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D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Engineering and Technology 42 6612 6385 218 215 275 6564

Piero Baraldi publications per year

The chart shows the history of publications by Piero Baraldi between 2003 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Piero Baraldi published across 23 years, from 2003 to 2025, averaging 13 papers a year. Output peaked at 27 publications in 2020. 25 of the 300 publications appeared in the last two years.

No. of publications
5 10 15 20 25
Bar chart. Horizontal axis: year, 2003 to 2025. Vertical axis: number of publications, 0 to 27. Peak 27 publications in 2020. 2003: 1 publication 2004: 3 publications 2005: 3 publications 2006: 5 publications 2007: 12 publications 2008: 11 publications 2009: 14 publications 2010: 12 publications 2011: 20 publications 2012: 11 publications 2013: 20 publications 2014: 24 publications 2015: 19 publications 2016: 13 publications 2017: 10 publications 2018: 11 publications 2019: 10 publications 2020: 27 publications 2021: 15 publications 2022: 20 publications 2023: 14 publications 2024: 11 publications 2025: 14 publications
2003 2025

300 publications in total across all disciplines

View publications per year as a table
Piero Baraldi: publications per year, 2003 to 2025
Year Publications
2003 1
2004 3
2005 3
2006 5
2007 12
2008 11
2009 14
2010 12
2011 20
2012 11
2013 20
2014 24
2015 19
2016 13
2017 10
2018 11
2019 10
2020 27
2021 15
2022 20
2023 14
2024 11
2025 14
Total 300
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Piero Baraldi publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Piero Baraldi sits on this spectrum.

No. of scientists
100 200 300 400
Bar chart with 78 bars. Horizontal axis: publications, 38–47 to 804+. Vertical axis: number of scientists, 0 to 457. Most scientists, 457, have 148–157 publications. The last bar groups every scientist with 804 publications or more. The highlighted bar, 268–277 publications, is where this scientist sits. 38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38–47 publications 804+

This scientist: 275 publications — 71st percentile

71% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

View publications distribution as a table
Number of Engineering and Technology scientists by publication count, Research.com 2026 ranking edition. Based on 9,796 ranked scientists.
Publications Scientists This scientist
38–47 20
48–57 35
58–67 96
68–77 135
78–87 190
88–97 259
98–107 283
108–117 369
118–127 341
128–137 386
138–147 372
148–157 457
158–167 415
168–177 407
178–187 421
188–197 378
198–207 403
208–217 317
218–227 346
228–237 321
238–247 260
248–257 280
258–267 240
268–277 214 275
278–287 242
288–297 203
298–307 166
308–317 154
318–327 175
328–337 159
338–347 99
348–357 131
358–367 106
368–377 118
378–387 97
388–397 108
398–407 82
408–417 71
418–427 64
428–437 55
438–447 54
448–457 60
458–467 47
468–477 40
478–487 30
488–497 29
498–507 38
508–517 40
518–527 32
528–537 23
538–547 28
548–557 23
558–567 19
568–577 16
578–587 17
588–597 18
598–607 22
608–617 15
618–627 9
628–637 11
638–647 21
648–657 12
658–667 9
668–677 11
678–687 9
688–697 6
698–707 14
708–717 7
718–727 8
728–737 10
738–747 9
748–757 5
758–767 5
768–777 11
778–787 7
788–797 2
798–803 4
804+ 100
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Piero Baraldi D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Piero Baraldi sits on this spectrum.

No. of scientists
100 200 300 400
Bar chart with 78 bars. Horizontal axis: D-Index, 30 to 107+. Vertical axis: number of scientists, 0 to 426. Most scientists, 426, have 42 D-Index. The last bar groups every scientist with 107 D-Index or more. The highlighted bar, 42 D-Index, is where this scientist sits. 30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 42 D-Index — 35th percentile

35% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

View D-Index distribution as a table
Number of Engineering and Technology scientists by D-index, Research.com 2026 ranking edition. Based on 9,796 ranked scientists.
D-Index Scientists This scientist
30 59
31 114
32 129
33 189
34 200
35 262
36 311
37 312
38 350
39 385
40 348
41 362
42 426 42
43 380
44 310
45 341
46 301
47 306
48 271
49 246
50 210
51 253
52 213
53 221
54 195
55 186
56 170
57 167
58 166
59 144
60 152
61 141
62 138
63 131
64 118
65 114
66 119
67 95
68 87
69 77
70 89
71 69
72 54
73 46
74 55
75 54
76 49
77 53
78 46
79 28
80 39
81 36
82 24
83 26
84 36
85 18
86 25
87 19
88 26
89 27
90 23
91 15
92 12
93 9
94 15
95 10
96 13
97 13
98 9
99 7
100 7
101 8
102 7
103 7
104 9
105 6
106 9
107+ 99
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Overview

Piero Baraldi is affiliated with the Polytechnic University of Milan in Italy. Their research primarily spans the fields of Engineering and Computer Science, with a total of 128 and 46 publications respectively.

Their work concentrates on several subfields including Control and Systems Engineering, Electrical and Electronic Engineering, Artificial Intelligence, Safety, Risk, Reliability and Quality, and Statistics, Probability and Uncertainty. This range reflects a focus on complex systems and the integration of advanced technologies for optimizing reliability and safety.

