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Smart machine fault diagnostics based on fault specified discrete wavelet transform

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Abstract

This study examines the impact of the mother wavelet, sensor selection, and machine learning (ML) models for smart fault diagnosis of rotating machines via discrete wavelet transform (DWT). The ability of Daubechies, Haar, Biorthogonal (Bior), Symlets (Sym), and Coiflets (Coif) wavelets is measured in terms of distinguishing imbalance, horizontal/vertical misalignment, and overhang/underhang bearing ball, cage, and outer race faults. For this purpose, single-step and two-step fault monitoring (SSFM and TSFM) approaches are proposed. In SSFM, the ML models detect the fault type by the healthy and faulty signals. In TSFM, the built models first determined whether the machine is faulty or not. If it is, then the models detect the fault type. As ML models, Random Forest (RF), AdaBoost with C4.5 (AB-C4.5), and two artificial neural network algorithms are trained by the features of DWT. Besides, the effect of the sensor type on the fault diagnosis is measured by considering the tachometer, microphone, and two accelerometers individually and combined. The results are interpreted regarding the evaluation metrics such as accuracy, precision, recall, confusion matrix, and model built time. It is concluded that Bior3.1 and Haar wavelets distinguish the fault type more accurately than other wavelets. Besides, the RF-Bior3.1 give the best results for SSFM and TSFM by accuracy values of 99.80% and 99.98%, respectively. It is also found that the sensor type is correlated with the selected mother wavelet.

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References

  1. Mobley RK (2002) An introduction to predictive maintenance. Elsevier, Massachusetts, USA

    Google Scholar 

  2. Selcuk S (2017) Predictive maintenance, its implementation and latest trends. Proc Inst Mech Eng Part B J Eng Manuf 231:1670–1679. https://doi.org/10.1177/0954405415601640

    Article  Google Scholar 

  3. Stenström C, Norrbin P, Parida A, Kumar U (2016) Preventive and corrective maintenance-cost comparison and cost-benefit analysis. Struct Infrastruct Eng 12:603–617. https://doi.org/10.1080/15732479.2015.1032983

    Article  Google Scholar 

  4. Arunraj NS, Maiti J (2007) Risk-based maintenance-techniques and applications. J Hazard Mater 142:653–661. https://doi.org/10.1016/j.jhazmat.2006.06.069

    Article  Google Scholar 

  5. Ahmad R, Kamaruddin S (2012) An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng 63:135–149. https://doi.org/10.1016/j.cie.2012.02.002

    Article  Google Scholar 

  6. Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Sig Process 20:1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012

    Article  Google Scholar 

  7. Zonta T, da Costa CA, da RosaRighi R, de Lima MJ, da Trindade ES, Li GP (2020) Predictive maintenance in the Industry 4.0: a systematic literature review. Comput Ind Eng 150:106889. https://doi.org/10.1016/j.cie.2020.106889

    Article  Google Scholar 

  8. Lee SM, Lee D, Kim YS (2019) The quality management ecosystem for predictive maintenance in the industry 4.0 era. Int J Qual Innov 5:4. https://doi.org/10.1186/s40887-019-0029-5

    Article  Google Scholar 

  9. Ceruti A, Marzocca P, Liverani A, Bil C (2019) Maintenance in aeronautics in an industry 4.0 context: the role of augmented reality and additive manufacturing. J Comput Des Eng 6:516–526. https://doi.org/10.1016/j.jcde.2019.02.001

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. Masoni R, Ferrise F, Bordegoni M, Gattullo M, Uva AE, Fiorentino M et al (2017) Supporting remote maintenance in industry 4.0 through augmented reality. Procedia Manuf 11:1296–1302. https://doi.org/10.1016/j.promfg.2017.07.257

    Article  Google Scholar 

  12. Dalzochio J, Kunst R, Pignaton E, Binotto A, Sanyal S, Favilla J et al (2020) Machine learning and reasoning for predictive maintenance in industry 4.0: current status and challenges. Comput Ind 123:103298. https://doi.org/10.1016/j.compind.2020.103298

    Article  Google Scholar 

  13. Paolanti M, Romeo L, Felicetti A, Mancini A, Frontoni E, Loncarski J (2018) Machine Learning approach for predictive maintenance in industry 4.0. IEEE, pp 1–6

  14. Franciosi C, Iung B, Miranda S, Riemma S (2018) Maintenance for sustainability in the industry 4.0 context: a sco** literature review. IFAC-PapersOnLine 51:903–908. https://doi.org/10.1016/j.ifacol.2018.08.459

