Skip to main content

Advertisement

Springer Nature Link
Log in

Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n = 12), decision tree methods (n = 11), hybrid models (n = 8), or latent variable models (n = 6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Aboelmaged M G (2014) Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms. Int J Inf Manag 34 (5):639–651. https://doi.org/10.1016/j.ijinfomgt.2014.05.002

    Article  Google Scholar 

  2. Holmberg K, Adgar A, Arnaiz A, Jantunen E, Mascolo J (2010). In: Holmberg K, Adgar A, Arnaiz A, Jantunen E, Mascolo J, Mekid S (eds) E-maintenance. Springer, London

  3. Muller A, Crespo Marquez A, Iung B (2008) On the concept of e-maintenance: Review and current research. Reliab Eng Syst Saf 93(8):1165–1187. https://doi.org/10.1016/j.ress.2007.08.006

    Article  Google Scholar 

  4. Mobley R K (2001) Predictive Maintenance. In: Plant Engineer’s Handbook. Elsevier, pp 867–888

  5. Sullivan G, Pugh R, Melendez A P, Hunt WD (2010) Operations & maintenance best practices-a guide to achieving operational efficiency (release 3). Technical Report, Pacific Northwest National Lab.(PNNL), Richland

  6. Rødseth H, Schjølberg P (2016) Data-driven predictive maintenance for green manufacturing. In: Proceedings of the 6th international workshop of advanced manufacturing and automation. Advances in Economics, Business and Management Research. Atlantis Press, pp 36–41

  7. Jardine A KS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510. https://doi.org/10.1016/J.YMSSP.2005.09.012, https://www.sciencedirect.com/science/article/pii/S0888327005001512

    Article  Google Scholar 

  8. Kan M S, Tan A CC, Mathew J (2015) A review on prognostic techniques for non-stationary and non-linear rotating systems. Mech Syst Signal Process 62:1–20. https://doi.org/10.1016/j.ymssp.2015.02.016, https://www.sciencedirect.com/science/article/pii/S0888327015000898

    Article  Google Scholar 

  9. Zhang W, Yang D, Wang H (2019) Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey. IEEE Syst J 13(3):2213–2227. https://doi.org/10.1109/JSYST.2019.2905565

    Article  Google Scholar 

  10. Hwang I, Kim S, Kim Y, Seah C E (2010) A survey of fault detection, isolation, and reconfiguration methods. IEEE Trans Control Syst Technol 18(3):636–653. https://doi.org/10.1109/TCST.2009.2026285, https://ieeexplore.ieee.org/abstract/document/5282515/

    Article  Google Scholar 

  11. Gertler J J (2017) Fault detection and diagnosis in engineering systems. CRC Press. https://www.taylorfrancis.com/books/9781351448796

  12. Boyes H, Hallaq B, Cunningham J, Watson T (2018) The industrial internet of things (IIoT): An analysis framework. Comput Ind 101:1–12. https://doi.org/10.1016/j.compind.2018.04.015

    Article  Google Scholar 

  13. O’Donovan P, Leahy K, Bruton K, O’Sullivan D TJ (2015) Big data in manufacturing: a systematic mapping study. J Big Data 2(1). https://doi.org/10.1186/s40537-015-0028-x

  14. Qin S J (2009) Data-driven Fault Detection and Diagnosis for Complex Industrial Processes. In: IFAC Proceedings Volumes, vol 42. Elsevier, pp 1115–1125

  15. Carvalho T P, Soares FAAMN, Vita R, Francisco R P, Basto JP, Alcalá SGS (2019) A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Engi 137:106024. https://doi.org/10.1016/j.cie.2019.106024

    Article  Google Scholar 

  16. Hu H, Tang B, Gong X, Wei W, Wang H (2017) Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks. IEEE Trans Ind Inf 13(4):2106–2116. https://doi.org/10.1109/TII.2017.2683528

    Article  Google Scholar 

  17. Mathew V, Toby T, Singh V, Rao B M, Kumar M G (2017) Prediction of remaining useful lifetime (rul) of turbofan engine using machine learning. In: 2017 IEEE International Conference on Circuits and Systems (ICCS). IEEE, pp 306–311

  18. Shao H, Jiang H, Wang F, Zhao H (2017) An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowl-Based Syst 119:200–220. https://doi.org/10.1016/j.knosys.2016.12.012

