Artificial intelligence applications in supply chain management
M Pournader, H Ghaderi, A Hassanzadegan… - International Journal of …, 2021 - Elsevier
This paper presents a systematic review of studies related to artificial intelligence (AI)
applications in supply chain management (SCM). Our systematic search of the related …
applications in supply chain management (SCM). Our systematic search of the related …
An analysis of process fault diagnosis methods from safety perspectives
Industry 4.0 provides substantial opportunities to ensure a safer environment through online
monitoring, early detection of faults, and preventing the faults to failures transitions. Decision …
monitoring, early detection of faults, and preventing the faults to failures transitions. Decision …
Deep convolutional neural network model based chemical process fault diagnosis
H Wu, J Zhao - Computers & chemical engineering, 2018 - Elsevier
Numerous accidents in chemical processes have caused emergency shutdowns, property
losses, casualties and/or environmental disruptions in the chemical process industry. Fault …
losses, casualties and/or environmental disruptions in the chemical process industry. Fault …
A convolutional neural network for fault classification and diagnosis in semiconductor manufacturing processes
KB Lee, S Cheon, CO Kim - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Many studies on the prediction of manufacturing results using sensor signals have been
conducted in the field of fault detection and classification (FDC) for semiconductor …
conducted in the field of fault detection and classification (FDC) for semiconductor …
Knowledge transfer for rotary machine fault diagnosis
This paper intends to provide an overview on recent development of knowledge transfer for
rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After …
rotary machine fault diagnosis (RMFD) by using different transfer learning techniques. After …
A deep learning model for process fault prognosis
Early fault detection and fault prognosis are crucial functions to ensure safe process
operations. Fault prognosis can detect and isolate early developing faults as well as predict …
operations. Fault prognosis can detect and isolate early developing faults as well as predict …
A deep belief network based fault diagnosis model for complex chemical processes
Z Zhang, J Zhao - Computers & chemical engineering, 2017 - Elsevier
Data-driven methods have been regarded as desirable methods for fault detection and
diagnosis (FDD) of practical chemical processes. However, with the big data era coming …
diagnosis (FDD) of practical chemical processes. However, with the big data era coming …
Intelligent particle filter and its application to fault detection of nonlinear system
The particle filter (PF) provides a kind of novel technique for estimating the hidden states of
the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from …
the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from …
Interaction-aware graph neural networks for fault diagnosis of complex industrial processes
Fault diagnosis of complex industrial processes becomes a challenging task due to various
fault patterns in sensor signals and complex interactions between different units. However …
fault patterns in sensor signals and complex interactions between different units. However …
One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes
Noise and high-dimension of process signals decrease effectiveness of those regular fault
detection and diagnosis models in multivariate processes. Deep learning technique shows …
detection and diagnosis models in multivariate processes. Deep learning technique shows …