    Article  Google Scholar 

  15. Rai VK, Mohanty AR (2007) Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform. Mech Syst Sig Process 21(6):2607–2615. https://doi.org/10.1016/j.ymssp.2006.12.004

    Article  Google Scholar 

  16. Alameh K, Cité N, Hoblos G, Barakat G (2015) Vibration-based fault diagnosis approach for permanent magnet synchronous motors. IFAC-PapersOnLine 48(21):1444–1450. https://doi.org/10.1016/j.ifacol.2015.09.728. (9th IFAC Symposium on Fault Detection, Supervision andSafety for Technical Processes SAFEPROCESS 2015)

    Article  Google Scholar 

  17. Janssens O, Schulz R, Slavkovikj V, Stockman K, Loccufier M, Van de Walle R et al (2015) Thermal image based fault diagnosis for rotating machinery. Infrared Phys Technol 73:78–87. https://doi.org/10.1016/j.infrared.2015.09.004

    Article  Google Scholar 

  18. He D, Cao H, Wang S, Chen X (2019) Time-reassigned synchrosqueezing transform: the algorithm and its applications in mechanical signal processing. Mech Syst Sig Process 117:255–279. https://doi.org/10.1016/j.ymssp.2018.08.004

    Article  Google Scholar 

  19. Bao W, Li F, Tu X, Hu Y, He Z (2020) Second-order synchroextracting transform with application to fault diagnosis. IEEE Trans Instrum Meas. https://doi.org/10.1109/TIM.2020.3045841

    Article  Google Scholar 

  20. Yu K, Ma H, Han H, Zeng J, Li H, Li X et al (2019) Second order multi-synchrosqueezing transform for rub-impact detection of rotor systems. Mech Mach Theory 140:321–349. https://doi.org/10.1016/j.mechmachtheory.2019.06.007

    Article  Google Scholar 

  21. Li Y, Wang Z, Zhao T, Song W (2021) An improved multi-ridge extraction method based on differential synchro-squeezing wavelet transform. IEEE Access 9:96763–96774. https://doi.org/10.1109/ACCESS.2021.3095054

    Article  Google Scholar 

  22. Alpaydin E (2020) Introduction to machine learning. The Mit Press

    MATH  Google Scholar 

  23. Daş DB, Birant D (2021) Ordered physical human activity recognition based on ordinal classification. Turk J Electr Eng Comput Sci 29:2416–2436. https://doi.org/10.3906/elk-2010-75

    Article  Google Scholar 

  24. Flah M, Nunez I, Chaabene WB, Nehdi ML (2021) Machine learning algorithms in civil structural health monitoring: a systematic review. Arch Comput Methods Eng 28:2621–2643. https://doi.org/10.1007/s11831-020-09471-9

    Article  Google Scholar 

  25. Sajedi SO, Liang X (2021) Uncertainty-assisted deep vision structural health monitoring. Computer-Aided Civ Infrastruct Eng 36:126–142. https://doi.org/10.1111/mice.12580

    Article  Google Scholar 

  26. Borzì L, Mazzetta I, Zampogna A, Suppa A, Olmo G, Irrera F (2021) Prediction of freezing of gait in Parkinson’s disease using wearables and machine learning. Sensors 21:614. https://doi.org/10.3390/s21020614

    Article  Google Scholar 

  27. Jourdan T, Debs N, Frindel C (2021) The contribution of machine learning in the validation of commercial wearable sensors for gait monitoring in patients: a systematic review. Sensors 21:4808. https://doi.org/10.3390/s21144808

    Article  Google Scholar 

  28. Chen W, Wang Q, Hesthaven JS, Zhang C (2021) Physics-informed machine learning for reduced-order modeling of nonlinear problems. J Comput Phys 446:110666. https://doi.org/10.1016/j.jcp.2021.110666

    Article  MathSciNet  MATH  Google Scholar 

  29. Faulon JL, Faure L (2021) In silico, in vitro, and in vivo machine learning in synthetic biology and metabolic engineering. Curr Opin Chem Biol 65:85–92. https://doi.org/10.1016/j.cbpa.2021.06.002

    Article  Google Scholar 

  30. Hwang DH, Youn YW, Sun JH, Choi KH, Lee JH, Kim YH (2015) Support vector machine based bearing fault diagnosis for induction motors using vibration signals. J Electr Eng Technol 10:1558–1565. https://doi.org/10.5370/JEET.2015.10.4.1558

