Piero Baraldi - Profile on Academia.edu (original) (raw)
Papers by Piero Baraldi
A machine learning-based methodology for multi-parametric solution of chemical processes operation optimization under uncertainty
Chemical Engineering Journal
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
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...
A data-driven framework for identifying important components in complex systems
Reliability Engineering & System Safety
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...
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 [...]
A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments
IEEE Transactions on Neural Networks and Learning Systems
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.
Integrated Computer-Aided Engineering
Equipment condition monitoring of nuclear power plants requires to optimally group the usually ve... more Equipment condition monitoring of nuclear power plants requires to optimally group the usually very large number of signals and to develop for each identified group a separate condition monitoring model. In this paper we propose an approach to optimally group the signals. We use a Genetic Algorithm (GA) for the optimization of the groups; the decision variables of the optimization problem relate to the composition of the groups (i.e., which signals they contain) and the objective function (fitness) driving the search for the optimal grouping is constructed in terms of quantitative indicators of the performances of the condition monitoring models themselves: in this sense, the GA search engine is a wrapper around the condition monitoring models. A real case study is considered, concerning the condition monitoring of the Reactor Coolant Pump (RCP) of a Pressurized Water Reactor (PWR). The optimization results are evaluated with respect to the accuracy and robustness of the monitored signals estimates. The condition monitoring models built on the groups found by the proposed approach outperform the model which uses all available signals, whereas they perform similarly to the models built on groups based on signal correlation. However, these latter do not guarantee the robustness of the reconstruction in case of abnormal conditions and require to a priori fix characteristics of the groups, such as the desired minimum correlation value in a group.
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Uncertainty importance measures typically reflect the degree to which uncertainty about risk and ... more Uncertainty importance measures typically reflect the degree to which uncertainty about risk and reliability parameters at the component level influences uncertainty about parameters at the system level. The definition of these measures is typically founded on a Bayesian perspective where subjective probabilities are used to express epistemic uncertainty; hence, they do not reflect the effect of imprecision in probability assignments, as captured by alternative uncertainty representation frameworks such as imprecise probability, possibility theory and evidence theory. In the present paper, we define an imprecision importance measure to evaluate the effect of removing imprecision to the extent that a probabilistic representation of uncertainty remains, as well as to the extent that no epistemic uncertainty remains. Possibility theory is highlighted throughout the paper as an example of an uncertainty representation reflecting imprecision, and used in particular in two numerical examples which are included for illustration.
Reliability Engineering & System Safety
We consider the assessment of the availability of oil and gas processing facilities operating und... more We consider the assessment of the availability of oil and gas processing facilities operating under Arctic conditions. The novelty of the work lies in modelling the time-dependent effects of environmental conditions on the components failure and repair rates. This is done by introducing weather-dependent multiplicative factors, which can be estimated by expert judgements given the scarce data available from Arctic offshore operations. System availability is assessed considering the equivalent age of the components to account for the impacts of harsh operating conditions on component life history and maintenance duration. The application of the model by direct Monte Carlo simulation is illustrated on an oil processing train operating in Arctic offshore. A scheduled preventive maintenance task is considered to cope with the potential reductions in system availability under harsh operating conditions.
Evolutionary fuzzy clustering for the Classification of transients in nuclear components
Progress in Nuclear Energy
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
A Compacted Object Sample Extraction (COMPOSE)-based method for fault diagnostics in evolving environment
2015 Prognostics and System Health Management Conference (PHM), 2015
Analysis of the results of accelerated aging tests in Insulated Gate Bipolar Transistors
IEEE Transactions on Power Electronics, 2015
Uncertainty Representation and Propagation in the Prediction of Structural Response: A Comparison of Different …
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
This paper describes a feature selection algorithm based on the extension to the transient case o... more This paper describes a feature selection algorithm based on the extension to the transient case of a classifiability index directly computable from the plant measured data. Feature selection is used to filter out irrelevant or redundant features, and this is particularly important for nuclear power plants, where hundreds of parameters are monitored for operation and safety reasons, and where expert judgment alone might not effectively drive the feature selection.
The right to utilise information originating from the research work of the Halden Project is limi... more The right to utilise information originating from the research work of the Halden Project is limited to persons and undertakings specifically given the right by one of the Project member organisations in accordance with the Project's rules for "Communication of Results of Scientific Research and Information". The content of this report should thus neither be disclosed to others nor be reproduced, wholly or partially, unless written permission to do so has been obtained from the appropriate Project member organisation.
The right to utilise information originating from the research work of the Halden Project is limi... more The right to utilise information originating from the research work of the Halden Project is limited to persons and undertakings specifically given the right by one of the Project member organisations in accordance with the Project's rules for "Communication of Results of Scientific Research and Information". The content of this report should thus neither be disclosed to others nor be reproduced, wholly or partially, unless written permission to do so has been obtained from the appropriate Project member organisation.
