专题：Fault Detection and Control Systems

This cluster of papers focuses on the application of various data-driven and statistical techniques for process fault detection and diagnosis in industrial settings. It covers topics such as process monitoring, fault isolation, soft sensors, model-based diagnosis, and the use of machine learning in analyzing and improving industrial processes.
最新文献
Digital twin-assisted graph contrastive domain adaptation for small-sample bearing fault diagnosis

article Full Text OpenAlex

Spatial-channel collaborative multi-scale graph interaction deep transfer learning for unsupervised rotating machinery fault diagnosis

article Full Text OpenAlex

A liquid-impulse neural network model based on heterogeneous fusion of multimodal information for interpretable rotating machinery fault diagnosis

article Full Text OpenAlex

Robust Open-Circuit Fault Diagnosis for PMSM Drives Under Unknown Operating Conditions

article Full Text OpenAlex

Semi-supervised cross-domain fault diagnosis via contrastive pre-training and annotation-efficient alignment strategy

article Full Text OpenAlex

An Improved Variational Autoencoder and Graph Attention Network Method for Wear Prediction of Aerospace Self-Lubricating Bearing Using Acoustic Emission Signal

article Full Text OpenAlex

SMNet: A Novel Compositional Generalization Model for Industrial Robot Multijoint Fault Diagnosis

article Full Text OpenAlex

Self-supervised learning for train bearing fault diagnosis based on time–frequency dual domain prediction

article Full Text OpenAlex

Data-driven fault detection and diagnosis in industrial process systems: A systematic review and perspective

review Full Text OpenAlex

Remaining useful life prediction based on self-attention mechanism -sequential variational autoencoder: From a semi-supervised perspective

article Full Text OpenAlex

近5年高被引文献
TranAD

article Full Text OpenAlex 1086 FWCI56.072

A review of the application of deep learning in intelligent fault diagnosis of rotating machinery

article Full Text OpenAlex 743 FWCI53.5277

Domain generalization for cross-domain fault diagnosis: An application-oriented perspective and a benchmark study

article Full Text OpenAlex 534 FWCI84.3178

Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016

review Full Text OpenAlex 522 FWCI13.7894

Feature Mode Decomposition: New Decomposition Theory for Rotating Machinery Fault Diagnosis

article Full Text OpenAlex 500 FWCI36.4932

Enriching Location Representation with Detailed Semantic Information

preprint Full Text OpenAlex 499 FWCI0

CNN parameter design based on fault signal analysis and its application in bearing fault diagnosis

article Full Text OpenAlex 438 FWCI47.4091

Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods

article Full Text OpenAlex 418 FWCI30.2877

From concept drift to model degradation: An overview on performance-aware drift detectors

article Full Text OpenAlex 410 FWCI23.7026

Deep discriminative transfer learning network for cross-machine fault diagnosis

article Full Text OpenAlex 397 FWCI30.6732