    Article  Google Scholar 

  31. Tian J, Morillo C, Azarian MH, Pecht M (2016) Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with K-nearest neighbor distance analysis. IEEE Trans Ind Electr 63:1793–1803. https://doi.org/10.1109/TIE.2015.2509913

    Article  Google Scholar 

  32. Sánchez RV, Lucero P, Vásquez RE, Cerrada M, Macancela JC, Cabrera D (2018) Feature ranking for multi-fault diagnosis of rotating machinery by using random forest and KNN. J Intell Fuzzy Syst 34:3463–3473. https://doi.org/10.3233/JIFS-169526

    Article  Google Scholar 

  33. Shah AK, Yadav A, Malik H (2018) EMD and ANN based intelligent model for bearing fault diagnosis. J Intell Fuzzy Syst 35:5391–5402. https://doi.org/10.3233/JIFS-169821

    Article  Google Scholar 

  34. Zheng J, Pan H, Cheng J (2017) Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mech Syst Sig Process 85:746–759. https://doi.org/10.1016/j.ymssp.2016.09.010

    Article  Google Scholar 

  35. Liu Z, Guo W, Hu J, Ma W (2017) A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM. ISA Trans 66:249–261. https://doi.org/10.1016/j.isatra.2016.11.001

    Article  Google Scholar 

  36. Wei Y, Li Y, Xu M, Huang W (2019) A review of early fault diagnosis approaches and their applications in rotating machinery. Entropy. https://doi.org/10.3390/e21040409

    Article  Google Scholar 

  37. de Jesus Romero-Troncoso R (2017) Multirate signal processing to improve FFT-based analysis for detecting faults in induction motors. IEEE Tran Ind Inform 13:1291–1300. https://doi.org/10.1109/TII.2016.2603968

    Article  Google Scholar 

  38. Dhamande LS, Chaudhari MB (2018) Compound gear-bearing fault feature extraction using statistical features based on time-frequency method. Measurement 125:63–77. https://doi.org/10.1016/j.measurement.2018.04.059

    Article  Google Scholar 

  39. ** X, Fan J, Chow TWS (2019) Fault detection for rolling-element bearings using multivariate statistical process control methods. IEEE Trans Instrum Meas 68:3128–3136. https://doi.org/10.1109/TIM.2018.2872610

    Article  Google Scholar 

  40. Kumar P, Hati AS (2020) Review on machine learning algorithm based fault detection in induction motors. Arch Comput Methods Eng 28(3):1929–1940. https://doi.org/10.1007/s11831-020-09446-w

    Article  Google Scholar 

  41. Jian Y, Qing X, He L, Zhao Y, Qi X, Du M (2019) Fault diagnosis of motor bearing based on deep learning. Adv Mech Eng. https://doi.org/10.1177/1687814019875620

    Article  Google Scholar 

  42. Liu R, Yang B, Zio E, Chen X (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Sig Process 108:33–47. https://doi.org/10.1016/j.ymssp.2018.02.016

    Article  Google Scholar 

  43. Katsifarakis N, Riga M, Voukantsis D, Karatzas K (2016) Computational intelligence methods for rolling bearing fault detection. J Braz Soc Mech Sci Eng 38:1565–1574. https://doi.org/10.1007/s40430-015-0458-6

    Article  Google Scholar 

  44. Hu Q, Qin A, Zhang Q, He J, Sun G (2018) Fault diagnosis based on weighted extreme learning machine with wavelet packet decomposition and KPCA. IEEE Sens J 18:8472–8483. https://doi.org/10.1109/JSEN.2018.2866708

    Article  Google Scholar 

  45. Guo MF, Zeng XD, Chen DY, Yang NC (2018) Deep-learning-based earth fault detection using continuous wavelet transform and convolutional neural network in resonant grounding distribution systems. IEEE Sens J 18:1291–1300. https://doi.org/10.1109/JSEN.2017.2776238

    Article  Google Scholar 

  46. Gangsar P, Tiwari R (2019) Diagnostics of mechanical and electrical faults in induction motors using wavelet-based features of vibration and current through support vector machine algorithms for various operating conditions. J Braz Soc Mech Sci Eng 41:71. https://doi.org/10.1007/s40430-019-1574-5

