Thomas Palmé

475 total citations
18 papers, 362 citations indexed

About

Thomas Palmé is a scholar working on Control and Systems Engineering, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Thomas Palmé has authored 18 papers receiving a total of 362 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Control and Systems Engineering, 6 papers in Artificial Intelligence and 5 papers in Automotive Engineering. Recurrent topics in Thomas Palmé's work include Fault Detection and Control Systems (9 papers), Machine Fault Diagnosis Techniques (6 papers) and Machine Learning and ELM (5 papers). Thomas Palmé is often cited by papers focused on Fault Detection and Control Systems (9 papers), Machine Fault Diagnosis Techniques (6 papers) and Machine Learning and ELM (5 papers). Thomas Palmé collaborates with scholars based in Switzerland, Norway and Sweden. Thomas Palmé's co-authors include Magnus Fast, Olga Fink, Marcus Thern, Gabriel Michau, Yang Hu, Yang Hu, Peter Breuhaus, Mohsen Assadi, Yang Hu and Marion Chartier and has published in prestigious journals such as New Phytologist, Applied Energy and Energy.

In The Last Decade

Thomas Palmé

18 papers receiving 339 citations

Peers

Countries citing papers authored by Thomas Palmé

Since Specialization

Citations

This map shows the geographic impact of Thomas Palmé's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Thomas Palmé with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Palmé more than expected).

Fields of papers citing papers by Thomas Palmé

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas Palmé. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Thomas Palmé. The network helps show where Thomas Palmé may publish in the future.

Co-authorship network of co-authors of Thomas Palmé

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Palmé. A scholar is included among the top collaborators of Thomas Palmé based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thomas Palmé. Thomas Palmé is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

#	Work	Indexed citations
1	Physics-Informed Machine Learning for Predictive Maintenance: Applied Use-Cases 2023 ·Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) ·(unknown), Thomas Palmé, Manuel Arias Chao	3
2	Global patterns and a latitudinal gradient of flower disparity: perspectives from the angiosperm order Ericales 2021 ·New Phytologist ·Marion Chartier, Maria von Balthazar, Susanne Sontag, Stefan Löfstrand, Thomas Palmé, Florian Jabbour, Hervé Sauquet, Jürg Schönenberger	21
3	How COVID-19 Will Shape Urban Mobility 2020 ·(unknown), (unknown), (unknown), David Hornstein, (unknown), Thomas Palmé	9
4	Feature learning for fault detection in high-dimensional condition monitoring signals 2019 ·Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ·Gabriel Michau, Yang Hu, Thomas Palmé, Olga Fink	36
5	Fleet PHM for Critical Systems: Bi-level Deep Learning Approach for Fault Detection 2018 ·Repository for Publications and Research Data (ETH Zurich) ·Gabriel Michau, Thomas Palmé, Olga Fink	13
6	Deep Health Indicator Extraction: A Method based on Auto-encoders and Extreme Learning Machines 2016 ·Annual Conference of the PHM Society ·Yang Hu, Thomas Palmé, Olga Fink	10
7	Online sequential extreme learning machines for fault detection 2016 ·Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) ·Yang Hu, Olga Fink, Thomas Palmé	2
8	Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines 2016 ·Engineering Applications of Artificial Intelligence ·Yang Hu, Thomas Palmé, Olga Fink	29
9	Deep health indicator extraction : a method based on auto-encoders and extreme learning machines 2016 ·Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) ·Yang Hu, Thomas Palmé, Olga Fink	8
10	Hybrid Modeling of Heavy Duty Gas Turbines for On-Line Performance Monitoring 2014 ·Thomas Palmé, (unknown), (unknown)	9
11	Similarity Based Modeling for Turbine Exit Temperature Spread Monitoring on Gas Turbines 2013 ·Thomas Palmé, (unknown), (unknown)	4
12	Compressor map prediction by neural networks 2012 ·RWTH Publications (RWTH Aachen) ·Thomas Palmé, (unknown), (unknown), Mohsen Assadi, Peter Jeschke	1
13	New Alstom Monitoring Tools Leveraging Artificial Neural Network Technologies 2011 ·Thomas Palmé, Peter Breuhaus, Mohsen Assadi, (unknown), (unknown)	15
14	Early Warning of Gas Turbine Failure by Nonlinear Feature Extraction Using an Auto-Associative Neural Network Approach 2011 ·Thomas Palmé, Peter Breuhaus, Mohsen Assadi, (unknown), (unknown)	15
15	Gas turbine sensor validation through classification with artificial neural networks 2011 ·Applied Energy ·Thomas Palmé, Magnus Fast, Marcus Thern	67
16	Different Condition Monitoring Models for Gas Turbines by Means of Artificial Neural Networks 2009 ·Thomas Palmé, Magnus Fast, Mohsen Assadi, (unknown), Peter Breuhaus	6
17	A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network 2009 ·Lund University Publications (Lund University) ·Magnus Fast, Thomas Palmé, Magnus Genrup	9
18	Application of artificial neural networks to the condition monitoring and diagnosis of a combined heat and power plant 2009 ·Energy ·Magnus Fast, Thomas Palmé	105

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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