Science Explorer Interactive view Map
Topic · Control and Systems Engineering

Machine Fault Diagnosis Techniques

Machine Fault Diagnosis Techniques is a research topic within Control and Systems Engineering. Science Explorer counts 31k research works in it since 1950. 23.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on machine fault diagnosis and prognostics using methods such as Empirical Mode Decomposition, wavelet transform, and deep learning. It covers topics like condition monitoring, vibration analysis, and remaining useful life estimation for rotating machinery. The research explores the application of machine learning techniques, neural networks, and signal processing in fault detection and health management of various mechanical systems.

  • Empirical Mode Decomposition
  • Fault Diagnosis
  • Machine Learning
  • Condition Monitoring
  • Vibration Analysis
  • Deep Learning
  • Remaining Useful Life Estimation
  • Wavelet Transform
  • Rotating Machinery
  • Neural Networks
Research works
31k
fractional, since 1950
In the world top 10%
7.2k
per year above
Top-10% rate
23.0%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+86%
the tick is no change

Which countries lead Machine Fault Diagnosis Techniques research?

By volume, China and India publish the most (6.5k and 713 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 6.5k works
  2. 2 India 713 works
  3. 3 United States 427 works
  4. 4 United Kingdom 225 works
  5. 5 South Korea 220 works
  6. 6 Germany 165 works
  7. 7 Italy 165 works
  8. 8 France 157 works
  9. 9 Spain 142 works
  10. 10 Canada 138 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 73.4%India: 8.1%United States: 4.8%United Kingdom: 2.5%6 others listed: 11.2%73%largest
China6,490 · 73.4%India713 · 8.1%United States427 · 4.8%United Kingdom225 · 2.5%6 others listed986 · 11.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Machine Fault Diagnosis Techniques research?

By volume in 2022–2025, Xi'an Jiaotong University publishes the most Machine Fault Diagnosis Techniques research, followed by Chongqing University and Southwest Jiaotong University.

By volume, 2022–2025

  1. 1 Xi'an Jiaotong UniversityChina 169 works
  2. 2 Chongqing UniversityChina 121 works
  3. 3 Southwest Jiaotong UniversityChina 110 works
  4. 4 Shanghai Jiao Tong UniversityChina 107 works
  5. 5 Nanjing University of Aeronautics and AstronauticsChina 101 works
  6. 6 Northwestern Polytechnical UniversityChina 101 works
  7. 7 Huazhong University of Science and TechnologyChina 100 works
  8. 8 Beihang UniversityChina 96 works
  9. 9 North China Electric Power UniversityChina 92 works
  10. 10 Harbin Institute of TechnologyChina 92 works

Who are the leading researchers in Machine Fault Diagnosis Techniques?

The most-cited researchers publishing on Machine Fault Diagnosis Techniques include David L. Donoho, Zidong Wang and Richard G. Baraniuk.

  1. 1 David L. Donoho United States 4.7k citations
  2. 2 Zidong Wang United Kingdom 4.4k citations
  3. 3 Richard G. Baraniuk United States 4.4k citations
  4. 4 Bhim Singh India 3.3k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Machine Fault Diagnosis Techniques research done?

The largest centres of Machine Fault Diagnosis Techniques research in 2022–2025 are Beijing (China), Xi'an (China), Shanghai (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Huddersfield, Qinhuangdao and Ma'anshan City.

Largest cities, 2022–2025

  1. 1 Beijing China 1k works
  2. 2 Xi'an China 502 works
  3. 3 Shanghai China 428 works
  4. 4 Nanjing China 350 works
  5. 5 Wuhan China 341 works
  6. 6 Chengdu China 253 works
  7. 7 Chongqing China 228 works
  8. 8 Shenyang China 201 works
  9. 9 Harbin China 192 works
  10. 10 Changsha China 190 works

Where it is the local speciality

  1. HuddersfieldGB · 20.1 works16×
  2. QinhuangdaoCN · 63.9 works15×
  3. Ma'anshan CityCN · 28.3 works14×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Machine Fault Diagnosis Techniques than the world average.

See Machine Fault Diagnosis Techniques on the map

Where is the best place to study Machine Fault Diagnosis Techniques?

Among universities, judged by research, Xi'an Jiaotong University, Chongqing University and Southwest Jiaotong University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%60%mean 35.6%fractional works in this node (log) →share in the world top 10% →Xi'an Jiaotong University: 169, 38.4%Chongqing University: 121, 44.2%Southwest Jiaotong University: 110, 30.0%Yanshan University: 61, 51.0%Nanjing University of Aeronautics and Astronautics: 101, 24.7%Wenzhou University: 28, 37.0%Beijing Jiaotong University: 81, 29.9%Shandong University of Science and Technology: 51, 23.2%University of Strathclyde: 18, 49.7%Anhui University of Technology: 28, 27.9%Yanshan UniversityChongqing UniversityXi'an Jiaotong Unive…Southwest Jiaotong U…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Xi'an Jiaotong UniversityChina 79.438.4%9.9×169 +131.2%
2 Chongqing UniversityChina 76.244.2%10.5×121 +51.3%
3 Southwest Jiaotong UniversityChina 73.130.0%13.5×110 +160.2%
4 Yanshan UniversityChina 72.851.0%17.1×61 +14.0%
5 Nanjing University of Aeronautics and AstronauticsChina 71.624.7%10.7×101 +259.0%
6 Wenzhou UniversityChina 71.337.0%12.3×28 +459.1%
7 Beijing Jiaotong UniversityChina 69.029.9%12.5×81 +127.7%
8 Shandong University of Science and TechnologyChina 65.423.2%9.8×51 +285.7%
9 University of StrathclydeUnited Kingdom 65.149.7%6.3×18 +135.1%
10 Anhui University of TechnologyChina 64.927.9%14.3×28 +260.3%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Machine Fault Diagnosis Techniques research growing?

Output in 2018–2022 was 86% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Machine Fault Diagnosis Techniques.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.