    Article  Google Scholar 

  47. Islam MMM, Kim JM (2019) Automated bearing fault diagnosis scheme using 2D representation of wavelet packet transform and deep convolutional neural network. Comput Ind 106:142–153. https://doi.org/10.1016/j.compind.2019.01.008

    Article  Google Scholar 

  48. Yang L, Chen H (2019) Fault diagnosis of gearbox based on RBF-PF and particle swarm optimization wavelet neural network. Neural Comput Appl 31:4463–4478. https://doi.org/10.1007/s00521-018-3525-y

    Article  Google Scholar 

  49. Zhang Y, Zhu D, Zhao L (2021) Fault diagnosis of rolling element bearing using ACYCBD based cross correlation spectrum. J Braz Soc Mech Sci Eng 43:447. https://doi.org/10.1007/s40430-021-02955-0

    Article  Google Scholar 

  50. Gangsar P, Tiwari R (2020) Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: a state-of-the-art review. Mech Syst Sig Process. https://doi.org/10.1016/j.ymssp.2020.106908

    Article  Google Scholar 

  51. Shi Z, Yang X, Li Y, Yu G (2021) Wavelet-based synchroextracting transform: an effective TFA tool for machinery fault diagnosis. Control Eng Pract. https://doi.org/10.1016/j.conengprac.2021.104884

    Article  Google Scholar 

  52. Han B, Zhou Y, Yu G (2021) Second-order synchroextracting wavelet transform for nonstationary signal analysis of rotating machinery. Sig Process. https://doi.org/10.1016/j.sigpro.2021.108123

    Article  Google Scholar 

  53. Varanis M, Pederiva R (2017) The influence of the wavelet filter in the parameters extraction for signal classification: an experimental study. Proc Ser Braz Soc Comput Appl Math. https://doi.org/10.5540/03.2017.005.01.0501

    Article  Google Scholar 

  54. Defdaf M, Berrabah F, Chebabhi A, Cherif BDE (2021) A new transform discrete wavelet technique based on artificial neural network for induction motor broken rotor bar faults diagnosis. Int Trans Electr Energy Syst. https://doi.org/10.1002/2050-7038.12807

    Article  Google Scholar 

  55. Varanis M, Pederiva R (2018) Statements on wavelet packet energy-entropy signatures and filter influence in fault diagnosis of induction motor in non-stationary operations. J Braz Soc Mech Sci Eng. https://doi.org/10.1007/s40430-018-1025-8

    Article  Google Scholar 

  56. Liang P, Deng C, Wu J, Yang Z (2020) Intelligent fault diagnosis of rotating machinery via wavelet transform, generative adversarial nets and convolutional neural network. Measurement 159:107768. https://doi.org/10.1016/j.measurement.2020.107768

    Article  Google Scholar 

  57. Shukla R, Kankar PK, Pachori RB (2021) Automated bearing fault classification based on discrete wavelet transform method. Life Cycle Reliab Saf Eng 10:99–111. https://doi.org/10.1007/S41872-020-00151-Y

    Article  Google Scholar 

  58. Lee KM, Vununu C, Moon KS, Lee SH, Kwon KR (2017) Automatic machine fault diagnosis system using discrete wavelet transform and machine learning. J Korea Multimed Soc 20:1299–1311

    Google Scholar 

  59. Malla C, Rai A, Kaul V, Panigrahi I (2019) Rolling element bearing fault detection based on the complex Morlet wavelet transform and performance evaluation using artificial neural network and support vector machine. Noise Vib Worldw 50:313–327. https://doi.org/10.1177/0957456519883280

    Article  Google Scholar 

  60. Sharma S, Tiwari SK, Singh S (2021) Integrated approach based on flexible analytical wavelet transform and permutation entropy for fault detection in rotary machines. Measurement 169:108389. https://doi.org/10.1016/j.measurement.2020.108389

    Article  Google Scholar 

  61. Tao X, Ren C, Wu Y, Li Q, Guo W, Liu R et al (2020) Bearings fault detection using wavelet transform and generalized Gaussian density modeling. Measurement 155:107557. https://doi.org/10.1016/j.measurement.2020.107557

    Article  Google Scholar 

  62. Ding J, Ding C (2019) Automatic detection of a wheelset bearing fault using a multi-level empirical wavelet transform. Measurement 134:179–192. https://doi.org/10.1016/j.measurement.2018.10.064

    Article  Google Scholar 

  63. Jha RK, Swami PD (2022) Failure prognosis of rolling bearings using maximum variance wavelet subband selection and support vector regression. J Braz Soc Mech Sci Eng 44:49. https://doi.org/10.1007/s40430-021-03345-2