Main topics covered in Piero Baraldi's research include:

  • Fault Detection and Control Systems
  • Machine Fault Diagnosis Techniques
  • Reliability and Maintenance Optimization
  • Energy Load and Power Forecasting
  • Risk and Safety Analysis
  • Anomaly Detection Techniques and Applications
  • Software Reliability and Analysis Research

Their recent publications demonstrate engagement with reliability engineering, renewable energy systems, and advanced neural network models for system prognostics and fault detection. Notable papers include:

  • "Maintenance optimization in industry 4.0" (2023), published in Reliability Engineering & System Safety
  • "Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants" (2020), published in Applied Energy
  • "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning" (2021), published in Renewable Energy
  • "A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks" (2021), published in Reliability Engineering & System Safety
  • "A multi-branch deep neural network model for failure prognostics based on multimodal data" (2021), published in Journal of Manufacturing Systems

Piero Baraldi has frequently collaborated with several researchers, including Enrico Zio, Luca Pinciroli, Michele Compare, Ahmed Shokry, and Luigi Serio, reflecting recurring partnerships that have contributed to multiple publications.

Publications are often found in venues focused on reliability, safety, and engineering systems. The most frequent publication venues include:

  • Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference
  • Reliability Engineering & System Safety
  • Energies
  • Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability
  • SSRN Electronic Journal

Best Publications

  • Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0

    Michele Compare;Piero Baraldi;Enrico Zio

  • Uncertainty in Risk Assessment: The Representation and Treatment of Uncertainties by Probabilistic and Non-Probabilistic Methods

    Terje Aven;Enrico Zio;Piero Baraldi;Roger Flage

  • Concerns, challenges, and directions of development for the issue of representing uncertainty in risk assessment.

    Roger Flage;Terje Aven;Enrico Zio;Enrico Zio;Piero Baraldi

  • A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis

    Piero Baraldi;Enrico Zio

  • Maintenance optimization in industry 4.0

    Unknown

  • Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data

    Piero Baraldi;Francesca Mangili;Enrico Zio;Enrico Zio

  • A particle filtering and kernel smoothing-based approach for new design component prognostics

    Yang Hu;Piero Baraldi;Francesco Di Maio;Enrico Zio;Enrico Zio

  • A Kalman Filter-Based Ensemble Approach With Application to Turbine Creep Prognostics

    P. Baraldi;F. Mangili;E. Zio

  • Comparing the treatment of uncertainty in Bayesian networks and fuzzy expert systems used for a human reliability analysis application

    Piero Baraldi;Luca Podofillini;Lusine Mkrtchyan;Enrico Zio;Enrico Zio

  • Model-based and data-driven prognostics under different available information

    Piero Baraldi;Francesco Cadini;Francesca Mangili;Enrico Zio;Enrico Zio

  • Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

    Piero Baraldi;Francesco Cannarile;Francesco Di Maio;Enrico Zio;Enrico Zio

  • A fuzzy set-based approach for modeling dependence among human errors

    E. Zio;P. Baraldi;M. Librizzi;L. Podofillini

  • Ensemble neural network-based particle filtering for prognostics

    Piero Baraldi;Michele Compare;Sergio Sauco;Enrico Zio;Enrico Zio

  • Ensemble of optimized echo state networks for remaining useful life prediction

    Marco Rigamonti;Piero Baraldi;Enrico Zio;Enrico Zio;Indranil Roychoudhury

  • Ensemble empirical mode decomposition and long short-term memory neural network for multi-step predictions of time series signals in nuclear power plants

    Hoang-Phuong Nguyen;Piero Baraldi;Enrico Zio;Enrico Zio;Enrico Zio

  • Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics

    P. Baraldi;G. Bonfanti;E. Zio;E. Zio

  • Probability and possibility-based representations of uncertainty in fault tree analysis.

    Roger Flage;Piero Baraldi;Enrico Zio;Enrico Zio;Terje Aven

  • Particle Filter-Based Prognostics for an Electrolytic Capacitor Working in Variable Operating Conditions

    Marco Rigamonti;Piero Baraldi;Enrico Zio;Daniel Astigarraga

  • A Novel Concept Drift Detection Method for Incremental Learning in Nonstationary Environments

    Zhe Yang;Sameer Al-Dahidi;Piero Baraldi;Enrico Zio

  • A method for fault detection in multi-component systems based on sparse autoencoder-based deep neural networks

    Unknown

  • Assessment of the availability of an offshore installation by Monte Carlo simulation

    Enrico Zio;Piero Baraldi;Edoardo Patelli

  • Uncertainty in Risk Assessment

    Terje Aven;Enrico Zio;Piero Baraldi;R. Flage

  • Fault Detection in Nuclear Power Plants Components by a Combination of Statistical Methods

    Francesco Di Maio;Piero Baraldi;Enrico Zio;Redouane Seraoui

  • Analysis of the Results of Accelerated Aging Tests in Insulated Gate Bipolar Transistors

    Daniel Astigarraga;Federico Martin Ibanez;Ainhoa Galarza;Jose Martin Echeverria

Frequent Co-Authors

Enrico Zio
Enrico Zio Polytechnic University of Milan
Terje Aven
Terje Aven University of Stavanger
Christophe Bérenguer
Christophe Bérenguer Grenoble Alpes University
Kai Goebel
Kai Goebel Palo Alto Research Center
Antonio Cammi
Antonio Cammi Polytechnic University of Milan
Eric Moulines
Eric Moulines Mohamed bin Zayed University of Artificial Intelligence

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