    Article  Google Scholar 

  19. Wang L, Zhang Z, Long H, Xu J, Liu R (2017) Wind turbine gearbox failure identification with deep neural networks. IEEE Trans Ind Inf 13(3):1360–1368. https://doi.org/10.1109/TII.2016.2607179

    Article  Google Scholar 

  20. You D, Gao X, Katayama S (2015) Wpd-pca-based laser welding process monitoring and defects diagnosis by using fnn and svm. IEEE Trans Ind Electron 62(1):628–636. https://doi.org/10.1109/TIE.2014.2319216

    Article  Google Scholar 

  21. Baptista M, Sankararaman S, de Medeiros I P, Nascimento Jr C, Prendinger H, Henriques EMP (2018) Forecasting fault events for predictive maintenance using data-driven techniques and arma modeling. Comput Ind Eng 115:41–53. https://doi.org/10.1016/j.cie.2017.10.033

  22. Li C, Sanchez R-V, Zurita G, Cerrada M, Cabrera D, Vásquez R E (2015) Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis. Neurocomputing 168:119–127. https://doi.org/10.1016/j.neucom.2015.06.008

    Article  Google Scholar 

  23. Susto G A, Schirru A, Pampuri S, McLoone S, Beghi A (2015) Machine learning for predictive maintenance: A multiple classifier approach. IEEE Trans Ind Inf 11(3):812–820. https://doi.org/10.1109/TII.2014.2349359

    Article  Google Scholar 

  24. Canizo M, Onieva E, Conde A, Charramendieta S, Trujillo S (2017) Real-time predictive maintenance for wind turbines using big data frameworks. In: 2017 ieee international conference on prognostics and health management (icphm). IEEE, pp 70–77

  25. Krishnakumari A, Elayaperumal A, Saravanan M, Arvindan C (2017) Fault diagnostics of spur gear using decision tree and fuzzy classifier. Int J Adv Manuf Technol 89(9-12):3487–3494. https://doi.org/10.1007/s00170-016-9307-8

    Article  Google Scholar 

  26. Amruthnath N, Gupta T (2018) A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance. In: 2018 5th International Conference on Industrial Engineering and Applications (ICIEA). IEEE, pp 355–361

  27. Uhlmann E, Pontes R P, Geisert C, Hohwieler E (2018) Cluster identification of sensor data for predictive maintenance in a selective laser melting machine tool. Procedia Manuf 24:60–65. https://doi.org/10.1016/j.promfg.2018.06.009

    Article  Google Scholar 

  28. Ahmad W, Khan S A, Islam MMM, Kim J-M (2019) A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models. Reliab Eng Syst Safety 184:67–76. https://doi.org/10.1016/j.ress.2018.02.003

    Article  Google Scholar 

  29. Yu J (2018) Tool condition prognostics using logistic regression with penalization and manifold regularization. Appl Soft Comput 64:454–467. https://doi.org/10.1016/j.asoc.2017.12.042

    Article  Google Scholar 

  30. Haq R (2020) Enterprise artificial intelligence transformation : a playbook for the next generation of business and technology leaders. Wiley

  31. Luo B, Wang H, Liu H, Li B, Peng F (2018) Early fault detection of machine tools based on deep learning and dynamic identification. IEEE Trans Ind Electron 66(1):509–518. https://doi.org/10.1109/TIE.2018.2807414

    Article  Google Scholar 

  32. Bang S H, Ak R, Narayanan A, Lee Y T, Cho H (2019) A survey on knowledge transfer for manufacturing data analytics, vol 104. Elsevier B.V.

  33. Quintana G, Ciurana J (2011) Chatter in machining processes: A review. Int J Mach Tools Manuf 51(5):363–376. https://doi.org/10.1016/j.ijmachtools.2011.01.001

    Article  Google Scholar 

  34. Liberati A, Altman D G, Tetzlaff J, Mulrow C, Gøtzsche P C, Ioannidis J PA, Clarke M, Devereaux P J, Kleijnen J, Moher D (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. In: Journal of Clinical Epidemiology, vol 62. Pergamon, pp e1–e34

  35. Kitchenham B (2004) Procedures for performing systematic reviews. Keele, UK, Keele Univ 33(2004):1–26. https://www.researchgate.net/publication/228756057_Procedures_for_Performing_Systematic_Reviews

