Skip to main content

Advertisement

Log in

RETRACTED ARTICLE: An intelligent Context Based Multi-layered Bayesian Inferential predictive analytic framework for classifying machine states

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

This article was retracted on 04 July 2022

This article has been updated

Abstract

Proactive fault diagnosis is a burning issue in condition monitoring of machines. Intelligent methods prove to be promising solutions for designing predictive analytic frameworks for fault diagnosis and machine state classification. The competency of machine learning algorithms in handling large voluminous data has marked them as a natural solution for developing intelligent framework that proactively classifies the machine states. The paper proposes a novel Context Based Multi-layered Bayesian Inferential (CBMBI) predictive analytic framework, which is motivated by MisMatch Negativity (MMN) and Predictive Coding. The CBMBI framework is augmented with a new hyperparameter (context) that greatly reduces the misclassification rate. The performance of the framework is analysed with Case Western Reserve University 6205-2RS JEM SKF dataset. The profound results reveal that the proposed framework shows 97% accuracy and 94% F1-score which is relatively higher than the state of art technique.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Change history

References

  • Ahmed HOA, Wong MLD, Nandi AK (2018) Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features. Mech Syst Signal Process 99:459–477

    Article  Google Scholar 

  • Brahim IH, Mehdi D, Chaabane M (2017) Robust fault detection for uncertain T-S fuzzy system with unmeasurable premise variables: descriptor approach. Int J Fuzzy Syst. https://doi.org/10.1007/s40815-017-0344-8

    Article  Google Scholar 

  • Cai B, Liu Y, Fan Q, Zhang Y, Liu Z, Yu S, Ji R (2014) Multi-source information fusion based fault diagnosis of ground-sourceheat pump using Bayesian network. J Appl Energy 114:1–9

    Article  Google Scholar 

  • Deivasigamani S, Senthilpari C, Yong WH (2020) Machine learning method based detection and diagnosis for epilepsy in EEG signal. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-01816-3

    Article  Google Scholar 

  • Friston K (2014) The free-energy principle: a rough guide to the brain?, Article from Cell Press

  • Gan M, Wang C, Zhu C (2016) Construction of hierarchial diagnosis network based on deep learning and its application in the fault pattern recognition on rolling element bearings. J Mech Syst Signals 72:94–102

    Google Scholar 

  • Garrido MI, Kilner JM, Stephan KE, Friston KJ (2009) The mismatch negativity: a review of underlying mechanisms. Clin Neurophysiol 120:453–463

    Article  Google Scholar 

  • Guo X, Chen L, Shen C (2016) Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. J Meas 93:490–502

    Article  Google Scholar 

  • Lieder F, Stephan KE, Daunizeau J, Garrido MI, Friston KJ (2007) Neurocomputational model of the mismatch negativity. PLoS

  • Lieder F, Daunizeau J, Garrido MI, Friston KJ, Stephan KE (2013) Modelling trial-by-trial changes in the mismatch negativity. PLoS

  • Mortada M-A, Yacout S, Lakis A (2013) Fault diagnosis in power transformers using multi-class logical analysis of data. J Intell Manuf 25:1429–1439

    Article  Google Scholar 

  • Muralidharan V, Sugumaran V (2016) A Comparative Study between Support Vector Machine (SVM) and Extreme Learning Machine (ELM) for Fault Detection in Pumps, Indian Journal of Science and Technology. 9(48), https://doi.org/10.17485/ijst/2016/v9i48/107915. ISSN (Print): 0974–6846. ISSN (Online): 0974–564

  • Muralidharan V, Sugumaran V, Pandey G (2011) Fault Diagnosis of monoblock centrifugal pump using stationary wavelet fatures and J48 algorithm. Int J Prod Technol Manag 1(1):0976–6383

    Google Scholar 

  • Naatanen R, Sussman ES, Salisbury D, Shafer VL (2014) Mismatch Negativity (MMN) as an Index of Cognitive Dysfunction. Brain Topogr 27:451–466

    Article  Google Scholar 

  • Report on Advanced Surveillance, Diagnostic and Prognostic Techniques in Monitoring Structures, Systems and Components in Nuclear Power Plants (2004), No: NP-T-3.14

  • Schwabacher M (2015) A survey of data-driven prognostics, infotech aerospace conferences, 2015

  • Smith WA, Randall RB (2015) Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech Syst Signal Process 64–65:100–131

    Article  Google Scholar 

  • Sridhar KP, Baskar S, Mohamed Shakeel P, Sarma Dhulipala VR (2018) J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-1058-y

  • Susman ES, Chen S, Fort JS, Dinces E (2014) The Five myths of MMN: redefining how to use MMN in basic and clinical research. Brain Topogr 27:553–564

    Article  Google Scholar 

  • Theresa WG, Sasikala E, Gopalakrishnan R, Radha R (2020) Intelligent oriented middleware system based navigation detection time orient node location identification in mobile ad hoc network. J Ambient Intell Humaniz Comput Putt. https://doi.org/10.1007/s12652-020-01720-w

    Article  Google Scholar 

  • Tian Ye, Ma J, Chen Lu, Wang Z (2015) Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine. J Mech Mach Theory 90:175–186

    Article  Google Scholar 

  • Vuust P, Brattico E, Glerean E, Seppanen M, Pakarinen S, Tervaniemi M, Näätänen R (2011) New fast mismatch negativity paradigm for determining the neural prerequisites for musical ability. Cortex 4(7):1091–1098

    Article  Google Scholar 

  • Xu H (2017) An intelligent fault diagnosis approach for power transformers based on support vector machines, Master of Science, Department of Mechanical Engineering, University of Alberta

  • Zhang W, Li C, Peng G, Chen Y, Zhang Z (2018) A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process 100:439–453

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Sharanya.

Additional information

Publisher's Note

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

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12652-022-04292-z

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharanya, S., Venkataraman, R. RETRACTED ARTICLE: An intelligent Context Based Multi-layered Bayesian Inferential predictive analytic framework for classifying machine states. J Ambient Intell Human Comput 12, 7353–7361 (2021). https://doi.org/10.1007/s12652-020-02411-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02411-2

Keywords