Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
The International Journal of Advanced Manufacturing Technology
Erschienen in: The International Journal of Advanced Manufacturing Technology 9-10/2022

06.10.2021 | ORIGINAL ARTICLE

Basic tools for vibration analysis with applications to predictive maintenance of rotating machines: an overview

verfasst von: Theodor D. Popescu, Dorel Aiordachioaie, Anisia Culea-Florescu

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 9-10/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The paper presents some basic tools for vibration signals with application in predictive maintenance of rotating machines. After an overview of the maintenance approach, the condition monitoring in predictive maintenance is discussed. Also, signal processing in vibration monitoring, making use of some basic tools as change detection, independent component analysis, time-frequency analysis, and energy distribution in time-frequency plane are presented. These techniques can be combined in a general approach, offering new possibilities for more robust detection of changes in vibration signals and assuring proactive actions in predictive maintenance.
Vorheriger Artikel The central strain analytical modeling and analysis for the plate rolling process
Nächster Artikel Job rotation and human–robot collaboration for enhancing ergonomics in assembly lines by a genetic algorithm

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Stankovic L (2001) A measure of some time-frequency distributions concentration. Signal Process: 621–631
2.
Antoni J (2005) Blind separation of vibration components: Principles and demonstrations. Mech Syst Signal Process: 1166–1180
3.
Cohen L (1995) Time-frequency distribution. Prentice Hall, New York
4.
Aviyente S (2004) Information processing on the time-frequency plane. Proc. IEEE International Conference Acoustics, Speech, and Signal Processing (ICASSP ’04), pp 617–620
5.
Lacey SJ (2010) The role of vibration monitoring in predictive maintenance. FAG Technical Publication, Schaeffler Limited UK
6.
7.
Wang D (2018) Spectral L2/L1 norm: a new perspective for spectral kurtosis for characterizing non-stationary signals. Mech Syst Signal Process 104:290–293CrossRef
8.
Antoni J (2006) The spectral kurtosis: a useful tool for characterising non-stationary signals. Mech Syst Signal Process 20:282–307CrossRef
9.
Borghesani P, Pennacchi P, Chatterton S (2014) The relationship between kurtosis- and envelope-based indexes for the diagnostic of rolling element bearings. Mech Syst Signal Process 43:25–43CrossRef
10.
Randall RB, Antoni J, Chobsaard S (2001) The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals. Mech Syst Signal Process 15:945–962CrossRef
11.
Wang D, Zhao X, Kou L. -L, Qin Y, Zhao Y, Tsui K. -L (2019) A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis. Mech Syst Signal Process 122:754–768CrossRef
12.
13.
Popescu TD (2010) Blind separation of vibration signals and source change detection - application to machine monitoring. Appl Math Model: 3408–3421
14.
Popescu TD (2014) Signal segmentation using changing regression models with application in seismic engineering. Digital Signal Processing: 14–26
15.
Popescu TD (2011) Detection and diagnosis of model parameter and noise variance changes with application in seismic signal processing. Mech Syst Signal Process: 1598–1616
16.
Basseville M, Nikiforov I (1993) Detection of abrupt changes - theory and applications. Prentice Hall: N.J.
17.
Gustafsson F (2001) Adaptive filtering and change detection. Wiley
18.
Ohlson H, Ljung L, Boyd S (2010) Segmentation ARX-models using sum-of-norms regularization. Automatica IFAC 46(6):1107–1111MathSciNetCrossRef
19.
Hubert P, Padovese L, Stern JM (2018) A sequential algorithm for signal segmentation. Entropy MDPI: 1–20
20.
Zimroz R, Madziarz M, Zak G, Wylomanska A, Obuchowski J (2015) Seismic signal segmentation procedure using time-frequency decomposition and statistical modeling. Journal of Vibroengineering 17:3111–3121
21.
Wang D, Tsui K. -L, Qin Y (2019) Optimization of segmentation fragments in empirical wavelet transform and its applications to extracting industrial bearing fault features. Measurement 133:328–340CrossRef
22.
Hyvärinen A., Karhunen J, Oja E (2001) Independent component analysis. John Wiley