    Google Scholar 

  36. Gama J (2012) A survey on learning from data streams: Current and future trends, vol 1. https://link.springer.com/content/pdf/10.1007/s13748-011-0002-6.pdf

  37. Gusenbauer M, Haddaway N R (2020) Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Res Synthes Methods 11(2):181–217. https://doi.org/10.1002/jrsm.1378

    Article  Google Scholar 

  38. Subramanya S H, Lama B, Acharya K P (2020) Impact of COVID-19 pandemic on the scientific community. https://doi.org/10.5339/QMJ.2020.21

  39. Grass A, Beecks C, Soto J A C (2019) Unsupervised Anomaly Detection in Production Lines. In: Beyerer JKO (ed) Machine Learning For Cyber Physical Systems, ML4CPS 2018. Technologien fur die intelligente Automation, vol 9, pp 18–25

  40. Liu Q, Zhang F, Liu M, Shen W (2016) A fault prediction method based on modified Genetic Algorithm using BP neural network algorithm. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp 4614–4619

  41. Linard A, Bueno M L P (2016) Towards Adaptive Scheduling of Maintenance for Cyber-Physical Systems. In: Margaria, T SB (ed) Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques, Pt I, Lecture Notes in Computer Science, vol 9952, pp 134–150

  42. Kinghorst J, Geramifard O, Luo M, Chan H L, Yong K, Folmer J, Zou M, Vogel-Heuser B (2017) Hidden Markov model-based predictive maintenance in semiconductor manufacturing: A genetic algorithm approach. In: IEEE International Conference on Automation Science and Engineering. IEEE, pp 1260–1267

  43. Paolanti M, Romeo L, Felicetti A, Mancini A, Frontoni E, Loncarski J (2018) Machine Learning approach for Predictive Maintenance in Industry 4.0. In: 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp 1–6

  44. Amihai I, Gitzel R, Kotriwala A M, Pareschi D, Subbiah S, Sosale G, Strecker C (2018) An Industrial Case Study Using Vibration Data and Machine Learning to Predict Asset Health. In: Proper HA (ed) 2018 20TH IEEE INTERNATIONAL CONFERENCE ON BUSINESS INFORMATICS (IEEE CBI 2018), VOL 1. Conference on Business Informatics. IEEE; IEEE Comp Soc, pp 178–185

  45. Strauß P, Schmitz M, Wöstmann R, Deuse J (2018) Enabling of Predictive Maintenance in the Brownfield through Low-Cost Sensors, an IIoTArchitecture and Machine Learning. In: Abe N, Liu H, Pu C, Hu X, Ahmed N, Qiao M, Song Y, Kossmann D, Liu B, Lee K, Tang J, He J, Saltz J (eds) Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. https://doi.org/10.1109/BigData.2018.8622076. IEEE; IEEE Comp Soc; Expedia Grp; Baidu; Squirrel AI Learning; Ankura; Springer, IEEE International Conference on Big Data, pp 1474–1483

  46. Kolokas N, Vafeiadis T, Ioannidis D, Tzovaras D (2018) Forecasting faults of industrial equipment using machine learning classifiers. In: Yildirim, T ML (ed) 2018 IEEE (SMC) International Conference on Innovations in Intelligent Systems and Applications, INISTA 2018. Aristotle Univ Thessaloniki; Democritus Univ Thrace; IEEE Systems & Cybernet Soc; IEEE; Yildiz Techn Univ

  47. Amruthnath N, Gupta T (2018) Fault class prediction in unsupervised learning using model-based clustering approach. In: 2018 International Conference on Information and Computer Technologies, ICICT 2018. IEEE, pp 5–12

  48. Fernandes M, Canito A, Corchado J M, Marreiros G (2020) Fault detection mechanism of a predictive maintenance system based on autoregressive integrated moving average models. In: Herrera F, Matsui K, Rodríguez-González S (eds) Distributed Computing and Artificial Intelligence, 16th International Conference. Springer International Publishing, Cham, pp 171–180

  49. Christou I T (2019) Avoiding the Hay for the Needle in the Stack: Online Rule Pruning in Rare Events Detection. In: 2019 16th International Symposium on Wireless Communication Systems (ISWCS), pp 661–665

  50. Cheng C, Zhang B, Gao D (2019) A Predictive Maintenance Solution for Bearing Production Line Based on Edge-Cloud Cooperation. In: 2019 Chinese Automation Congress (CAC), pp 5885–5889

