Traction motor roller bearings are one of the critical structures for health monitoring of high-speed train vehicles. The technique of motor current signature analysis (MCSA) is a novel way to monitor the mechanical system with induction machines in recent years. In this paper, a fault diagnosis method for motor bearings in high-speed railway vehicles based on MCSA is proposed. It is a sensor-less way for the health monitoring of motor bearings because the induction machine itself can be considered as a sensor. The weakness that vibration data is sensitive to measuring points is avoided. To verify the feasibility of the proposed method, a set of experiments using an induction machine with faulty bearings from a real high-speed train are processed. Compared with the examples under healthy conditions in the time domain, the RMS (Root Mean Square) values of motor stator currents under faulty conditions show an around 20% increase. The modulation phenomena usually happen on motor currents according to both theoretical analysis and practical measurements. A demodulation preprocessing is applied to capture the fault-related harmonic components in the frequency domain. To eliminate the influence of preprocessing algorithms on the result, three different demodulation techniques (HT, WT, and TEO) are compared. The signal processing results prove that it is feasible to monitor the health status of motor bearings in high-speed train based on MCSA.
ChengYZhouNZhangW, et al. (2018) Application of an improved minimum entropy deconvolution method for railway rolling element bearing fault diagnosis. Journal of Sound and Vibration425: 53–69.
CiszewskiTSwędrowskiL (2012) Comparison of induction motor bearing diagnostic test results through vibration and stator current measurement. Computer Applications in Engineering Education10: 170.
4.
CombetF (2014) Gear fault diagnosis by motor current analysis–application to industrial cases. International Gear Conference7: 969–975.
5.
EntezamiMRobertsCWestonP, et al. (2020) Perspectives on railway axle bearing condition monitoring. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit234(1): 17–31.
6.
Garcia-BracamonteJERamirez-CortesJMJesus Rangel-MagdalenoJ, et al. (2019) An approach on MCSA-based fault detection using independent component analysis and neural networks. IEEE Transactions on Instrumentation and Measurement68(5): 1353–1361.
7.
HuNQXiaLRGuFS, et al. (2011) A novel transform demodulation algorithm for motor incipient fault detection. IEEE Transactions on Instrumentation and Measurement60(2): 480–487.
8.
HuangWSunHLiuY, et al. (2017) Feature extraction for rolling element bearing faults using resonance sparse signal decomposition. Experimental Techniques41(3): 251–265.
9.
HuangYHuangCDingJ, et al. (2019a) Fault diagnosis on railway vehicle bearing based on fast extended singular value decomposition packet. Measurement152: 107277.
10.
HuangYLinJLiuZ, et al. (2019b) A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis. Journal of Sound and Vibration444: 216–234.
11.
KiakojouriALuZMirringP, et al. (2023) A novel hybrid technique combining improved cepstrum pre-whitening and high-pass filtering for effective bearing fault diagnosis using vibration data. Sensors23(22): 27.
12.
KlepkaADziedziechKMrówkaJ, et al. (2021) Experimental investigation of modulation effects for contact-type interfaces in vibro-acoustic modulation tests. Structural Health Monitoring20(3): 917–930.
13.
LiuHShengFLuX, et al. (2024) MCSA-based fault diagnosis for rotary parts in servo motion system: approach and experimental study. Journal of Physics: Conference Series2762(1): 012055.
14.
Morales-PerezCRangel-MagdalenoJPeregrina-BarretoH, et al. (2020) Bearing fault detection on IM using MCSA and sparse representation. In: 2020 IEEE International Instrumentation and Measurement Technology Conference. Piscataway, NJ: IEEE, 1–6.
15.
NateghSBogliettiALiuY, et al. (2020) A review on different aspects of traction motor design for railway applications. IEEE Transactions on Industry Applications99: 2148–2157.
16.
NikolaouNGAntoniadisIA (2002) Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted Morlet wavelets. Mechanical Systems and Signal Processing16(4): 677–694.
17.
OkwuosaCNAkpudoUEHurJ (2022) A cost-efficient MCSA-based fault diagnostic framework for SCIM at low-load conditions. Algorithms15(212): 212.
18.
OñateWPerezRCaizaG (2020) Diagnosis of incipient faults in induction motors using mcsa and thermal analysis. Advances in Emerging Trends and Technologies2: 74–84.
19.
Puche-PanaderoRMartinez-RomanJSapena-BanoA, et al. (2019) Diagnosis of rotor asymmetries faults in induction machines using the rectified stator current. IEEE Transactions on Energy Conversion35(1): 213–221.
20.
RandallRBJérmeA (2011) Rolling element bearing diagnostics - a tutorial. Mechanical Systems and Signal Processing25(2): 485–520.
21.
SacerdotiDStrozziMSecchiC (2023) A comparison of signal analysis techniques for the diagnostics of the ims rolling element bearing dataset. Applied Sciences13(10): 2076–3417.
22.
SeeraMLimCPNahavandiS, et al. (2014) Condition monitoring of induction motors: a review and an application of an ensemble of hybrid intelligent models. Expert Systems with Applications41(10): 4891–4903.
23.
SinghSKumarN (2017) Detection of bearing faults in mechanical systems using stator current monitoring. IEEE Transactions on Industrial Informatics13(3): 1341–1349.
24.
TianQHouCLiS (2013) Adaptive resonant demodulation for early fault diagnosis of ball bearing. In: 2013 IEEE China Summit and International Conference on Signal and Information Processing. Piscataway, NJ: IEEE, 34–38.
25.
TrajinBChabertMRegnierJ, et al. (2009) Hilbert versus Concordia transform for three-phase machine stator current time-frequency monitoring. Mechanical Systems and Signal Processing23(8): 2648–2657.
26.
TrajinBRegnierJFaucherJ (2010) Comparison between vibration and stator current analysis for the detection of bearing faults in asynchronous drives. IET Electric Power Applications4(2): 90–100.
27.
WangW (2001) Early detection of gear tooth cracking using the resonance demodulation technique. Mechanical Systems and Signal Processing15(5): 887–903.
28.
WangTChuF (2019) Bearing fault diagnosis under time-varying rotational speed via the fault characteristic order (FCO) index based demodulation and the stepwise resampling in the fault phase angle (FPA) domain. ISA Transactions94: 391–400.
29.
WangZWangJZhangP (2023) Gear Fault diagnosis and evaluation based on MCSA in gear phase-domain. In: 2023 IEEE Energy Conversion Congress and Exposition. Piscataway, NJ: IEEE, 5156–5162.
30.
XuGHouDQiH, et al. (2021) High-speed train wheel set bearing fault diagnosis and prognostics: a new prognostic model based on extendable useful life. Mechanical Systems and Signal Processing146: 107050.
31.
YangYLiuCJiangD, et al. (2020) Nonlinear vibration signatures for localized fault of rolling element bearing in rotor-bearing-casing system. International Journal of Mechanical Sciences173: 105449.
32.
YangYLiuHHanL, et al. (2023) A feature extraction method using VMD and improved envelope spectrum entropy for rolling bearing fault diagnosis. IEEE Sensors Journal23(4): 3848–3858.
33.
ZhaoDZChengJYWenWD, et al. (2016) Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed. Journal of Sound and Vibration378: 109–123.
34.
ZouYZhangYMaoH (2021) Fault diagnosis on the bearing of traction motor in high-speed trains based on deep learning. Alexandria Engineering Journal60(1): 1209–1219.