23.
Parra L, Spence C (2000) Convolutive blind source separation of non-stationary signals. IEEE Trans Speech Audio Process 6:320–327CrossRef
24.
Der R (2001) Blind signal separation. Technical Report, Department of Electrical & Computer Engineering McGill University
25.
Belouchrani A, Abed Meraim K, Cardoso JF, Moulines E (1997) A blind source separation technique using second - order statistics. IEEE Trans Signal Process 45:434–444CrossRef
26.
Cardoso JF, Souloumiac A (1993) Blind beamforming for non gaussian signals. IEE Proceedings-F: 362–370
27.
Hyvarinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9:1483–1492CrossRef
28.
Gribonval R, Benaroya L, Vincent E, Fevotte C (2002) Proposal for performance measurement in source separation. Publication Interne, No. 1501 IRISA
29.
Heeris J (2007) Single channel blind source separation using independent subspace analysis, Bachelor of Engineering Thesis, School of Electrical, Electronic and Computer Engineering. The University of Western Australia, Australia
30.
Hong H, Liang M (2007) Separation of fault features from a single-channel mechanical signal mixture using wavelet decomposition. Mech Syst Signal Process 21:2025–2040CrossRef
31.
He P, She T, Li W, Yuan W (2018) Single channel blind source separation on the instantaneous mixed signal of multiple dynamic sources. Mech Syst Signal Process 113:22–35CrossRef
32.
Wang D, Guo W, Tse PW (2016) An enhanced empirical mode decomposition method for blind component separation of a single-channel vibration signal mixture. J Vib Control 22:2603–2618CrossRef
33.
Hild IIKE, Erdogmus D, Principe JC (2006) An analysis of entropy estimators for blind source separation. Signal Process 86:182–194CrossRef
34.
Sejdić E, Djurović I, Jiang J (2009) Time-frequency feature representation using energy concentration: An overview of recent advances. Digital Signal Processing: 153–183
35.
Mallat SG (1999) A wavetet tour of signal processing. Academic Press, CambridgeMATH
36.
Gröchenig K. (2001) Foundations of time-frequency analysis. Birkhäuser
37.
Daubechies I (1992) Ten lectures on wavelets. SIAM
38.
Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process: 3397–3415
39.
Antoni J (2003) On the benefits of the Wigner-Ville spectrum for analysing certain types of vibration signals. In: Proc. WESPAC 8, The 8-th Western Pacific Acoustics Conference, Melbourne
40.
Boashash B (2003) Time frequency signal analysis and processing: A compressive reference. Elsevier
41.
Cohen L (1989) Time frequency distribution: a review, Proc. of IEEE, pp 941–981
42.
Flandrin P (1998) Temps-fréquence. Hermes
43.
Hlwatsch F, Boudreaux-Bartels GF (1992) Linear and quadratic time-frequency signal representations. IEEE Signal Process Mag: 21–67
44.
Hussain M, Bouashash B (2000) Multi-component IF estimation. Proceedings of the 10th IEEE Workshop on statistical signal and array processing - SSAP, pp 559–563
45.
Jeong J, Williams WJ (1992) Kernel design for reduced interference distributions. IEEE Trans Signal Process, pp 402–4012
46.
Yang Y, Peng Z, Zhang W, Meng G (2019) Parameterised time-frequency analysis methods and their engineering applications: a review of recent advances. Mech Sys Signal Process 119:182–221CrossRef
47.
Feng Z, Liang M, Chu F (2013) Recent advances in time-frequency analysis methods for machinery fault diagnosis: A review with application examples. Mech Syst Signal Process 38:165–205CrossRef
48.
Al-Badour F, Sunar M, Cheded L (2011) Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques. Mech Syst Signal Process 25:2083–2101CrossRef
49.
Manson G, Staszewski WJ, Barszcz T, Worden K (2015) A time-frequency analysis approach for condition monitoring of a wind turbine gearbox under varying load conditions. Mech Syst Signal Process 64-65:188–216CrossRef
50.
Cohen L (1990) Distributions concentrated along the instantaneous frequency, SPIE. Adv. Signal Process. Algebra Arch Imp: 149–157
51.
Jones DL, Parks TW (1990) A high resolution data-adaptive time-frequency representation. IEEE Trans. Signal Process
52.
Sang TH, Williams WJ (1995) Rényi information and signal dependent optimal kernel design. Proc. of the ICASSP: 997–1000
53.
Williams WJ, Brown ML, Hero AO (1991) Uncertainity, information and time-frequency distributions. SPIE Adv Signal Process Algebra Arch Imp, pp 144–156