  51. Chen L-Y, Lee J-H, Yang Y-L, Yeh M-T, Hsiao T-C (2019) Predicting the Remaining Useful Life of Plasma Equipment through XCSR. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO ’19. https://doi.org/10.1145/3319619.3326879. Association for Computing Machinery, New York, pp 1263–1270

  52. Aremu O O, O’Reilly D O, Hyland-Wood D, McAree P R (2019) Kullback-leibler divergence constructed health indicator for data-driven predictive maintenance of multi-sensor systems. In: IEEE International Conference on Industrial Informatics (INDIN). Inst Elect & Elect Engineers; Tampere Univ; Finnish Soc Automat; IEEE Ind Elect Soc, pp 1315–1320

  53. Binding A, Dykeman N, Pang S (2019) Machine learning predictive maintenance on data in the wild. In: IEEE 5th World Forum on Internet of Things, WF-IoT 2019 - Conference Proceedings. IEEE; Univ Limerick; IEEE Commun Soc; IEEE Consumer Elect Soc; IEEE Reliabil Soc; IEEE Sensors Council; IEEE Signal Proc Soc; IEEE Stand Assoc; IEEE Control Syst Soc; IEEE Council Elect Design Automat; IEEE Council RFID; IEEE Electromagnet Compatibil Soc;, pp 507–512

  54. Farbiz F, Miaolong Y, Yu Z (2020) A cognitive analytics based approach for machine health monitoring, anomaly detection, and predictive maintenance. In: 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). IEEE, pp 1104–1109

  55. Das M, Pratama M, Tjahjowidodo T (2020) A self-evolving mutually-operative recurrent network-based model for online tool condition monitoring in delay scenario. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 2775–2783

  56. KOCA O, Kaymakci O T, Mercimek M (2020) Advanced Predictive Maintenance with Machine Learning Failure Estimation in Industrial Packaging Robots. In: 2020 International Conference on Development and Application Systems (DAS), pp 1–6

  57. Li Z, Wang Y, Wang K-S (2017) Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Adv Manuf 5(4, SI):377–387. https://doi.org/10.1007/s40436-017-0203-8

    Article  Google Scholar 

  58. Kim D, Lee S, Kim D (2021) An applicable predictive maintenance framework for the absence of run-to-failure data. Appl Sci 11(11):5180. https://doi.org/10.3390/app11115180

    Article  Google Scholar 

  59. Zschech P, Heinrich K, Bink R, Neufeld J S (2019) Prognostic Model Development with Missing Labels: A Condition-Based Maintenance Approach Using Machine Learning. Bus Inf Syst Eng 61(3):327–343. https://doi.org/10.1007/s12599-019-00596-1

    Article  Google Scholar 

  60. Wang H, Du W (2020) Fast spectral correlation based on sparse representation self-learning dictionary and its application in fault diagnosis of rotating machinery. Complexity. https://doi.org/10.1155/2020/9857839

  61. Mohan T R, Roselyn J P, Uthra R A, Devaraj D, Umachandran K (2021) Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery. Comput Ind Eng 157:107267. https://doi.org/10.1016/j.cie.2021.107267

    Article  Google Scholar 

  62. Giordano D, Mellia M, Cerquitelli T (2021) K-mdtsc: K-multi-dimensional time-series clustering algorithm. Electronics 10(10):1166. https://doi.org/10.3390/electronics10101166

    Article  Google Scholar 

  63. Ruiz-Sarmiento J-R, Monroy J, Moreno F-A, Galindo C, Bonelo J-M, Gonzalez-Jimenez J (2020) A predictive model for the maintenance of industrial machinery in the context of industry 4.0. Eng Appl Artif Intell 87:103289. https://doi.org/10.1016/j.engappai.2019.103289, http://www.sciencedirect.com/science/article/pii/S0952197619302489

    Article  Google Scholar 

  64. Chen X, Van Hillegersberg J, Topan E, Smith S, Roberts M (2021) Application of data-driven models to predictive maintenance: Bearing wear prediction at tata steel. Expert Syst Appl 186:115699. https://doi.org/10.1016/j.eswa.2021.115699

