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Papers by Piero Baraldi

Research paper thumbnail of A machine learning-based methodology for multi-parametric solution of chemical processes operation optimization under uncertainty

Chemical Engineering Journal

Research paper thumbnail of An Ensemble of Echo State Networks for Predicting the Energy Production of Wind Plants

Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference

Research paper thumbnail of Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines

Energies

This work proposes a data-driven methodology for identifying critical components in Complex Techn... more This work proposes a data-driven methodology for identifying critical components in Complex Technical Infrastructures (CTIs), for which the functional logic and/or the system structure functions are not known due the CTI’s complexity and evolving nature. The methodology uses large amounts of CTI monitoring data acquired over long periods of time and under different operating conditions. The critical components are identified as those for which the condition monitoring signals permit the optimal classification of the CTI functioning or failed state. The methodology includes two stages: in the first stage, a feature selection filter method based on the Relief technique is used to rank the monitoring signals according to their importance with respect to the CTI functioning or failed state; the second stage identifies the subset of signals among those highlighted by the Relief technique that are most informative with respect to the CTI state. This identification is performed on the basi...

Research paper thumbnail of A data-driven framework for identifying important components in complex systems

Reliability Engineering & System Safety

Research paper thumbnail of A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments

Energies

Condition monitoring (CM) in the energy industry is limited by the lack of pre-classified data ab... more Condition monitoring (CM) in the energy industry is limited by the lack of pre-classified data about the normal and/or abnormal plant states and the continuous evolution of its operational conditions. The objective is to develop a CM model able to: (1) Detect abnormal conditions and classify the type of anomaly; (2) recognize novel plant behaviors; (3) select representative examples of the novel classes for labeling by an expert; (4) automatically update the CM model. A CM model based on the never-ending learning paradigm is developed. It develops a dictionary containing labeled prototypical subsequences of signal values representing normal conditions and anomalies, which is continuously updated by using a dendrogram to identify groups of similar subsequences of novel classes and to select those subsequences to be labelled by an expert. A 1-nearest neighbor classifier is trained to online detect abnormal conditions and classify their types. The proposed CM model is applied to a synt...

Research paper thumbnail of Addendum: Termite, M.R. et al. A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments. Energies 2019, 12, 4802

Energies

The authors would like to add the following note to Figure 3 of their paper published in Energies... more The authors would like to add the following note to Figure 3 of their paper published in Energies [...]

Research paper thumbnail of A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments

IEEE Transactions on Neural Networks and Learning Systems

Research paper thumbnail of Uncertainty analysis in degradation modeling for maintenance policy assessment

Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

ABSTRACT We consider the problem of the evaluation of the maintenance policy of a component by me... more ABSTRACT We consider the problem of the evaluation of the maintenance policy of a component by means of degradation modeling. We assume that the stochastic laws governing the degradation process are uncertain, and so are the related parameters. We assume that the information available is in the form of qualitative judgment by an expert. We develop a representation framework based on possibility theory and the concept of fuzzy random variables. An example of application is given with reference to a medium-voltage circuit-breaker test facility.

Research paper thumbnail of Genetic algorithm-based wrapper approach for grouping condition monitoring signals of nuclear power plant components

Integrated Computer-Aided Engineering

Research paper thumbnail of An imprecision importance measure for uncertainty representations interpreted as lower and upper probabilities, with special emphasis on possibility theory

Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

Research paper thumbnail of Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Reliability Engineering & System Safety

Research paper thumbnail of Evolutionary fuzzy clustering for the Classification of transients in nuclear components

Progress in Nuclear Energy

Research paper thumbnail of A Fuzzy Logic–Based Model for the Classification of Faults in the Pump Seals of the Primary Heat Transport System of a CANDU 6 Reactor

Nuclear Science and Engineering

Research paper thumbnail of A Compacted Object Sample Extraction (COMPOSE)-based method for fault diagnostics in evolving environment

2015 Prognostics and System Health Management Conference (PHM), 2015

Research paper thumbnail of Analysis of the results of accelerated aging tests in Insulated Gate Bipolar Transistors

IEEE Transactions on Power Electronics, 2015

Research paper thumbnail of Uncertainty Representation and Propagation in the Prediction of Structural Response: A Comparison of Different …

Research paper thumbnail of Predicting the Resistance of Power Cables to Flame Propagation by Neural Networks (PSAM-0069)

Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM), 2006

Research paper thumbnail of A Filter Approach to the Selection of Features for Classification of Nuclear Transients

Research paper thumbnail of Reconstructing Signals for Sensor Validation by a GA-optimized Ensemble of PCA Models

Research paper thumbnail of Signal Grouping for Sensor Validation: a Multi-Objective Genetic Algorithm Approach

Research paper thumbnail of A machine learning-based methodology for multi-parametric solution of chemical processes operation optimization under uncertainty

