RESEARCH PAPER
Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system
1
Poznan University of Technology, Faculty of Civil and Transport Engineering, Institute of Transport, ul. Piotrowo 3, 61-138 Poznań, Poland
Publication date: 2022-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(1):70-79
HIGHLIGHTS
- Vibration measurements of the drive shaft bearings in selected vehicle during operation.
- Vibration signal processing and analysis in time and frequency domains.
- Selection of the most sensitive diagnostic parameter based on decision trees.
- On-board diagnostic algorithm to assess the technical condition of drive shaft bearings.
KEYWORDS
ABSTRACT
Currently, one of the trends in the automotive industry is to make vehicles as autonomous as possible. In particular, this concerns the implementation of complex and innovative selfdiagnostic systems for cars. This paper proposes a new diagnostic algorithm that evaluates
the performance of the drive shaft bearings of a road vehicle during use. The diagnostic parameter was selected based on vibration measurements and machine learning analysis results. The analyses included the use of more than a dozen time domain features of vibration signal in different frequency ranges. Upper limit values and down limit values of the diagnostic parameter were determined, based on which the vehicle user will receive information about impending wear and total bearing damage. Additionally, statistical verification of the developed model and validation of the results were performed.
REFERENCES (43)
1.
Abid F Ben, Sallem M, Braham A. Optimized SWPT and Decision Tree for Incipient Bearing Fault Diagnosis. 2019 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), IEEE: 2019: 231-236, https://doi.org/10.1109/STA.20....
2.
Ahmed U, Ali F, Jennions I. A review of aircraft auxiliary power unit faults, diagnostics and acoustic measurements. Progress in Aerospace Sciences 2021; 124: 100721, https://doi.org/10.1016/j.paer....
3.
Amarnath M, Sugumaran V, Kumar H. Exploiting sound signals for fault diagnosis of bearings using decision tree. Measurement 2013; 46(3): 1250-1256, https://doi.org/10.1016/j.meas....
4.
Andria G, Attivissimo F, Di Nisio A et al. Development of an automotive data acquisition platform for analysis of driving behavior. Measurement: Journal of the International Measurement Confederation 2016; 93: 278-287, https://doi.org/10.1016/j.meas....
5.
Bi X, Cao S, Zhang D. Diesel Engine Valve Clearance Fault Diagnosis Based on Improved Variational Mode Decomposition and Bispectrum. Energies 2019; 12(4): 661, https://doi.org/10.3390/en1204....
6.
Borucki S, Cichoń A, Majchrzak H, Zmarzły D. Evaluation of the Technical Condition of the Active Part of the High Power Transformer Based on Measurements and Analysis of Vibroacoustic Signals. Archives of Acoustics 2017; 42(2): 313-320, https://doi.org/10.1515/aoa-20....
7.
Cai B, Sun X, Wang J et al. Fault detection and diagnostic method of diesel engine by combining rule-based algorithm and BNs/BPNNs. Journal of Manufacturing Systems 2020; 57: 148-157, https://doi.org/10.1016/j.jmsy....
8.
Cempel C. Limit value in the practice of machine vibration diagnostics. Mechanical Systems and Signal Processing 1990; 4(6): 483-493, https://doi.org/10.1016/0888-3....
9.
Cempel C. Vibroacoustic condition monitoring (Ellis Horwood Series in Mechanical Engineering). Warsaw, Ellis Horwood: 1993.
10.
Cempel C. Vibroacoustical diagnostics of machinery: An outline. Mechanical Systems and Signal Processing 1988; 2(2): 135-151, https://doi.org/10.1016/0888-3....
11.
Corni I, Symonds N, Wood R J K et al. Real-time on-board condition monitoring of train axle bearings. Stephenson Conference Research for Railways 2015 2015; (17): 477-489.
12.
Czechyra B, Firlik B. On-line monitoring system of the technical condition of the infrastructure and running gear of a light rail vehicle (in Polish). Communication Review 2014; 2: 6-10.
13.
Donelson J, Dicus R L. Bearing defect detection using on-board accelerometer measurements. ASME/IEEE 2002 Joint Rail Conference, RTD 2002 2002: 95-102, https://doi.org/10.1115/RTD200....
14.
Firlik B, Czechyra B, Chudzikiewicz A. Condition monitoring system for light rail vehicle and track. Key Engineering Materials 2012; 518: 66-75, https://doi.org/10.4028/www.sc....
15.
Gharesi N, Arefi M M, Ebrahimi Z et al. Analyzing the Vibration Signals for Bearing Defects Diagnosis Using the Combination of SGWT Feature Extraction and SVM. IFAC-PapersOnLine 2018; 51(24): 221-227, https://doi.org/10.1016/j.ifac....
16.
Gill A. Optimisation of the technical object maintenance system taking account of risk analysis results. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2017; 19(3): 420-431, https://doi.org/10.17531/ein.2....
18.
Holguín-Londoño M, Cardona-Morales O, Sierra-Alonso E F et al. Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis. Mathematical Problems in Engineering 2016, https://doi.org/10.1155/2016/7....
19.
Immovilli F, Cocconcelli M, Bellini A, Rubini R. Detection of generalized-roughness bearing fault by spectral-kurtosis energy of vibration or current signals. IEEE Transactions on Industrial Electronics 2009; 56(11): 4710-4717, https://doi.org/10.1109/TIE.20....
20.
Kaul S. Crack diagnostics in beams using wavelets, kurtosis and skewness. Nondestructive Testing and Evaluation 2014; 29(2): 99-122, https://doi.org/10.1080/105897....
21.
Liu H, Li D, Yuan Y et al. Fault diagnosis for a bearing rolling element using improved VMD and HT. Applied Sciences 2019, https://doi.org/10.3390/app907....