    Article  Google Scholar 

  65. Yu W, Dillon T, Mostafa F, Rahayu W, Liu Y (2020) A Global Manufacturing Big Data Ecosystem for Fault Detection in Predictive Maintenance. IEEE Trans Ind Inf 16(1):183–192. https://doi.org/10.1109/TII.2019.2915846

    Article  Google Scholar 

  66. Zhai S, Gehring B, Reinhart G (2021) Enabling predictive maintenance integrated production scheduling by operation-specific health prognostics with generative deep learning. J Manuf Syst. https://doi.org/10.1016/j.jmsy.2021.02.006

  67. Kolokas N, Vafeiadis T, Ioannidis D, Tzovaras D (2020) A generic fault prognostics algorithm for manufacturing industries using unsupervised machine learning classifiers. Simul Model Pract Theory 103:102109. https://doi.org/10.1016/j.simpat.2020.102109, http://www.sciencedirect.com/science/article/pii/S1569190X20300472

    Article  Google Scholar 

  68. Turkoglu B, Komesli M, Unluturk M S (2019) Application of Data Mining in Failure Estimation of Cold Forging Machines: An Industrial Research. Stud Inf Control 28(1):87–94. https://doi.org/10.24846/v28i1y201909

    Article  Google Scholar 

  69. Naskos A, Gounaris A, Metaxa I, Koechling D, Stirna J (2019) Detecting anomalous behavior towards predictive maintenance. In: Proper HA (ed) Advanced Information Systems Engineering Workshops (CAISE 2019), Lecture Notes in Business Information Processing, vol 349, pp 73–82

  70. Rousopoulou V, Nizamis A, Giugliano L, Haigh P, Martins L, Ioannidis D, Tzovaras D, Stirna J (2019) Data Analytics Towards Predictive Maintenance for Industrial Ovens A Case Study Based on Data Analysis of Various Sensors Data. In: Proper HA (ed) Advanced Information Systems Engineering Workshops (CAISE 2019), Lecture Notes in Business Information Processing, vol 349, pp 83–94

  71. Bukkapatnam S T S, Afrin K, Dave D, Kumara S R T (2019) Machine learning and AI for long-term fault prognosis in complex manufacturing systems. CIRP Ann-Manuf Technol 68(1):459–462. https://doi.org/10.1016/j.cirp.2019.04.104

    Article  Google Scholar 

  72. Vrabič R, Kozjek D, Butala P (2017) Knowledge elicitation for fault diagnostics in plastic injection moulding: A case for machine-to-machine communication. CIRP Ann Manuf Technol 66(1):433–436. https://doi.org/10.1016/j.cirp.2017.04.001, http://www.sciencedirect.com/science/article/pii/S000785061730001X

    Article  Google Scholar 

  73. Bampoula X, Siaterlis G, Nikolakis N, Alexopoulos K (2021) A deep learning model for predictive maintenance in cyber-physical production systems using lstm autoencoders. Sensors 21(3):972. https://doi.org/10.3390/s21030972

    Article  Google Scholar 

  74. Syafrudin M, Alfian G, Fitriyani N L, Rhee J (2018) Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing. Sensors 18(9). https://doi.org/10.3390/s18092946

  75. Avendano D N, Caljouw D, Deschrijver D, Van Hoecke S (2021) Anomaly detection and event mining in cold forming manufacturing processes. Int J Adv Manuf Technol:1–16. https://doi.org/10.1007/s00170-020-06156-2

  76. Zhang Y, Beudaert X, Argandoña J, Ratchev S, Munoa J (2020) A cpps based on gbdt for predicting failure events in milling. Int J Adv Manuf Technol 111(1):341–357. https://doi.org/10.1007/s00170-020-06078-z

    Article  Google Scholar 

  77. Chen B, Liu Y, Zhang C, Wang Z (2020) Time Series Data for Equipment Reliability Analysis With Deep Learning. IEEE Access 8:105484–105493. https://doi.org/10.1109/ACCESS.2020.3000006

    Article  Google Scholar 

  78. Kiangala K S, Wang Z (2020) An effective predictive maintenance framework for conveyor motors using dual time-series imaging and convolutional neural network in an industry 4.0 environment. IEEE Access:1. https://doi.org/10.1109/ACCESS.2020.3006788

  79. Liu C, Tang D, Zhu H, Nie Q (2021) A novel predictive maintenance method based on deep adversarial learning in the intelligent manufacturing system. IEEE Access 9:49557–49575. https://doi.org/10.1109/ACCESS.2021.3069256