    Article  Google Scholar 

  64. Li G, Deng C, Wu J, Chen Z, Xu X (2020) Rolling bearing fault diagnosis based on wavelet packet transform and convolutional neural network. Appl Sci 10:770. https://doi.org/10.3390/app10030770

    Article  Google Scholar 

  65. Narendiranath BT, Himamshu HS, Prabin KN, Rama PD, Nishant C (2017) Journal bearing fault detection based on Daubechies wavelet. Arch Acoust 42:401–414. https://doi.org/10.1515/aoa-2017-0042

    Article  Google Scholar 

  66. Chen B, Shen B, Chen F, Tian H, **ao W, Zhang F et al (2019) Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing. Measurement 131:400–411. https://doi.org/10.1016/j.measurement.2018.07.043

    Article  Google Scholar 

  67. Wang Z, Zhang Q, **ong J, **ao M, Sun G, He J (2017) Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests. IEEE Sens J 17(17):5581–5588. https://doi.org/10.1109/JSEN.2017.2726011

    Article  Google Scholar 

  68. Saari J, Lundberg J, Odelius J, Rantatalo M (2018) Selection of features for fault diagnosis on rotating machines using random forest and wavelet analysis. Insight Non-Destr Test Cond Monit 60:434–442. https://doi.org/10.1784/insi.2018.60.8.434

    Article  Google Scholar 

  69. Strömbergsson D, Marklund P, Berglund K, Saari J, Thomson A (2019) Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings. Wind Energy 22:1581–1592. https://doi.org/10.1002/WE.2390

    Article  Google Scholar 

  70. Toma RN, Kim JM (2020) Article bearing fault classification of induction motors using discrete wavelet transform and ensemble machine learning algorithms. Appl Sci (Switz). https://doi.org/10.3390/APP10155251

    Article  Google Scholar 

  71. Rafiee J, Rafiee MA, Tse PW (2010) Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Syst Appl 37:4568–4579. https://doi.org/10.1016/j.eswa.2009.12.051

    Article  Google Scholar 

  72. Sharma A, Amarnath M, Kankar P (2014) Feature extraction and fault severity classification in ball bearings. J Vib Control 22:1–17. https://doi.org/10.1177/1077546314528021

    Article  Google Scholar 

  73. Nikravesh SMY, Rezaie H, Kilpatrik M, Taheri H (2019) Intelligent fault diagnosis of bearings based on energy levels in frequency bands using wavelet and support vector machines (SVM). J Manuf Mater Process. https://doi.org/10.3390/jmmp3010011

    Article  Google Scholar 

  74. Nath AG, Udmale SS, Singh SK (2021) Role of artificial intelligence in rotor fault diagnosis: a comprehensive review. Artif Intell Rev 54:2609–2668. https://doi.org/10.1007/s10462-020-09910-w

    Article  Google Scholar 

  75. Chen R, Huang X, Yang L, Xu X, Zhang X, Zhang Y (2019) Intelligent fault diagnosis method of planetary gearboxes based on convolution neural network and discrete wavelet transform. Comput Ind 106:48–59. https://doi.org/10.1016/j.compind.2018.11.003

    Article  Google Scholar 

  76. Yan J, Laflamme S, Singh P, Sadhu A, Dodson J (2020) A comparison of time-frequency methods for real-time application to high-rate dynamic systems. Vibration 3:204–216. https://doi.org/10.3390/vibration3030016

    Article  Google Scholar 

  77. Tran H, Noori M, Altabey WA, Wu X (2017) Fault diagnosis of rotating machinery using wavelet-based feature extraction and support vector machine classifier. High Speed Mach. https://doi.org/10.1515/hsm-2017-0003

    Article  Google Scholar 

  78. Burrus CS, Gopinath RA, Guo H (1998) Introduction to wavelets and wavelet transforms: a primer. Prentice Hall, New Jersey, USA

    Google Scholar 

  79. Minsky M, Papert S, Léon B (2017) Perceptrons: an introduction to computational geometry. The MIT Press, Massachusetts, USA

    Book  MATH  Google Scholar 

  80. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  81. Schapire RE (2013) Explaining adaboost. Empirical Inference: Festschrift in Honor of Vladimir N Vapnik, Heidelberg, Germany, pp 37–52. https://doi.org/10.1007/978-3-642-41136-6_5

