Process monitoring technologies play a key role in maintaining the steady state of industrial
processes. However, with the increasing complexity of modern industrial processes …

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 …

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 …

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 …

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 …

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 …

Chemical processes are in general subject to time variant conditions because of load
changes, product grade transitions, or other causes, resulting in typical nonstationary …

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 …

Abstract Faulty operations of Heating, Ventilation and Air Conditioning (HVAC) chiller
systems can lead to discomfort for the users, energy wastage, system unreliability and …

Projection to latent structures or partial least squares (PLS) produces output-supervised
decomposition on input X, while principal component analysis (PCA) produces …