    Article  Google Scholar 

  80. Tran T, Lundgren J (2020) Drill fault diagnosis based on the scalogram and mel spectrogram of sound signals using artificial intelligence. IEEE Access 8:203655–203666. https://doi.org/10.1109/ACCESS.2020.3036769

    Article  Google Scholar 

  81. Xu Y, Sun Y, Liu X, Zheng Y (2019) A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning. IEEE Access 7:19990–19999. https://doi.org/10.1109/ACCESS.2018.2890566

    Article  Google Scholar 

  82. Kontaki M, Gounaris A, Papadopoulos A N, Tsichlas K, Manolopoulos Y (2016) Efficient and flexible algorithms for monitoring distance-based outliers over data streams. Inf Syst 55:37–53. https://doi.org/10.1016/j.is.2015.07.006, http://www.sciencedirect.com/science/article/pii/S0306437915001349

    Article  Google Scholar 

  83. Luxton D D (2015) Artificial Intelligence in Behavioral and Mental Health Care. Technical Report, https://www.sciencedirect.com/science/article/pii/B9780124202481000015

  84. Mitchell T M (2006) The Discipline of Machine Learning. Technical Report, http://www-cgi.cs.cmu.edu/~tom/pubs/MachineLearningTR.pdf

  85. Meyer B, Choppy C, Staunstrup J, van Leeuwen J (2009) Viewpoint research evaluation for computer science. Commun ACM 52(4):31–34. https://doi.org/10.1145/1498765.1498780

    Article  Google Scholar 

  86. (2021). History of IEEE. https://www.ieee.org/about/ieee-history.html

  87. Efthymiou K, Pagoropoulos A, Papakostas N, Mourtzis D, Chryssolouris G (2012) Manufacturing systems complexity review: Challenges and outlook. In: Procedia CIRP, vol 3. Elsevier B.V., pp 644–649

  88. García-Martín E, Rodrigues C F, Riley G, Grahn H (2019) Estimation of energy consumption in machine learning. J Parallel Distrib Comput 134:75–88. https://doi.org/10.1016/j.jpdc.2019.07.007

    Article  Google Scholar 

  89. Mulders M, Haarman M (2017) Predictive Maintenance 4.0, Predict the unpredictable (PwC Publication). Technical Report, PwC and Mainnovation

  90. Gama J , žliobaitċ I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv 46(4):1–37. https://doi.org/10.1145/2523813, http://dl.acm.org/citation.cfm?doid=2597757.2523813

    Article  MATH  Google Scholar 

  91. Tsymbal A (2004) The problem of concept drift: definitions and related work. Computer Science Department, Trinity College Dublin.

  92. Gomes H M, Read J, Bifet A, Barddal J P, Gama J (2019) Machine Learning for Streaming Data: State of the Art, Challenges, and Opportunities. SIGKDD Explor Newsl 21(2):6–22. https://doi.org/10.1145/3373464.3373470

    Article  Google Scholar 

  93. Krempl G, žliobaite I, Brzeziński D, Hüllermeier E, Last M, Lemaire V, Noack T, Shaker A, Sievi S, Spiliopoulou M, Stefanowski J (2014) Open challenges for data stream mining research. ACM SIGKDD Explor Newslett 16(1):1–10. https://doi.org/10.1145/2674026.2674028, https://dl.acm.org/citation.cfm?id=2674028

    Article  Google Scholar 

Download references

Acknowledgements

The present work has been developed under project PIANiSM (EUREKA - ITEA3: 17008; ANI|P2020 40125) and has received Portuguese National Funds through FCT (Portuguese Foundation for Science and Technology) under project UIDB/00760/2020 and Ph.D. Scholarship SFRH/BD/136253/2018.

Author information

Authors and Affiliations

  1. GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (ISEP/IPP), Porto, Portugal

    Marta Fernandes & Goreti Marreiros

  2. BISITE Research Centre, University of Salamanca (USAL), Salamanca, Spain

    Juan Manuel Corchado

Authors
  1. Marta Fernandes
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Juan Manuel Corchado
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Goreti Marreiros
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Marta Fernandes.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fernandes, M., Corchado, J.M. & Marreiros, G. Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review. Appl Intell 52, 14246–14280 (2022). https://doi.org/10.1007/s10489-022-03344-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-022-03344-3

Keywords