[HTML][HTML] A review on autoencoder based representation learning for fault detection and diagnosis in industrial processes
Process monitoring technologies play a key role in maintaining the steady state of industrial
processes. However, with the increasing complexity of modern industrial processes …
processes. However, with the increasing complexity of modern industrial processes …
Survey on data-driven industrial process monitoring and diagnosis
SJ Qin - Annual reviews in control, 2012 - Elsevier
This paper provides a state-of-the-art review of the methods and applications of data-driven
fault detection and diagnosis that have been developed over the last two decades. The …
fault detection and diagnosis that have been developed over the last two decades. The …
Performance supervised plant-wide process monitoring in industry 4.0: A roadmap
The intensive research and development efforts directed towards large-scale complex
industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose …
industrial systems in the context of Industry 4.0 indicate that safety and reliability issues pose …
Industrial process monitoring in the big data/industry 4.0 era: From detection, to diagnosis, to prognosis
MS Reis, G Gins - Processes, 2017 - mdpi.com
We provide a critical outlook of the evolution of Industrial Process Monitoring (IPM) since its
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …
introduction almost 100 years ago. Several evolution trends that have been structuring IPM …
Wind turbine fault detection using a denoising autoencoder with temporal information
Data-driven approaches have gained increasing interests in the fault detection of wind
turbines (WTs) due to the difficulty in system modeling and the availability of sensor data …
turbines (WTs) due to the difficulty in system modeling and the availability of sensor data …
Distributed parallel PCA for modeling and monitoring of large-scale plant-wide processes with big data
In order to deal with the modeling and monitoring issue of large-scale industrial processes
with big data, a distributed and parallel designed principal component analysis approach is …
with big data, a distributed and parallel designed principal component analysis approach is …
A full‐condition monitoring method for nonstationary dynamic chemical processes with cointegration and slow feature analysis
Chemical processes are in general subject to time variant conditions because of load
changes, product grade transitions, or other causes, resulting in typical nonstationary …
changes, product grade transitions, or other causes, resulting in typical nonstationary …
Total projection to latent structures for process monitoring
Partial least squares or projection to latent structures (PLS) has been used in multivariate
statistical process monitoring similar to principal component analysis. Standard PLS often …
statistical process monitoring similar to principal component analysis. Standard PLS often …
Data-driven fault detection and diagnosis for HVAC water chillers
A Beghi, R Brignoli, L Cecchinato, G Menegazzo… - Control Engineering …, 2016 - Elsevier
Abstract Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller
systems can lead to discomfort for the users, energy wastage, system unreliability and …
systems can lead to discomfort for the users, energy wastage, system unreliability and …
Geometric properties of partial least squares for process monitoring
Projection to latent structures or partial least squares (PLS) produces output-supervised
decomposition on input X, while principal component analysis (PCA) produces …
decomposition on input X, while principal component analysis (PCA) produces …