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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. 9.8% 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%
3.1k
per year above
Top-10% rate
9.8%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+81%
the tick is no change
Largest producer
China
74% of the 10 largest, 2022-now
Machine Fault Diagnosis Techniques

Which countries lead Machine Fault Diagnosis Techniques research?

By volume, China and India publish the most (8.1k and 911 works in 2022-now).

By volume, 2022-now

  1. 1 China 8.1k works
  2. 2 India 911 works
  3. 3 United States 550 works
  4. 4 United Kingdom 275 works
  5. 5 South Korea 273 works
  6. 6 Germany 211 works
  7. 7 Italy 199 works
  8. 8 France 177 works
  9. 9 Brazil 172 works
  10. 10 Spain 166 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: 73.5%India: 8.2%United States: 5.0%United Kingdom: 2.5%6 others listed: 10.8%74%largest
China8,144 · 73.5%India911 · 8.2%United States550 · 5.0%United Kingdom275 · 2.5%6 others listed1,196 · 10.8%

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

Which institutions lead Machine Fault Diagnosis Techniques research?

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

By volume, 2022-now

  1. 1 Xi'an Jiaotong UniversityChina 205 works
  2. 2 Chongqing UniversityChina 146 works
  3. 3 Southwest Jiaotong UniversityChina 130 works
  4. 4 Shanghai Jiao Tong UniversityChina 130 works
  5. 5 Nanjing University of Aeronautics and AstronauticsChina 123 works
  6. 6 Northwestern Polytechnical UniversityChina 119 works
  7. 7 Huazhong University of Science and TechnologyChina 116 works
  8. 8 North China Electric Power UniversityChina 112 works
  9. 9 Beihang UniversityChina 112 works
  10. 10 Harbin Institute of TechnologyChina 111 works

Who are the leading researchers in Machine Fault Diagnosis Techniques?

The most-cited researchers publishing on Machine Fault Diagnosis Techniques include Kai You Wang, Frede Blaabjerg and Han Zhang.

  1. 1 Kai You Wang China 224k citations
  2. 2 Frede Blaabjerg Denmark 203k citations
  3. 3 Han Zhang China 161k citations
  4. 4 Seyedali Mirjalili Australia 154k citations
  5. 5 David L. Donoho United States 154k citations
  6. 6 Huan Liu Australia 148k citations

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

What are the most cited papers in Machine Fault Diagnosis Techniques?

The most cited paper in Machine Fault Diagnosis Techniques is "Matching pursuits with time-frequency dictionaries" (Stéphane Mallat, Zhifeng Zhang, 1993), with 9,224 citations. Next come "Variational Mode Decomposition" (9,123) and "ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD" (8,819).

Most cited papers
#PaperReleasedCitations
1 Matching pursuits with time-frequency dictionaries Stéphane Mallat, Zhifeng Zhang · IEEE Transactions on Signal Processing 1993 9,224
2 Variational Mode Decomposition Konstantin Dragomiretskiy, Dominique Zosso · IEEE Transactions on Signal Processing 24 Jan 2014 9,123
3 ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD Zhaohua Wu, Norden Eh Huang · Advances in Adaptive Data Analysis 17 Oct 2008 8,819
4 The wavelet transform, time-frequency localization and signal analysis Ingrid C. Daubechies · IEEE Transactions on Information Theory 1990 6,510
5 A review on machinery diagnostics and prognostics implementing condition-based maintenance Andrew K. S. Jardine, Daming Lin, Dragan Banjević · Mechanical Systems and Signal Processing 4 Nov 2005 4,775
All 100 most cited papers, with filters and sorting

Where is Machine Fault Diagnosis Techniques research done?

The largest centres of Machine Fault Diagnosis Techniques research in 2022-now 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, Ma'anshan City and Qinhuangdao.

Largest cities, 2022-now

  1. 1 BeijingChina 1.2k works
  2. 2 Xi'anChina 610 works
  3. 3 ShanghaiChina 531 works
  4. 4 NanjingChina 426 works
  5. 5 WuhanChina 407 works
  6. 6 ChengduChina 311 works
  7. 7 ChongqingChina 281 works
  8. 8 ShenyangChina 250 works
  9. 9 HarbinChina 244 works
  10. 10 ChangshaChina 235 works

Where it is the local speciality

  1. HuddersfieldGB · 24.3 works15×
  2. Ma'anshan CityCN · 37.0 works15×
  3. QinhuangdaoCN · 73.0 works13×
← 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 Northwestern Polytechnical 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%10%20%30%mean 20.28%fractional works in this field (log) →share of work in the world top 10% →Hunan UniversityChongqing UniversityXi'an Jiaotong Unive…Northwestern Polytec…
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Xi'an Jiaotong UniversityChina 73.818.9%10.0×205 +30.8%
2Chongqing UniversityChina 69.820.8%11.5×146 +95.5%
3Northwestern Polytechnical UniversityChina 66.018.9%8.9×119 +79.3%
4Hunan UniversityChina 65.721.1%8.5×71 +117.3%
5Yanshan UniversityChina 65.724.6%17.0×70 +172.6%
6Huazhong University of Science and TechnologyChina 65.119.5%5.5×116 +77.7%
7City University of Hong KongChina 64.826.8%2.7×18 +50.6%
8Southwest Jiaotong UniversityChina 61.413.6%14.4×130 +83.7%
9Shanghai Jiao Tong UniversityChina 61.314.0%3.8×130 +63.3%
10Beijing University of TechnologyChina 60.424.6%6.7×55 +124.9%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Machine Fault Diagnosis Techniques, country by country

Is Machine Fault Diagnosis Techniques research growing?

Output in 2022-2025 was 81% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Machine Fault Diagnosis Techniques.

still counting19801990200020102020
grewheldshrank

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

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Machine Fault Diagnosis Techniques as a percentage of every work published that year, 0.048% in 2024.

still counting0.025%0.050%0.075%Machine Fault Diagnosis Techniques0.067% in 202519801990200020102020

Fractional works of Machine Fault Diagnosis Techniques over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Machine Fault Diagnosis Techniques9.7% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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