Abstract
Reliable fault diagnosis of mechanical drives can become nontrivial task in case of restricted instrumentation and variable operating conditions. Under such circumstances changes in the calculated features can not be unambiguously associated with change in system condition. In this paper we propose a feature appropriate for diagnosing faults in mechanical drives that is robust to fluctuations in operating conditions. Therefore, its time evolution seems to be correlated only with the machine condition. Instead of relying on spectral properties of the vibration signal, we rather observe changes in the statistical patterns of the derived distribution functions. The effectiveness of the algorithm was evaluated on three datasets comprising both gear and bearing faults under constant and variable load.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jardine AKS, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20:1483–1510
Bartelmus W, Zimroz R (2009) A new feature for monitoring the condition of gearboxes in non–stationary operating conditions. Mech Syst Signal Process 23(5):1528–1534
Zimroz R, Bartelmus W, Barszcz T, Urbanek J (2014) Diagnostics of bearings in presence of strong operating conditions non-stationarity—a procedure of load-dependent features processing with application to wind turbine bearings. Mech Syst Signal Process 46(1):16–27
Villa LF, Reñones A, Perán JR, de Miguel LJ (2012) Statistical fault diagnosis based on vibration analysis for gear test-bench under non-stationary conditions of speed and load. Mech Syst Signal Process 29:436–446
Chen P, Taniguchi M, Toyota T, He Z (2005) Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming. Mech Syst Sign Process 19(1):175–194
Boškoski P, Juričić Ð (2012) Fault detection of mechanical drives under variable operating conditions based on wavelet packet rényi entropy signatures. Mech Syst Signal Process 31:369—381
Wang W (2001) Early detection of gear tooth cracking using the resonance demodulation technique. Mech Syst Signal Process 15:887–903
GfT. Arbeitsblatt 7 tribologie definitionen, begriffe, prüfung (in german). Technical report 7, GfT, 2002
Randall RB (1982) A new method of modeling gear faults. J Mech Des 104:259–267
Boškoski P Juričić Ð (2014) Inverse gaussian mixtures models of bearing vibrations under local faults. Mech Syst Signal Process, page Under review
Antoni J, Randall RB (2003) A stochastic model for simulation and diagnostics of rolling element bearings with localized faults. J Vib Acoust 125(3):282–289
Rényi (1960) On measures of information and entropy. In: 4th Berkeley Symp Math Stat Prob 1:547–561
Basseville M (2013) Divergence measures for statistical data processing–an annotated bibliography. Signal Process 93:621–633
Lee J, Qiu H, Yu G, Lin J, Rexnord Technical Services (2007) Bearing data set, IMS, University of Cincinnati. NASA Ames, Moffett Field, CA
Acknowledgment
The authors acknowledge the financial support of the Slovenian Research Agency through the Research Program P2-0001 and the Ministry of Education, Science and Sport for support of the Eurostars project PRODISMON through grant 2130-13-090007.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Dolenc, B., Boškoski, P., Juričić, Ð. (2016). Robust Information Indices for Diagnosing Mechanical Drives Under Non-stationary Operating Conditions. In: Chaari, F., Zimroz, R., Bartelmus, W., Haddar, M. (eds) Advances in Condition Monitoring of Machinery in Non-Stationary Operations. CMMNO 2014. Applied Condition Monitoring, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-20463-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-20463-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20462-8
Online ISBN: 978-3-319-20463-5
eBook Packages: EngineeringEngineering (R0)