Deep learning for time-series prediction in IIoT: progress, challenges, and prospects
Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable
intelligent process control, analysis, and management, such as complex equipment …
intelligent process control, analysis, and management, such as complex equipment …
Machine learning and deep learning in chemical health and safety: a systematic review of techniques and applications
Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that
can automatically learn from data and can perform tasks such as predictions and decision …
can automatically learn from data and can perform tasks such as predictions and decision …
Intelligent condition monitoring of industrial plants: An overview of methodologies and uncertainty management strategies
Condition monitoring plays a significant role in the safety and reliability of modern industrial
systems. Artificial intelligence (AI) approaches are gaining attention from academia and …
systems. Artificial intelligence (AI) approaches are gaining attention from academia and …
Bidirectional deep recurrent neural networks for process fault classification
In this study, a new approach for time series based condition monitoring and fault diagnosis
based on bidirectional recurrent neural networks is presented. The application of …
based on bidirectional recurrent neural networks is presented. The application of …
Explainability: Relevance based dynamic deep learning algorithm for fault detection and diagnosis in chemical processes
The focus of this work is on Statistical Process Control (SPC) of a manufacturing process
based on available measurements. Two important applications of SPC in industrial settings …
based on available measurements. Two important applications of SPC in industrial settings …
A novel unsupervised approach for batch process monitoring using deep learning
Process monitoring is an important tool used to ensure safe operation of a process plant and
to maintain high quality of end products. The focus of this work is on unsupervised Statistical …
to maintain high quality of end products. The focus of this work is on unsupervised Statistical …
A multimodal deep learning-based fault detection model for a plastic injection molding process
The authors of this work propose a deep learning-based fault detection model that can be
implemented in the field of plastic injection molding. Compared to conventional approaches …
implemented in the field of plastic injection molding. Compared to conventional approaches …
Hierarchical deep lstm for fault detection and diagnosis for a chemical process
A hierarchical structure based on a Deep LSTM Supervised Autoencoder Neural Network
(Deep LSTM-SAE NN) is presented for the detection and classification of faults in industrial …
(Deep LSTM-SAE NN) is presented for the detection and classification of faults in industrial …
Data-driven fault classification in large-scale industrial processes using reduced number of process variables
N Yassaie, S Gargoum… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
In large-scale industrial processes, fault diagnosis is of paramount importance, as faults
jeopardize the stability and performance of processes. However, effective fault diagnosis …
jeopardize the stability and performance of processes. However, effective fault diagnosis …
Unsupervised fault detection of pharmaceutical processes using long short-term memory autoencoders
M Aghaee, S Krau, M Tamer… - Industrial & Engineering …, 2023 - ACS Publications
Unsupervised multilayer long short-term memory autoencoder (LSTM-AE) models are
proposed for monitoring nonlinear batch processes. The methodology is demonstrated for a …
proposed for monitoring nonlinear batch processes. The methodology is demonstrated for a …