A machine learning-based methodology for multi-parametric solution of chemical processes operation optimization under uncertainty
Chemical Engineering Journal
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
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...
A data-driven framework for identifying important components in complex systems
Reliability Engineering & System Safety
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...
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 [...]
A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments
IEEE Transactions on Neural Networks and Learning Systems
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.
Integrated Computer-Aided Engineering
Equipment condition monitoring of nuclear power plants requires to optimally group the usually ve... more Equipment condition monitoring of nuclear power plants requires to optimally group the usually very large number of signals and to develop for each identified group a separate condition monitoring model. In this paper we propose an approach to optimally group the signals. We use a Genetic Algorithm (GA) for the optimization of the groups; the decision variables of the optimization problem relate to the composition of the groups (i.e., which signals they contain) and the objective function (fitness) driving the search for the optimal grouping is constructed in terms of quantitative indicators of the performances of the condition monitoring models themselves: in this sense, the GA search engine is a wrapper around the condition monitoring models. A real case study is considered, concerning the condition monitoring of the Reactor Coolant Pump (RCP) of a Pressurized Water Reactor (PWR). The optimization results are evaluated with respect to the accuracy and robustness of the monitored signals estimates. The condition monitoring models built on the groups found by the proposed approach outperform the model which uses all available signals, whereas they perform similarly to the models built on groups based on signal correlation. However, these latter do not guarantee the robustness of the reconstruction in case of abnormal conditions and require to a priori fix characteristics of the groups, such as the desired minimum correlation value in a group.
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability
Uncertainty importance measures typically reflect the degree to which uncertainty about risk and ... more Uncertainty importance measures typically reflect the degree to which uncertainty about risk and reliability parameters at the component level influences uncertainty about parameters at the system level. The definition of these measures is typically founded on a Bayesian perspective where subjective probabilities are used to express epistemic uncertainty; hence, they do not reflect the effect of imprecision in probability assignments, as captured by alternative uncertainty representation frameworks such as imprecise probability, possibility theory and evidence theory. In the present paper, we define an imprecision importance measure to evaluate the effect of removing imprecision to the extent that a probabilistic representation of uncertainty remains, as well as to the extent that no epistemic uncertainty remains. Possibility theory is highlighted throughout the paper as an example of an uncertainty representation reflecting imprecision, and used in particular in two numerical examples which are included for illustration.
Reliability Engineering & System Safety
We consider the assessment of the availability of oil and gas processing facilities operating und... more We consider the assessment of the availability of oil and gas processing facilities operating under Arctic conditions. The novelty of the work lies in modelling the time-dependent effects of environmental conditions on the components failure and repair rates. This is done by introducing weather-dependent multiplicative factors, which can be estimated by expert judgements given the scarce data available from Arctic offshore operations. System availability is assessed considering the equivalent age of the components to account for the impacts of harsh operating conditions on component life history and maintenance duration. The application of the model by direct Monte Carlo simulation is illustrated on an oil processing train operating in Arctic offshore. A scheduled preventive maintenance task is considered to cope with the potential reductions in system availability under harsh operating conditions.
Evolutionary fuzzy clustering for the Classification of transients in nuclear components
Progress in Nuclear Energy
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
A Compacted Object Sample Extraction (COMPOSE)-based method for fault diagnostics in evolving environment
2015 Prognostics and System Health Management Conference (PHM), 2015
Analysis of the results of accelerated aging tests in Insulated Gate Bipolar Transistors
IEEE Transactions on Power Electronics, 2015
Uncertainty Representation and Propagation in the Prediction of Structural Response: A Comparison of Different …
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
This paper describes a feature selection algorithm based on the extension to the transient case o... more This paper describes a feature selection algorithm based on the extension to the transient case of a classifiability index directly computable from the plant measured data. Feature selection is used to filter out irrelevant or redundant features, and this is particularly important for nuclear power plants, where hundreds of parameters are monitored for operation and safety reasons, and where expert judgment alone might not effectively drive the feature selection.
The right to utilise information originating from the research work of the Halden Project is limi... more The right to utilise information originating from the research work of the Halden Project is limited to persons and undertakings specifically given the right by one of the Project member organisations in accordance with the Project's rules for "Communication of Results of Scientific Research and Information". The content of this report should thus neither be disclosed to others nor be reproduced, wholly or partially, unless written permission to do so has been obtained from the appropriate Project member organisation.
The right to utilise information originating from the research work of the Halden Project is limi... more The right to utilise information originating from the research work of the Halden Project is limited to persons and undertakings specifically given the right by one of the Project member organisations in accordance with the Project's rules for "Communication of Results of Scientific Research and Information". The content of this report should thus neither be disclosed to others nor be reproduced, wholly or partially, unless written permission to do so has been obtained from the appropriate Project member organisation.