2021 |
Wachulec, Małgorzata; Luckner, Marcin Fault detection of jet engine heat sensor Journal Article Procedia Computer Science, 192 , pp. 844–852, 2021, ISSN: 18770509. Abstract | Links | BibTeX | Tagi: Anomaly detection, Oil temperature sensor, Outlier detection, Sister engines, Time series @article{Wachulec2021, title = {Fault detection of jet engine heat sensor}, author = {Małgorzata Wachulec and Marcin Luckner}, url = {https://doi.org/10.1016/j.procs.2021.08.087}, doi = {10.1016/j.procs.2021.08.087}, issn = {18770509}, year = {2021}, date = {2021-01-01}, journal = {Procedia Computer Science}, volume = {192}, pages = {844--852}, publisher = {Elsevier B.V.}, abstract = {This paper presents an algorithm predicting oil level and temperature sensor (OLTS) failure to replace it before it carries serious costs. OLTS sensor showing too high oil temperature cockpit indications is a driver of significant air turnback events and commanded in-flight shutdown (IFSD). A prediction of sensor malfunction is possible, but an operator requires at least 11 months of historical data. The developed algorithm automates the process of identifying potential failures using a data-driven, dissimilarity based model. It calculates the rolling mean of the oil temperature difference between sister engines for short-term and long-term periods (counted in flights). If the difference between the short-term and long-term means is greater than a set threshold at least confirmation window times, it sets an alert. The proposed model requires less than three months of data to detect the malfunction, with the final F1 score measured on the test set equal to 0.71.}, keywords = {Anomaly detection, Oil temperature sensor, Outlier detection, Sister engines, Time series}, pubstate = {published}, tppubtype = {article} } This paper presents an algorithm predicting oil level and temperature sensor (OLTS) failure to replace it before it carries serious costs. OLTS sensor showing too high oil temperature cockpit indications is a driver of significant air turnback events and commanded in-flight shutdown (IFSD). A prediction of sensor malfunction is possible, but an operator requires at least 11 months of historical data. The developed algorithm automates the process of identifying potential failures using a data-driven, dissimilarity based model. It calculates the rolling mean of the oil temperature difference between sister engines for short-term and long-term periods (counted in flights). If the difference between the short-term and long-term means is greater than a set threshold at least confirmation window times, it sets an alert. The proposed model requires less than three months of data to detect the malfunction, with the final F1 score measured on the test set equal to 0.71. |
2013 |
Luckner, Marcin; Filasiak, Robert Reference data sets for spam detection: Creation, analysis, propagation Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 212–221, 2013, ISSN: 03029743. Abstract | Links | BibTeX | Tagi: Anomaly detection, Flow analysis, Hybrid classifiers, Reference sets, Spam detection @inproceedings{Luckner2013, title = {Reference data sets for spam detection: Creation, analysis, propagation}, author = {Marcin Luckner and Robert Filasiak}, doi = {10.1007/978-3-642-40846-5_22}, issn = {03029743}, year = {2013}, date = {2013-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8073 LNAI}, pages = {212--221}, abstract = {A reference set is a set of data of network traffic whose form and content allows detecting an event or a group of events. Realistic and representative datasets based on real traffic can improve research in the fields of intruders and anomaly detection. Creating reference sets tackles a number of issues such as the collection and storage of large volumes of data, the privacy of information and the relevance of collected events. Moreover, rare events are hard to analyse among background traffic and need specialist detection tools. One of the common problems that can be detected in network traffic is spam. This paper presents the methodology for creating a network traffic reference set for spam detection. The methodology concerns the selection of significant features, the collection and storage of data, the analysis of the collected data, the enrichment of the data with additional events and the propagation of the set. Moreover, a hybrid classifier that detects spam on relatively high level is presented. textcopyright 2013 Springer-Verlag.}, keywords = {Anomaly detection, Flow analysis, Hybrid classifiers, Reference sets, Spam detection}, pubstate = {published}, tppubtype = {inproceedings} } A reference set is a set of data of network traffic whose form and content allows detecting an event or a group of events. Realistic and representative datasets based on real traffic can improve research in the fields of intruders and anomaly detection. Creating reference sets tackles a number of issues such as the collection and storage of large volumes of data, the privacy of information and the relevance of collected events. Moreover, rare events are hard to analyse among background traffic and need specialist detection tools. One of the common problems that can be detected in network traffic is spam. This paper presents the methodology for creating a network traffic reference set for spam detection. The methodology concerns the selection of significant features, the collection and storage of data, the analysis of the collected data, the enrichment of the data with additional events and the propagation of the set. Moreover, a hybrid classifier that detects spam on relatively high level is presented. textcopyright 2013 Springer-Verlag. |
Luckner, Marcin; Filasiak, Robert Reference data sets for spam detection: Creation, analysis, propagation Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 212–221, 2013, ISSN: 03029743. Abstract | Links | BibTeX | Tagi: Anomaly detection, Flow analysis, Hybrid classifiers, Reference sets, Spam detection @inproceedings{Luckner2013f, title = {Reference data sets for spam detection: Creation, analysis, propagation}, author = {Marcin Luckner and Robert Filasiak}, doi = {10.1007/978-3-642-40846-5_22}, issn = {03029743}, year = {2013}, date = {2013-01-01}, booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}, volume = {8073 LNAI}, pages = {212--221}, abstract = {A reference set is a set of data of network traffic whose form and content allows detecting an event or a group of events. Realistic and representative datasets based on real traffic can improve research in the fields of intruders and anomaly detection. Creating reference sets tackles a number of issues such as the collection and storage of large volumes of data, the privacy of information and the relevance of collected events. Moreover, rare events are hard to analyse among background traffic and need specialist detection tools. One of the common problems that can be detected in network traffic is spam. This paper presents the methodology for creating a network traffic reference set for spam detection. The methodology concerns the selection of significant features, the collection and storage of data, the analysis of the collected data, the enrichment of the data with additional events and the propagation of the set. Moreover, a hybrid classifier that detects spam on relatively high level is presented. textcopyright 2013 Springer-Verlag.}, keywords = {Anomaly detection, Flow analysis, Hybrid classifiers, Reference sets, Spam detection}, pubstate = {published}, tppubtype = {inproceedings} } A reference set is a set of data of network traffic whose form and content allows detecting an event or a group of events. Realistic and representative datasets based on real traffic can improve research in the fields of intruders and anomaly detection. Creating reference sets tackles a number of issues such as the collection and storage of large volumes of data, the privacy of information and the relevance of collected events. Moreover, rare events are hard to analyse among background traffic and need specialist detection tools. One of the common problems that can be detected in network traffic is spam. This paper presents the methodology for creating a network traffic reference set for spam detection. The methodology concerns the selection of significant features, the collection and storage of data, the analysis of the collected data, the enrichment of the data with additional events and the propagation of the set. Moreover, a hybrid classifier that detects spam on relatively high level is presented. textcopyright 2013 Springer-Verlag. |
Publikacje
2021 |
Fault detection of jet engine heat sensor Journal Article Procedia Computer Science, 192 , pp. 844–852, 2021, ISSN: 18770509. |
2013 |
Reference data sets for spam detection: Creation, analysis, propagation Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 212–221, 2013, ISSN: 03029743. |
Reference data sets for spam detection: Creation, analysis, propagation Inproceedings Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 212–221, 2013, ISSN: 03029743. |