54.
Flandrin P, Baraniuk RG, Michel O (1994) Time-frequency complexity and information. Proceedings of the ICASSP: 329–332
55.
Baraniuk RG, Jones DL, A signal dependent time-frequency representation (1993) Optimal kernel design. IEEE Trans Signal Process: 1589–1602
56.
Baraniuk RG, Jones DL (1993) Signal-dependent time-frequency analysis using radially Gaussian kernel. IEEE Trans Signal Process: 263–284
57.
Jones DL, Baraniuk RG (1995) An adaptive optimal-kernel time-frequency representation. IEEE Trans Signal Process: 2361–2372
58.
Baraniuk RG, Flandrin P, Janssen AJEM, Michel OJJ (2001) Measuring time-frequency information content using the Rényi entropies. IEEE Trans Inf Theory: 1391–1409
59.
Liuni M, Röbel A, Romito M, Rodet X (2011) Rényi information measures for spectral change detection, CASSP, Prague, Czech Republic hal-01161299
60.
Bercher JF (2008) On some entropy functionals derived from Rényi information divergence. Information Sciences, vol 178. Elsevier, Amsterdam. 2489-2506hal-00276749MATH
61.
Eisberg R, Resnick R (1974) Quantum physics. Wiley
62.
Yu G (2020) A concentrated time-frequency analysis tool for bearing fault diagnosis. IEEE Trans Instrum Meas 69:371–381CrossRef
63.
Baur M, Albertelli P, Monno M (2020) A review of prognostics and health management of machine tools. Int J Adv Manuf Technol 107:2843–2863CrossRef
64.
Gao Z, Cecati C, Ding S (2015) A survey of fault diagnosis and fault-tolerant techniques-part i: fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron: 3757–3767
65.
Rehorn A, Jiang J, Orban P (2005) State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26:693–710CrossRef
66.
Ibarra-Zarate D, Tamayo-Pazos O, Vallejo-Guevara A (2019) Bearing fault diagnosis in rotating machinery based on cepstrum pre-whitening of vibration and acoustic emission. Int J Adv Manuf Technol 104:4155–4168CrossRef
67.
Tarek K, Abderrazek D, Khemissi BM, et al. (2020) Comparative study between cyclostationary analysis, EMD, and CEEMDAN for the vibratory diagnosis of rotating machines in industrial environment. Int J Adv Manuf Technol 109:2747–2775CrossRef
68.
Youcef Khodja A, Guersi N, Saadi MN, et al. (2020) Rolling element bearing fault diagnosis for rotating machinery using vibration spectrum imaging and convolutional neural networks. Int J Adv Manuf Technol 106:1737–1751CrossRef
69.
Zhang Z, Wang Y, Wang K (2013) Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks. Int J Adv Manuf Technol 68:763–773CrossRef
70.
Duan Z, Wu T, Guo S, et al. (2018) Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review. Int J Adv Manuf Technol 96:803–819CrossRef
71.
Babouri MK, Ouelaa N, Kebabsa T, et al. (2019) Application of the cyclostationarity analysis in the detection of mechanical defects: comparative study. Int J Adv Manuf Technol 103:1681– 1699CrossRef
72.
Popescu T. h. D, Aiordachioaie D (2019) Fault detection of rolling element bearings using optimal segmentation of vibrating signals. Mech Syst Signal Process: 370–391
73.
Aiordachioaie D, Popescu TD VIBROCHANGE - A development system for condition monitoring based on advanced techniques of signal processing, Int J Adv Manuf Technol: 919-936
74.
Popescu TD (2010) Analysis of traffic-induced vibrations by blind source separation with application in building monitoring. Math Comput Simul: 2392–2403
75.
Popescu TD (2011) A new approach for dam monitoring and surveillance using blind source separation. Int J Innov Comput Inf Control: 3811–3824
76.
Popescu TD, Aiordachioaie D (2017) New procedure for change detection operating on Rényi entropy with application in seismic signals processing, Circuits, Systems, and Signal Processing: 3778–3798
77.
Popescu TD, Aiordachioaie D (2013) Signal segmentation in time-frequency plane using Rényi entropy - application in seismic signal processing. In: Proceedings of The 2-nd IEEE International Conference on Control and Fault-Tolerant Systems (SysTol13), Nice, France, pp 312–317
78.
Zhang B, et al. (2008) Rolling element bearing feature extraction and anomaly detection based on vibration monitoring. Proceedings of The 16-th Mediterranean conference on control and automation, pp 1792–1797
79.
Howard I, Howard IM (1994) A review of rolling element bearing vibration: detection, diagnosis and prognosis. DSTO Aeronautical and maritime research laboratory, Melbourne
