zhou, L Zheng, W Fan - Journal of Manufacturing Systems, 2024 - Elsevier
To address the problem of multirobot collaborative task scheduling considering the
degradation of industrial robot performance and the recovery of robot performance through …
degradation of industrial robot performance and the recovery of robot performance through …
A label information vector generative zero-shot model for the diagnosis of compound faults
Diagnosis of compound faults remains a challenge owing to the coupling of fault
characteristics and the exponential increment of the number of possible fault types. Current …
characteristics and the exponential increment of the number of possible fault types. Current …
Lightweight convolutional transformers enhanced meta-learning for compound fault diagnosis of industrial robot
Recent advance of deep learning has seen remarkable progress in compound fault
diagnosis modeling for industrial robots. Nevertheless, the data scarcity of compound fault …
diagnosis modeling for industrial robots. Nevertheless, the data scarcity of compound fault …
[HTML][HTML] A Review of Digital Twinning for Rotating Machinery
This review focuses on the definitions, modalities, applications, and performance of various
aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this …
aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this …