Fault diagnosis and self-healing for smart manufacturing: a review
Manufacturing systems are becoming more sophisticated and expensive, particularly with
the development of the intelligent industry. The complexity of the architecture and concept of …
the development of the intelligent industry. The complexity of the architecture and concept of …
Interpretation and explanation of convolutional neural network-based fault diagnosis model at the feature-level for building energy systems
Although deep learning models have been rapidly developed, their practical applications
still lag behind for building energy systems (BESs) fault diagnosis. Owing to the “black-box” …
still lag behind for building energy systems (BESs) fault diagnosis. Owing to the “black-box” …
Data-Driven Process Monitoring and Fault Diagnosis: A Comprehensive Survey
This paper presents a comprehensive review of the historical development, the current state
of the art, and prospects of data-driven approaches for industrial process monitoring. The …
of the art, and prospects of data-driven approaches for industrial process monitoring. The …
Fault detection and isolation using probabilistic wavelet neural operator auto-encoder with application to dynamic processes
Fault detection and isolation are crucial aspects that need to be considered for the safe and
reliable operation of process systems. The modern industrial process frequently employs …
reliable operation of process systems. The modern industrial process frequently employs …
A machine learning and data analytics approach for predicting evacuation and identifying contributing factors during hazardous materials incidents on railways
An emergency evacuation order might be issued in response to a railway incident involving
hazardous materials (hazmat), such as the February 2023 derailment at Palestine, Ohio …
hazardous materials (hazmat), such as the February 2023 derailment at Palestine, Ohio …
Causality-embedded reconstruction network for high-resolution fault identification in chemical process
F Lv, X Bi, Z Xu, J Zhao - Process Safety and Environmental Protection, 2024 - Elsevier
Fault identification is essential for analyzing the root causes and propagation of faults.
Traditional identification based on contribution plots often suffer from the smearing effect, a …
Traditional identification based on contribution plots often suffer from the smearing effect, a …
Multiple fault recognition for chemical processes based on TSK-type neural networks with nonlinear consequences
J Chen, X Liu, W Lu - Granular Computing, 2024 - Springer
Fault recognition systems are developed to characterize normal conditions and detect
different faults in a process plant, which is important for early warning and diagnosis …
different faults in a process plant, which is important for early warning and diagnosis …
Multivariate alarm systems to recognize rare unpostulated abnormal events
Most chemical and manufacturing plants have safety/reliability systems in place that are well
equipped to handle commonly occurring postulated abnormal events, but often prove to be …
equipped to handle commonly occurring postulated abnormal events, but often prove to be …
Unsupervised Transfer Learning for Fault Diagnosis across Similar Chemical Processes
R Qin, F Lv, H Ye, J Zhao - Process Safety and Environmental Protection, 2024 - Elsevier
Fault diagnosis plays a crucial role in chemical processes to prevent major accidents.
Recent advancements have leveraged deep learning to enhance fault diagnosis capabilities …
Recent advancements have leveraged deep learning to enhance fault diagnosis capabilities …
A deep learning methodology based on adaptive multiscale CNN and enhanced highway LSTM for industrial process fault diagnosis
Intelligent fault diagnostic techniques are crucial for ensuring the long-term reliability of
manufacturing. The process variables collected by sensors in real industrial systems …
manufacturing. The process variables collected by sensors in real industrial systems …