80.
Shi DF, Wang WJ, Qu LS (2004) Defect detection for bearings using envelope spectra of wavelet transform. J Vib Acoust: pp 567–573
81.
Ghafari SH (2007) A fault diagnosis system for rotary machinery supported by rolling element bearings. PhD Thesis, University of Waterloo
82.
Yang B-S, Lim D-S, Tan ACCT (2005) Vibex: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table. Expert Syst Appl: 735–742
83.
Ebersbach S, Peng Z (2008) Expert system development for vibration analysis in machine condition monitoring. Expert Syst Appl: 291–299
84.
Lei Y, He Z, Zi Y (2008) A new approach to intelligent fault diagnosis of rotating machinery. Expert Sys Appl: 1593–1600
85.
Sun Q, Chen P, Zhang D, Xi F (2004) Pattern recognition for automatic machinery fault diagnosis. J. Vib. Acoust: 307–316
86.
Edwards S, Lees AW, Friswell IM (1998), Fault diagnosis of rotating machinery. Shock and vibration digest
87.
Lin J, Qu LL (2000) Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis. J Sound Vib: 135–148
88.
Kim YY, Hong JC, Lee NY (2001) Frequency response function estimation via a robust wavelet de-noising method. J. Sound Vib 635–649
89.
Lin J, Zuo MJ, Fyfe KR (2004) Mechanical fault detection based on the wavelet de-noising technique. J Vib Acoust: 9–16
90.
Singh GK, Ahmed SAKS (2004) Vibration signal analysis using wavelet transform for isolation and identification of electrical faults in induction machine. Electric Power Sys Res: 119–136
91.
Luo GY, Osypiw D, Irle M (2003) On-line vibration analysis with fast continuous wavelet algorithm for condition monitoring of bearing. J Vib Control: 931–947
92.
Mallat S, Hwang WL (1992) Singularity detection and processing with wavelets. IEEE Trans Inf Theory: 617–643
93.
Donoho DL (1995) De-noising by soft-thresholding. IEEE Trans Inf Theory: 613–627
94.
Kazzaz SASA, Singh GK (2003) Experimental investigations on induction machine condition monitoring and fault diagnosis using digital signal processing techniques. Electric Power Sys Res: 197–221
95.
Clayton EH, Koh B-H, Xing G, Fok C-L, Dyke SJ, Lu C (2005) Damage detection and correlation-based localization using wireless mote sensors
96.
Parlar J (2010) Vibration analysis & vibration screens: theory & practice, PhD Thesis, McMaster University, Canada
97.
de Moura EP, Souto CR, Silva AA, Irmão MAS (2011) Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses. Mech. Syst. Signal Process: 1765–1772
98.
Ahmed M, Baqqar M, Gu F, Ball AD (2012) Fault detection and diagnosis using principal component analysis of vibration data from a reciprocating compressor. Proceedings of UKACC International Conference on Control: 461–466
99.
Chopade SA, Gaikwad JA, Kulkarni JV (2016) Bearing fault detection using PCA and wavelet based envelope analysis. Proc The 2-nd international conference on applied and theoretical computing and communication technology (iCATccT), pp 248–253
Metadaten
Titel
Basic tools for vibration analysis with applications to predictive maintenance of rotating machines: an overview
verfasst von
Theodor D. Popescu
Dorel Aiordachioaie
Anisia Culea-Florescu
Publikationsdatum
06.10.2021
Verlag
Springer London
Erschienen in
The International Journal of Advanced Manufacturing Technology / Ausgabe 9-10/2022
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-021-07703-1

Weitere Artikel der Ausgabe 9-10/2022

The International Journal of Advanced Manufacturing Technology 9-10/2022 Zur Ausgabe

ORIGINAL ARTICLE

Circular manufacturing 4.0: towards internet of things embedded closed-loop supply chains

ORIGINAL ARTICLE

Rapid residual stress prediction and feedback control during fused deposition modeling of PLA

ORIGINAL ARTICLE

The central strain analytical modeling and analysis for the plate rolling process

ORIGINAL ARTICLE

Numerical and experimental study on the hot cross wedge rolling of Ti-6Al-4V vehicle lower arm preform

ORIGINAL ARTICLE

A method of thermal error prediction modeling for CNC machine tool spindle system based on linear correlation

ORIGINAL ARTICLE

Optimization of squeeze casting process of gearbox cover based on FEM and Box-Behnken design

Premium Partner

    Marktübersichten

    Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen. 

    Zur Marktübersicht
    Bildnachweise