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Reliability and Maintenance Optimization

Reliability and Maintenance Optimization is a research topic within Safety, Risk, Reliability and Quality. Science Explorer counts 24k research works in it since 1950. 10.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on reliability engineering and maintenance optimization, covering topics such as degradation modeling, condition-based maintenance, multi-state systems, prognostic models, accelerated degradation tests, risk-based maintenance, stochastic modeling, and system reliability. The papers explore various methods and strategies for optimizing maintenance policies and improving the reliability of deteriorating systems.

  • Reliability Engineering
  • Maintenance Optimization
  • Degradation Modeling
  • Condition-Based Maintenance
  • Multi-State Systems
  • Prognostic Models
  • Accelerated Degradation Tests
  • Risk-Based Maintenance
  • Stochastic Modeling
  • System Reliability
Research works
24k
fractional, since 1950
In the world top 10%
2.4k
per year above
Top-10% rate
10.0%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+11%
the tick is no change
Largest producer
China
53% of the 10 largest, 2022-now
Reliability and Maintenance Optimization

Which countries lead Reliability and Maintenance Optimization research?

By volume, China and the United States publish the most (1.9k and 435 works in 2022-now).

By volume, 2022-now

  1. 1 China 1.9k works
  2. 2 United States 435 works
  3. 3 India 428 works
  4. 4 France 136 works
  5. 5 Indonesia 127 works
  6. 6 Iran 125 works
  7. 7 United Kingdom 123 works
  8. 8 Germany 121 works
  9. 9 Canada 103 works
  10. 10 Saudi Arabia 88 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: 52.9%United States: 12.2%India: 12.0%France: 3.8%6 others listed: 19.2%53%largest
China1,892 · 52.9%United States435 · 12.2%India428 · 12.0%France136 · 3.8%6 others listed688 · 19.2%

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

Which institutions lead Reliability and Maintenance Optimization research?

By volume in 2022-now, Beihang University publishes the most Reliability and Maintenance Optimization research, followed by Northwestern Polytechnical University and Beijing Institute of Technology.

Who are the leading researchers in Reliability and Maintenance Optimization?

The most-cited researchers publishing on Reliability and Maintenance Optimization include Wei Huang and Frede Blaabjerg.

  1. 1 Wei Huang China 267k citations
  2. 2 Frede Blaabjerg Denmark 203k 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 Reliability and Maintenance Optimization?

The most cited paper in Reliability and Maintenance Optimization is "Reciprocally convex approach to stability of systems with time-varying delays" (PooGyeon Park, Jeong Wan Ko, Changki Jeong, 2010), with 2,680 citations. Next come "Machinery health prognostics: A systematic review from data acquisition to RUL prediction" (2,574) and "Statistical Methods for Reliability Data" (2,520).

Most cited papers
#PaperReleasedCitations
1 Reciprocally convex approach to stability of systems with time-varying delays PooGyeon Park, Jeong Wan Ko, Changki Jeong · Automatica 21 Nov 2010 2,680
2 Machinery health prognostics: A systematic review from data acquisition to RUL prediction Yaguo Lei, Naipeng Li, Liang Guo et al. · Mechanical Systems and Signal Processing Review Dec 2017 2,574
3 Statistical Methods for Reliability Data Wayne B. Nelson · Technometrics Aug 1998 2,520
4 Aleatory or epistemic? Does it matter? Armen Der Kiureghian, Ove Ditlevsen · Structural Safety 4 Aug 2008 2,462
5 Remaining useful life estimation – A review on the statistical data driven approaches Xiaosheng Si, Wenbin Wang, Chang-Hua Hu et al. · European Journal of Operational Research 25 Nov 2010 2,125
All 99 most cited papers, with filters and sorting

Where is Reliability and Maintenance Optimization research done?

The largest centres of Reliability and Maintenance Optimization research in 2022-now are Beijing (China), Xi'an (China), Shanghai (China) and Nanjing (China).

Largest cities, 2022-now

  1. 1 BeijingChina 427 works
  2. 2 Xi'anChina 142 works
  3. 3 ShanghaiChina 139 works
  4. 4 NanjingChina 108 works
  5. 5 ChengduChina 78 works
  6. 6 WuhanChina 56 works
  7. 7 ChongqingChina 56 works
  8. 8 TianjinChina 54 works
  9. 9 GuangzhouChina 52 works
  10. 10 ChangshaChina 52 works
See Reliability and Maintenance Optimization on the map

Where is the best place to study Reliability and Maintenance Optimization?

Among universities, judged by research, Beijing Institute of Technology, Beihang University and Xi'an 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%mean 25.72%fractional works in this field (log) →share of work in the world top 10% →Xi'an Jiaotong Unive…Beijing Institute of…Chongqing UniversityBeihang University
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
1Beijing Institute of TechnologyChina 70.528.6%8.8×45 +13.3%
2Beihang UniversityChina 64.014.9%23.8×114 +8.2%
3Xi'an Jiaotong UniversityChina 63.035.3%3.6×28 +5.2%
4Chongqing UniversityChina 61.325.9%6.2×29 +48.4%
5Hefei University of TechnologyChina 56.931.5%5.3×13 +144.5%
6University of Science and Technology of ChinaChina 56.436.9%1.9×10 +52.8%
7Nanjing University of Aeronautics and AstronauticsChina 55.415.2%10.5×41 +24.1%
8Tsinghua UniversityChina 54.123.6%2.6×26 +32.1%
9Shanghai Jiao Tong UniversityChina 54.015.4%3.0×38 +15.8%
10North University of ChinaChina 53.429.9%9.1×10 +114.8%

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 Reliability and Maintenance Optimization, country by country

Is Reliability and Maintenance Optimization research growing?

Output in 2022-2025 was 11% higher than in 2018-2021, peaking in 2023. The fastest-growing topics are Reliability and Maintenance Optimization.

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 Reliability and Maintenance Optimization as a percentage of every work published that year, 0.019% in 2024.

still counting0.020%0.040%Reliability and Maintenance Optimization0.020% in 202519801990200020102020

Fractional works of Reliability and Maintenance Optimization 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%Reliability and Maintenance Optimization10.1% 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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