Dear Colleagues,

The principles at the basis of Industry 4.0 presuppose continuous monitoring of processes and working conditions. An efficient measurement system is a fundamental pillar of this strategy. Physical and economic constraints are often in contrast with efficiency and cost reduction. In these scenarios, the possibility to adopt models, known as soft sensors (SSs), devoted to the estimation of process variables, represents a significant turning point.

The implementation of Soft Sensors for industrial processes historically represents an interesting field of applications for machine learning techniques. Advanced methodologies like long short-term memory, stacked autoencoders, convolutional neural networks, reservoir computing, and bio-inspired learning techniques have recently been proposed to improve SS behavior.

Though SSs are already widely used in industrial systems, and in particular in process industries, different aspects need to be investigated. Among these, we can consider SS design for time-variant systems, feature and data selection, outliers detection, big/small datasets, choice of the model class, model validation and maintenance, model interpretability, and transfer learning.

This Special Issue will focus on recent developments and challenges of soft sensor design both from a theoretical perspective and industrial applications.

Contributions related to the aforementioned topics are encouraged and a non-exhaustive list of topics can be found in the keywords.

Prof. Dr. Maria Gabriella Xibilia
Prof. Dr. Luca Patanè
Guest Editors

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• Feature extraction 
• Outliers detection 
• Data selection 
• Big and small datasets 
• System identification 
• Linear and nonlinear models 
• Deep learning techniques 
• Optimization strategies 
• Recurrent neural networks 
• Reservoir computing 
• Bio-inspired learning techniques 
• Model validation 
• Soft sensor maintenance 
• Transfer learning 
• Model interpretability 
• Sparse modeling 
• Soft sensors for time-varying systems 
• Industrial applications of soft sensors