The friction characteristics and performance of wet clutches have been investigated by several authors. Studies have also been made to understand the frictional performance during the service life of the clutch system. However, most lifetime studies have been conducted for systems with paper-based friction material so that systems using sintered bronze friction material remain largely unexplored. To study the friction performance of how these systems can vary over time, the friction characteristics for a clutch system using lubricants aged in three different ways were compared. The effects on friction characteristics resulting from oxidation of the lubricant, reduced additive concentration, and ageing under real operating conditions in a wet clutch test rig were studied.The oxidation effects on friction characteristics were examined using a modified waterless turbine oil oxidation stability test on a fully formulated lubricant. Five oxidation time periods from 48 to 408 h were investigated. For each period of oxidation, a friction performance test was run using a pin-on-disc machine.The ageing carried out in a wet clutch test rig is a standard test of a wet clutch systems manufacturer which is used in order to verify that an oil-friction disc combination will last the full service life of the specific application. This test gives a realistic ageing process similar to that in a wet clutch in a field test.Under boundary-lubricated conditions, additives are vital to the performance of wet clutches. Therefore, the effect of reducing the additive concentration in the oil was also studied, in the range of 10 to 100 per cent of the original additive package used in the fully formulated wet clutch lubricant.Results showed that a general friction increase can be observed for oxidation, additive reduction, and test rig ageing. It was also concluded that different methods of simulating the wet clutch ageing process differ and cannot be directly correlated with each other.

No abstract

Wind turbines are often plagued by premature component failures, with drivetrain bearings being particularly subjected to these failures. To identify failing components, vibration condition monitoring has emerged and grown substantially. The fast Fourier transform (FFT) is the major signal processing method of vibrations. Recently, the wavelet transforms have been used more frequently in bearing vibration research, with one alternative being the discrete wavelet transform (DWT). Here, the low-frequency component of the signal is repeatedly decomposed into approximative and detailed coefficients using a predefined mother wavelet. An extension to this is the wavelet packet transform (WPT), which decomposes the entire frequency domain and stores the wavelet coefficients in packets. How wavelet transforms and FFT compare regarding fault detection in wind turbine drivetrain bearings has been largely overlooked in literature when applied on field data, with non-ideal placement of sensors and uncertain parameters influencing the measurements. This study consists of a comprehensive comparison of the FFT, a three-level DWT, and the WPT when applied on enveloped vibration measurements from two 2.5-MW wind turbine gearbox bearing failures. The frequency content is compared by calculating a robust condition indicator by summation of the harmonics and shaft speed sidebands of the bearing fault frequencies. Results show a higher performance of the WPT when used as a field vibration measurement analysis tool compared with the FFT as it detects one bearing failure earlier and more clearly, leading to a more stable alarm setting and avoidable, costly false alarms. KEYWORDS bearing failure, condition monitoring, discrete wavelet transform, wavelet packet transform, wind turbine gearbox bearings 1 INTRODUCTION Wind power is today the fastest growing renewable energy source in the world, with an installed capacity of 591 GW in 2018 and a predicted growth up to 908 GW in 2023. 1 However, wind turbines designed for a 20-year lifetime still experience premature failures with the root cause not yet fully understood. When compiling failures occurring in all the subsystems within the wind turbine, gearbox failures have been shown to cause the longest downtime and are thereby also associated with the highest cost per failure. 2,3 Out of the two main component types, the bearings experience most failures, around 76% of the time and with the gearbox output and generator shaft bearings being most represented, while the gears fail 17% of the time and other sources 7%. 4The method considered most effective to minimize the costs of these failures in rotating equipment is condition monitoring, where vibrations is the most common method as it can give early warnings on the health of bearings and gears before their degradation threaten the surrounding components. 5 Statistical methods in the time domain as well as frequency domain methods such as the fast Fourier transform (FFT) has throughoutThe peer review history for this article is available at...

Nowadays, hydropower plants are forced to have more frequent power control and the self-lubricated bearings used in the applications are one of the most critical components affected by the continuously changing operating conditions. In this study, microstructure and composition of two commercially available bearing materials (Orkot TXM Marine and Thordon ThorPlas) used in hydropower turbines were studied. In addition, the influence of sliding speed and applied pressure on the friction and wear behavior of the materials was investigated systematically for dry sliding conditions. The bearing materials were characterized using X-ray microtomography, Nuclear Magnetic Resonance (NMR) spectroscopy and Inductively Coupled Plasma-Sector Field Mass Spectrometry (ICP-SFMS) techniques. Friction and wear tests were carried out with a polymer pin sliding against a stainless steel (SS2333) plate with a linear reciprocating motion. Test conditions were: room temperature, 9-28 MPa pressure and 10-40 mm/s sliding speed ranges. Surface analysis of the polymer pins and the wear tracks were performed by optical profilometry, Scanning Electron Microscope (SEM) and Energy Dispersive Spectroscopy (EDS) techniques. Test results show that, for both materials, the coefficient of friction (COF) is decreasing at higher pressures. Surface analysis reveals higher concentrations of solid lubricants in the transfer layers formed at higher pressures, explaining the decrease in COF. Furthermore, the specific wear rate coefficients are increasing at higher sliding speeds, especially at lower pressures. Results of this study demonstrate that, under dry sliding conditions, changes in sliding speed and pressure have a significant influence on the tribological behavior of these bearing materials.

Although the discrete wavelet transform has been used for diagnosing bearing faults for two decades, most work in this field has been done with test rig data. Since field data starts to be made more available, there is a need to shift into application studies. The choice of mother wavelet, ie, the predefined shape used to analyse the signal, has previously been investigated with simulated and test rig data without consensus of optimal choice in literature. Common between these investigations is the use of the wavelet coefficients' Shannon entropy to find which mother wavelet can yield the most useful features for condition monitoring. This study attempts to find the optimal mother wavelet selection using the discrete wavelet transform. Datasets from wind turbine gearbox accelerometers, consisting of enveloped vibration measurements monitoring both healthy and faulty bearings, have been analysed. The bearing fault frequencies' excitation level has been analysed with 130 different mother wavelets, yielding a definitive measure on their performance. Also, the applicability of Shannon entropy as a ranking method of mother wavelets has been investigated. The results show the discrete wavelet transforms ability to identify faults regardless of mother wavelet used, with the excitation level varying no more than 4%. By analysing the Shannon entropy, broad predictions to the excitation level could be drawn within the mother wavelet families but no direct correlation to the main results. Also, the high computational effort of high order Symlet wavelets, without increased performance, makes them unsuitable.