  82. Riberio FML.: MaFaulDa-machinery fault database. http://www02.smt.ufrj.br/~offshore/mfs/page_01.html#SEC1

  83. Marins MA, Ribeiro FML, Netto SL, da Silva EAB (2018) Improved similarity-based modeling for the classification of rotating-machine failures. J Frankl Inst 355:1913–1930. https://doi.org/10.1016/j.jfranklin.2017.07.038

    Article  MATH  Google Scholar 

  84. Yang X, Xue B, Jia L, Zhang H (2017) Quantitative analysis of pit defects in an automobile engine cylinder cavity using the radial basis function neural network-genetic algorithm model. Struct Health Monit 16:696–710. https://doi.org/10.1177/1475921716680591

    Article  Google Scholar 

  85. Yang M, Sang YF, Liu C, Wang Z (2016) Discussion on the choice of decomposition level for wavelet based hydrological time series modeling. Water. https://doi.org/10.3390/w8050197

    Article  Google Scholar 

  86. Nourani V, Komasi M, Mano A (2009) A multivariate ANN-wavelet approach for rainfall-runoff modeling. Water Resour Manag 23:2877. https://doi.org/10.1007/s11269-009-9414-5

    Article  Google Scholar 

  87. Aussem A, Campbell J, Murtagh F (1998) Wavelet-based feature extraction and decomposition for financial forecasting. J Comput Intell Financ 3:5–12

    Google Scholar 

  88. Kaplun D, Voznesenskiy A, Romanov S, Nepomuceno E, Butusov D (2019) Optimal estimation of wavelet decomposition level for a matching pursuit algorithm. Entropy 21:843. https://doi.org/10.3390/e21090843

    Article  MathSciNet  Google Scholar 

  89. He H, Starzyk JA (2006) A self-organizing learning array system for power quality classification based on wavelet transform. IEEE Trans Power Deliv 21:286–295. https://doi.org/10.1109/tpwrd.2005.852392

    Article  Google Scholar 

  90. Liu D, **ao Z, Hu X, Zhang C, Malik OP (2019) Feature extraction of rotor fault based on EEMD and curve code. Meas J Int Meas Confed 135:712–724. https://doi.org/10.1016/j.measurement.2018.12.009

    Article  Google Scholar 

  91. Shukla S, Yadav RN, Sharma J, Khare S (2015) Analysis of statistical features for fault detection in ball bearing. In: 2015 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–7

  92. Altinors A, Yol F, Yaman O (2021) A sound based method for fault detection with statistical feature extraction in UAV Motors. Appl Acoust 183:108325. https://doi.org/10.1016/j.apacoust.2021.108325

    Article  Google Scholar 

  93. Refaeilzadeh P, Tang L, Liu H (2009) Cross-validation. Springer, US

  94. Yi H, Jiang Q, Yan X, Wang B (2021) Imbalanced classification based on minority clustering synthetic minority oversampling technique with wind turbine fault detection application. IEEE Trans Ind Inform 17:5867–5875. https://doi.org/10.1109/TII.2020.3046566

    Article  Google Scholar 

  95. Sharif I, Khare S (2014) Comparative analysis of Haar and daubechies wavelet for hyper spectral image classification. International Society for Photogrammetry and Remote Sensing, vol XL-8, pp 937–941

  96. Lee BY, Tarng YS (1999) Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current. Int J Adv Manuf Technol 15:238–243

    Article  Google Scholar 

  97. Sharma P, Saxena A (2017) Critical investigations on performance of ANN and wavelet fault classifiers. Cogent Eng. https://doi.org/10.1080/23311916.2017.1286730

    Article  Google Scholar 

  98. Chazal PD, Celler BG, Reilly RB (2000) Using wavelet coefficients for the classification of the electrocardiogram. In: Annual international conference of the IEEE engineering in medicine and biology-proceedings, vol 1, pp 64–67. https://doi.org/10.1109/IEMBS.2000.900669

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  1. Department of Aeronautics, Air NCO Higher Vocational School, National Defence University, Izmir, Türkiye

    Oguzhan Das

  2. Department of Computer Programming, Ege University, Izmir, Türkiye

    Duygu Bagci Das

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Das, O., Bagci Das, D. Smart machine fault diagnostics based on fault specified discrete wavelet transform. J Braz. Soc. Mech. Sci. Eng. 45, 55 (2023). https://doi.org/10.1007/s40430-022-03975-0

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  • DOI: https://doi.org/10.1007/s40430-022-03975-0

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