Search results for "soft sensor"

Input Selection Methods for Soft Sensor Design: A Survey

2020

Soft Sensors (SSs) are inferential models used in many industrial fields. They allow for real-time estimation of hard-to-measure variables as a function of available data obtained from online sensors. SSs are generally built using industries historical databases through data-driven approaches. A critical issue in SS design concerns the selection of input variables, among those available in a candidate dataset. In the case of industrial processes, candidate inputs can reach great numbers, making the design computationally demanding and leading to poorly performing models. An input selection procedure is then necessary. Most used input selection approaches for SS design are addressed in this …

Neural Networks as Soft Sensors: a Comparison in a Real World Application.

2006

Physical atmosphere parameters, as temperature or humidity, can be indirectly estimated on the surface of a monument by means of soft sensors based on neural networks, if an ambient air monitoring station works in the neighborhood of the monument itself. Since the soft sensors work as virtual instruments, the accuracy of such measurements has to be analyzed and validated from statistical and metrological points of view. The paper compares different typologies of neural networks, which can be used as soft sensors in a complex real world application: a non invasive monitoring of the conservation state of old monuments. In this context, several designed connessionistic systems, based on radial…

RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process

2021

The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acquired in the design of a SS for a given process to a similar one. This has been approached as a transfer learning problem from a source to a target domain. The implementation of a transf…

A methodology for sequencing batch reactor identification with artificial neural networks: A case study

2009

This paper presents a systematic methodology based on the application of artificial neural networks for sequencing batch reactor (SBR) identification. The SBR is a fill-and-draw biological wastewater technology, which is specially suited for nutrient removal. The proposed approach makes optimal use of the available data during the training stage and it is aimed at achieving high generalization ability. For this purpose, a wide range of experimental conditions, including different solids retention times and influent characteristics, has been used. The methodology is successfully applied to develop a soft-sensor for monitoring a laboratory-scale SBR operated for enhanced biological phosphorus…

Fuzzy Data Fusion for Real-World Mapping Using 360° Rotating Ultrasonic Sensor

1997

Abstract Mobile robot perception of the external environment is limited by the features of the used sensor. An useful technique used to improve robot perception is data fusion. This paper presents an approach to build a map of an unknown environment applying fuzzy data fusion methods to data acquired through an ultrasonic sensor. Conditioning of these data and motion control of the mobil robot by fuzzy data fusion are also described. The resulting two dimensional map is used for path planning and navigation. The proposed approach is exrperimentally tested using real distance measures acquired by a 360° rotating sensor.

A Novel Approach for Faulty Sensor Detection and Data Correction in Wireless Sensor Network

2013

he main Wireless Sensor Networks purpose is represented by areas of interest monitoring. Even if the Wireless sensor network is properly initialized, errors can occur during its monitoring tasks. The present work describes an approach for detecting faulty sensors in Wireless Sensor Network and for correcting their corrupted data. The approach is based on the assumption that exist a spatio-temporal cross- correlations among sensors. Two sequential mathematical tools are used. The first stage is a probabilistic tools, namely Markov Random Field, for a two-fold sensor classification (working or damaged). The last stage is represented by the Locally Weighted Regression model, a learning techniq…

Soft Sensor Transferability between Lines of a Sulfur Recovery Unit

2021

Abstract Soft Sensors (SSs) are mathematical models that allow real-time estimation of hard-to-measure variables as a function of easy-to-measure ones in an industrial process, emulating the behavior of existing sensors when they are, for instance, taken off for maintenance. The Sulfur Recovery Unit (SRU) from a refinery is taken in exam. Recurrent Neural Networks (RNN) can capture the nonlinearity of such process but present a high complexity training and a very time-consuming structure optimization. For this reason, strategies to use pre-existing models are here examined by testing the transferability of the SSs between two parallel lines of the process.

Comparison of different predictive models for nutrient estimation in a sequencing batch reactor for wastewater treatment

2006

Abstract In this paper different predictive models for nutrient estimation in a sequencing batch reactor (SBR) for wastewater treatment are compared: principal component regression (PCR), partial least squares (PLS), and artificial neural networks (ANNs). Two unfolding procedures were used: batch-wise and variable-wise. For the latter unfolding method, X and Y matrix augmentation with lagged variables were used in some models to incorporate process dynamics. The results have shown that batch-wise unfolding PLS models outperform the other approaches. The ANN models are good predictive models, but in this particular case-study, they do not outperform those multivariate projection models that …

Soft Sensor Design, Transferability and Causality through Machine Learning Techniques

2023

Roboception and adaptation in a cognitive robot

2023

In robotics, perception is usually oriented at understanding what is happening in the external world, while few works pay attention to what is occurring in the robot’s body. In this work, we propose an artificial somatosensory system, embedded in a cognitive architecture, that enables a robot to perceive the sensations from its embodiment while executing a task. We called these perceptions roboceptions, and they let the robot act according to its own physical needs in addition to the task demands. Physical information is processed by the robot to behave in a balanced way, determining the most appropriate trade-off between the achievement of the task and its well being. The experiments show …