A novel fault diagnosis method for rotating machinery based on S transform and morphological pattern spectrum

Abstract

Abstract:To improve vehicle fuel economy whilst enhancing road handling and ride comfort, power generating suspension systems have recently attracted increased attention in automotive engineering. This paper presents our study of a regenerative hydraulic shock absorber system which converts the oscillatory motion of a vehicle suspension into unidirectional rotary motion of a generator. Firstly a model which takes into account the influences of the dynamics of hydraulic flow, rotational motion and power regeneration is developed. Thereafter the model parameters of fluid bulk modulus, motor efficiencies, viscous friction torque, and voltage and torque constant coefficients are determined based on modelling and experimental studies of a prototype system. The model is then validated under different input excitations and load resistances, obtaining results which show good agreement between prediction and measurement. In particular, the system using piston-rod dimensions of 50-30 mm achieves recoverable power of 260 W with an efficiency of around 40% under sinusoidal excitation of 1 Hz frequency and 25 mm amplitude when the accumulator capacity is set to 0.32 L with the load resistance 20 Ω. It is then shown that the appropriate damping characteristics required from a shock absorber in a heavy-haulage vehicle can be met by using variable load resistances and accumulator capacities in a device akin to the prototype. The validated model paves the way for further system optimisation towards maximising the performance of regeneration, ride comfort and handling.

A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery

“…Georgoulas et al [150] Symbolic Aggregate approximation + KNN Gao et al [151] Stransform + morphological pattern spectrum + KNN Rajeswari et al [152] EEMD + hybrid binary bat + KNN Geramifard et al [153] Hidden Markov model + KNN Holguín-Londoño [154] Filter bank + KNN…”

“…The histogram was taken as the input of KNN for fault location and severity identification of rolling bearing. Gao et al [151] combined S transform with morphological pattern spectrum for feature extraction, and then KNN was used to identify bearing faults. Rajeswari et al [152] used EEMD to extract features.…”

Experimental application of stochastic resonance based on Wood–Saxon potential on fault diagnosis of bearing and planetary gearbox

“…Serious faults even lead to fatal casualties such as the LN-OJF helicopter accident at Norway in June 2016, which are caused by the main planetary gearbox fault. Therefore, mechanical fault diagnosis like fault mechanism and fault feature extraction has caused more and more attention [1][2][3][4][5][6][7][8][9].…”

“…Feng et al [6,7] proposed two timefrequency analysis methods (adaptive optimal kernel timefrequency analysis and iterative generalized synchrosqueezing transform) to diagnose planetary gearbox. Gao et al [8] proposed a novel characterization method based on S transform and morphological pattern spectrum for rotating machinery. Yan et al [9] applied the improved singular spectrum decomposition-based 1.5-dimensional energy spectrum for rotating machinery fault diagnosis.…”