Diego Galar

144 papers receiving 2.3k citations

Diego Galar's Hit Papers

A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance 2021 · 244 citations
2440+2+5Years since publication100200300400

Peers

Diego Galar
Comparison fields: 5 of 122
  • Medical Laboratory Technology 230
  • Industrial and Manufacturing Engineering 499
  • Safety, Risk, Reliability and Quality 444
  • Management Information Systems 332
  • Control and Systems Engineering 794
Replace Tangbin Xia with:
Tangbin Xia China
Ershun Pan China
Benoît Iung France
Uday Kumar India
Marco Macchi Italy
Xiaoning Jin United States
Qing Chang United States
Tongdan Jin United States
Haitao Liao United States
Lifeng Xi China
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Countries citing papers authored by Diego Galar

Since Specialization
Citations

This map shows the geographic impact of Diego Galar'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 Diego Galar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Diego Galar more than expected).

Fields of papers citing papers by Diego Galar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Diego Galar. 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 Diego Galar. The network helps show where Diego Galar may publish in the future.

Co-authors

The 25 scholars most cited alongside Diego Galar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Diego Galar Line = papers co-authored together Diego Galar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 152 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0
Hit paper breakdown →
2018431
2
A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance
Hit paper breakdown →
2021244
3 2013138
4 2015107
5 201578
6 201570
7 202053
8 201652
9
Maintenance Decision Making based on different types of data fusion
201244
10 201742
11 201937
12 201737
13 201735
14 201833
15 202033
16 201230
17 201230
18 201430
19 201630
20 202129

About Diego Galar

Diego Galar is a scholar working on Control and Systems Engineering, Safety, Risk, Reliability and Quality, Mechanical Engineering, Industrial and Manufacturing Engineering and Medical Laboratory Technology, having authored 152 papers that have together received 2.5k indexed citations. Recurring topics across this work include Reliability and Maintenance Optimization (44 papers), Machine Fault Diagnosis Techniques (34 papers), Fault Detection and Control Systems (19 papers), Quality and Safety in Healthcare (19 papers), Life Cycle Costing Analysis (16 papers), Quality and Supply Management (11 papers), Risk and Safety Analysis (10 papers) and Railway Engineering and Dynamics (10 papers). The work is most often cited by research in Medical Laboratory Technology (230 citations), Industrial and Manufacturing Engineering (499 citations), Safety, Risk, Reliability and Quality (444 citations), Management Information Systems (332 citations) and Control and Systems Engineering (794 citations). Diego Galar has collaborated with scholars based in Sweden, Spain and Italy. Frequent co-authors include Uday Kumar, Alberto Diez-Olivan, Basilio Sierra, Javier Del Ser, Christer Stenström, Aditya Parida, Lorenzo Ciani, Uday Kumar, Marcantonio Catelani and Jing Lin. Their work appears in journals such as Journal of Quality in Maintenance Engineering, Insight - Non-Destructive Testing and Condition Monitoring, Eksploatacja i Niezawodnosc - Maintenance and Reliability, IEEE Access and Measurement.

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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