Chemical Engineering Journal

Research paper thumbnail of An Ensemble of Echo State Networks for Predicting the Energy Production of Wind Plants

Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference

Research paper thumbnail of Identification of Critical Components in the Complex Technical Infrastructure of the Large Hadron Collider Using Relief Feature Ranking and Support Vector Machines

Energies

This work proposes a data-driven methodology for identifying critical components in Complex Techn... more This work proposes a data-driven methodology for identifying critical components in Complex Technical Infrastructures (CTIs), for which the functional logic and/or the system structure functions are not known due the CTI’s complexity and evolving nature. The methodology uses large amounts of CTI monitoring data acquired over long periods of time and under different operating conditions. The critical components are identified as those for which the condition monitoring signals permit the optimal classification of the CTI functioning or failed state. The methodology includes two stages: in the first stage, a feature selection filter method based on the Relief technique is used to rank the monitoring signals according to their importance with respect to the CTI functioning or failed state; the second stage identifies the subset of signals among those highlighted by the Relief technique that are most informative with respect to the CTI state. This identification is performed on the basi...

Research paper thumbnail of A data-driven framework for identifying important components in complex systems

Reliability Engineering & System Safety

Research paper thumbnail of A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments

Energies

Condition monitoring (CM) in the energy industry is limited by the lack of pre-classified data ab... more Condition monitoring (CM) in the energy industry is limited by the lack of pre-classified data about the normal and/or abnormal plant states and the continuous evolution of its operational conditions. The objective is to develop a CM model able to: (1) Detect abnormal conditions and classify the type of anomaly; (2) recognize novel plant behaviors; (3) select representative examples of the novel classes for labeling by an expert; (4) automatically update the CM model. A CM model based on the never-ending learning paradigm is developed. It develops a dictionary containing labeled prototypical subsequences of signal values representing normal conditions and anomalies, which is continuously updated by using a dendrogram to identify groups of similar subsequences of novel classes and to select those subsequences to be labelled by an expert. A 1-nearest neighbor classifier is trained to online detect abnormal conditions and classify their types. The proposed CM model is applied to a synt...

Research paper thumbnail of Addendum: Termite, M.R. et al. A Never-Ending Learning Method for Fault Diagnostics in Energy Systems Operating in Evolving Environments. Energies 2019, 12, 4802

Energies

The authors would like to add the following note to Figure 3 of their paper published in Energies... more The authors would like to add the following note to Figure 3 of their paper published in Energies [...]

Research paper thumbnail of A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments

IEEE Transactions on Neural Networks and Learning Systems

Research paper thumbnail of Uncertainty analysis in degradation modeling for maintenance policy assessment

Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

ABSTRACT We consider the problem of the evaluation of the maintenance policy of a component by me... more ABSTRACT We consider the problem of the evaluation of the maintenance policy of a component by means of degradation modeling. We assume that the stochastic laws governing the degradation process are uncertain, and so are the related parameters. We assume that the information available is in the form of qualitative judgment by an expert. We develop a representation framework based on possibility theory and the concept of fuzzy random variables. An example of application is given with reference to a medium-voltage circuit-breaker test facility.

Research paper thumbnail of Genetic algorithm-based wrapper approach for grouping condition monitoring signals of nuclear power plant components

Integrated Computer-Aided Engineering

Research paper thumbnail of An imprecision importance measure for uncertainty representations interpreted as lower and upper probabilities, with special emphasis on possibility theory

Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability

Research paper thumbnail of Availability assessment of oil and gas processing plants operating under dynamic Arctic weather conditions

Reliability Engineering & System Safety

Research paper thumbnail of Evolutionary fuzzy clustering for the Classification of transients in nuclear components

Progress in Nuclear Energy

Research paper thumbnail of A Fuzzy Logic–Based Model for the Classification of Faults in the Pump Seals of the Primary Heat Transport System of a CANDU 6 Reactor

Nuclear Science and Engineering

Research paper thumbnail of A Compacted Object Sample Extraction (COMPOSE)-based method for fault diagnostics in evolving environment

2015 Prognostics and System Health Management Conference (PHM), 2015

Research paper thumbnail of Analysis of the results of accelerated aging tests in Insulated Gate Bipolar Transistors

IEEE Transactions on Power Electronics, 2015

Research paper thumbnail of Uncertainty Representation and Propagation in the Prediction of Structural Response: A Comparison of Different …

Research paper thumbnail of Predicting the Resistance of Power Cables to Flame Propagation by Neural Networks (PSAM-0069)

Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM), 2006

Research paper thumbnail of A Filter Approach to the Selection of Features for Classification of Nuclear Transients

Research paper thumbnail of Reconstructing Signals for Sensor Validation by a GA-optimized Ensemble of PCA Models

Research paper thumbnail of Signal Grouping for Sensor Validation: a Multi-Objective Genetic Algorithm Approach

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