22.
Liu N, Liu B, Xi C. Fault diagnosis method of rolling bearing based on the multiple features of LMD and random forest. IOP Conference Series: Materials Science and Engineering 2020, https://doi.org/10.1088/1757-8....
23.
Lorenzo F De, Calabro M. Kurtosis : A Statistical Approach to Identify Defect in Roller Bearings. 2nd International Conference on Marine Research and Transportation 2007; 3: 17-24.
24.
Merkisz J, Rychter M. Basic proceeding of diagnosis and strategy of decision on OBD II system. AVEC - International Symposium on Advanced Vehicle Control, Hiroshima, 2002.
25.
Niziński S, Michalski R. Diagnostics of technical objects (in Polish). Radom (Poland), ITeE: 2002.
26.
Nowakowski T, Komorski P, Szymański G M, Tomaszewski F. Wheel-flat detection on trams using envelope analysis with Hilbert transform. Latin American Journal of Solids and Structures 2019, https://doi.org/10.1590/1679-7....
27.
Nowakowski T, Motyl M, Babiak A. Simplified diagnostics of the drive system in the operation of a rail vehicle. Railway Reports 2019; 2(182): 49-54, https://doi.org/10.36137/1824p.
29.
Sankavaram C, Kodali A, Pattipati K R, Singh S. Incremental classifiers for data-driven fault diagnosis applied to automotive systems. IEEE Access 2015; 3: 407-419, https://doi.org/10.1109/ACCESS....
30.
Sawczuk W, Szymański G M. Diagnostics of the railway friction disc brake based on the analysis of the vibration signals in terms of resonant frequency. Archive of Applied Mechanics 2017; 87(5): 801-815, https://doi.org/10.1007/s00419....
31.
Smith C, Akujuobi C M, Hamory P, Kloesel K. An approach to vibration analysis using wavelets in an application of aircraft health monitoring. Mechanical Systems and Signal Processing 2007; 21(3): 1255-1272, https://doi.org/10.1016/j.ymss....
32.
Soy H, Toy I. Design and implementation of smart pressure sensor for automotive applications. Measurement: Journal of the International Measurement Confederation 2021, https://doi.org/10.1016/j.meas....
33.
Szymański G M, Josko M, Tomaszewski F, Filipiak R. Application of time-frequency analysis to the evaluation of the condition of car suspension. Mechanical Systems and Signal Processing 2015; 58-59: 298-307, https://doi.org/10.1016/j.ymss....
34.
Szymański G. Problems in diagnostics of internal combustion engines with the use of resonance vibrations (in Polish). Poznan, Poznan University of Technology: 2013.
35.
Szymański G M, Tabaszewski M. Engine valve clearance diagnostics based on vibration signals and machine learning methods. Eksploatacja i Niezawodnosc – Maintenance and Reliability 2020; 22(2): 331-339, https://doi.org/10.17531/ein.2....
36.
Tabaszewski M. Identification of rolling bearing condition by means of a classification tree. Vibrations in Physical Systems 2019; 30(2): 1-8.
37.
Theissler A, Pérez-Velázquez J, Kettelgerdes M, Elger G. Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliability Engineering and System Safety 2021; 215: 107864, https://doi.org/10.1016/j.ress....
38.
Wang H, Chen P. Fault diagnosis method based on kurtosis wave and information divergence for rolling element bearings. WSEAS Transactions on Systems 2009; 8(10): 1155-1165.
39.
Wu J Da, Liao S Y. A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower. Expert Systems with Applications 2011; 38(1): 545-552, https://doi.org/10.1016/j.eswa....
40.
Yu D, Cheng J, Yang Y. Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mechanical Systems and Signal Processing 2005; 19(2): 259-270, https://doi.org/10.1016/S0888-....
41.
Zhao X, Yang Z, Pan B et al. Analysis of excitation source characteristics and their contribution in a 2-cylinder diesel engine. Measurement: Journal of the International Measurement Confederation 2021; 176(February): 109195, https://doi.org/10.1016/j.meas....
42.
Zhao Y, Liu P, Wang Z et al. Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods. Applied Energy 2017; 207: 354-362, https://doi.org/10.1016/j.apen....
43.
Żółtowski B, Łukasiewicz M. Machine vibration diagnostics (in Polish). Bydgoszcz, Scientific Publisher of the Institute of Sustainable Technologies - National Research Institute: 2012.
CITATIONS (5):
1.
Supply Sequence Modelling Using Hidden Markov Models
Anna Borucka, Edward Kozłowski, Rafał Parczewski, Katarzyna Antosz, Leszek Gil, Daniel Pieniak
Applied Sciences
Anna Borucka, Edward Kozłowski, Rafał Parczewski, Katarzyna Antosz, Leszek Gil, Daniel Pieniak
Applied Sciences
2.
Roller damage detection method based on the measurement of transverse vibrations of the conveyor belt
Piotr Bortnowski, Robert Król, Maksymilian Ozdoba
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Piotr Bortnowski, Robert Król, Maksymilian Ozdoba
Eksploatacja i Niezawodnosc - Maintenance and Reliability
3.
Vibration-based identification of engine valve clearance using a convolutional neural network
Maciej Tabaszewski, Grzegorz Szymański, Tomasz Nowakowski
Archives of Transport
Maciej Tabaszewski, Grzegorz Szymański, Tomasz Nowakowski
Archives of Transport
4.
Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System
Krzysztof Prażnowski, Jarosław Mamala, Adam Deptuła, Anna Deptuła, Andrzej Bieniek
Sensors
Krzysztof Prażnowski, Jarosław Mamala, Adam Deptuła, Anna Deptuła, Andrzej Bieniek
Sensors
Share
RELATED ARTICLE
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
