latest.bib

@article{LosVen21-JEGTP-IJ,
  author = {Losi, Enzo and Venturini, Mauro and Manservigi, Lucrezia and Ceschini, Giuseppe Fabio and Bechini, Giovanni and Cota, Giuseppe and Riguzzi, Fabrizio},
  title = {Structured Methodology for Clustering Gas Turbine Transients by means of Multi-variate Time Series},
  year = {2021},
  publisher = {ASME},
  journal = {Journal of Engineering for Gas Turbines and Power},
  volume = {143},
  number = {3},
  pages = {031014-1 (13 pages)},
  doi = {10.1115/1.4049503}
}
@article{CheCotGavLamMelRig20-EAAI-IJ,
  author = {Federico Chesani and
               Giuseppe Cota and
               Marco Gavanelli and
               Evelina Lamma and
               Paola Mello and
               Fabrizio Riguzzi},
  title = {Declarative and Mathematical Programming approaches to Decision Support
               Systems for food recycling},
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {95},
  pages = {103861},
  year = {2020},
  doi = {10.1016/j.engappai.2020.103861},
  scopus = {2-s2.0-85089188550}
}
@inproceedings{LosVen20-TurboExpo-IC,
  author = {Losi, Enzo and Venturini, Mauro and Manservigi, Lucrezia and Ceschini, Giusppe Fabio and Bechini, Giovanni. and Cota, Giuseppe and Riguzzi, Fabrizio},
  title = {Structured Methodology for Clustering Gas Turbine Transients by means of Multi-variate Time Series},
  year = {2020},
  publisher = {ASME},
  booktitle = {Proc. ASME Turbo Expo 2020},
  abstract = {The challenges related to current energy market force gas turbine owners to improve the reliability and availability of gas turbine engines, especially in the ever competitive market of the Oil & Gas sector. Gas turbine trip leads to business interruption and also reduces equipment remaining useful life. Thus, the identification of symptoms of trips is a key factor to predict their occurrence and avoid further damages and costs. Gas turbine transients are tracked by gas turbine operators while they occur, but a database including the complete details of past events for many fleets of engines is not always available. Therefore, a methodology aimed at classifying transients into clusters that identify the type of event (e.g., normal shutdown or trip) is required. Clustering is a data mining technique that addresses the scope of partitioning multi-variate time series into a given number of homogeneous and separated groups. In such a manner, the multi-variate time series belonging to the same cluster are very similar to each other and dissimilar to those of the other clusters.
This paper presents a structured methodology composed of a subsequent matching algorithm, a featured-based clustering approach exploiting the unsupervised fuzzy C-means algorithm and a procedure that assigns a label to each cluster for classification purposes. The methodology is applied to a real-word case-study, by investigating transients acquired from a fleet of Siemens gas turbines in operation during three years.
The results obtained by using heterogeneous datasets including six measured variables allowed values of Precision, Recall and Accuracy higher than 90 % in almost all cases.},
  volume = {Volume 9: Oil and Gas Applications; Organic Rankine Cycle Power Systems; Steam Turbine},
  series = {Turbo Expo: Power for Land, Sea, and Air},
  pages = {1--16},
  note = {V009T21A005},
  doi = {10.1115/GT2020-14751}
}
@incollection{CotZesBelLamRig20-SSWS-BC,
  title = {A Framework for Reasoning on Probabilistic Description Logics},
  author = {Cota, Giuseppe and Zese, Riccardo and Bellodi, Elena and Lamma, Evelina and Riguzzi, Fabrizio},
  booktitle = {Applications and Practices in Ontology Design, Extraction, and Reasoning},
  series = {Studies on the Semantic Web},
  volume = {49},
  editor = {Cota, Giuseppe and Daquino, Marilena and Pozzato, Gian Luca},
  isbn = {978-1-64368-142-9},
  doi = {10.3233/SSW200040},
  language = {English},
  pages = {127-144},
  year = {2020},
  publisher = {{IOS} Press},
  abstract = {While there exist several reasoners for Description Logics, very few of them can cope with uncertainty. BUNDLE is an inference framework that can exploit several OWL (non-probabilistic) reasoners to perform inference over Probabilistic Description Logics.
	In this chapter, we report the latest advances implemented in BUNDLE. In particular, BUNDLE can now interface with the reasoners of the TRILL system, thus providing a uniform method to execute probabilistic queries using different settings. BUNDLE can be easily extended and can be used either as a standalone desktop application or as a library in OWL API-based applications that need to reason over Probabilistic Description Logics.
	The reasoning performance heavily depends on the reasoner and method used to compute the probability. We provide a comparison of the different reasoning settings on several datasets.
	},
  copyright = {Akademische Verlagsgesellschaft AKA GmbH, Berlin}
}

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