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This paper proposes a new diagnosis method based on Adaptive maximum correlated kurtosis deconvolution (AMCKD) for accurate identification of compound faults of rolling bearings. The AMCKD method combines the powerful capability of cuckoo search algorithm for global optimization with the advantage of Maximum correlated kurtosis deconvolution (MCKD) for impact signal extraction. In contrast to traditional methods, such as direct envelop spectrum, Discrete wavelet transform (DWT), and empirical mode decomposition, the proposed method extracts each fault signal related to the single failed part from the compound fault signals and effectively separates the coupled fault features. First, the original signal is processed using AMCKD method. Demodulation operation is then performed on the obtained single fault signal, and the envelope spectrum is calculated to identify the characteristic frequency information. Verification is performed on simulated and experimental signals. Results show that the proposed method is more suitable for detecting compound faults in rolling bearings compared with traditional methods. This research provides a basis for improving the monitoring and diagnosis precision of rolling bearings.

Spectral kurtosis (SK) is an algorithm that gives an indication of how kurtosis varies with frequency. A frequency band that contains abundant information, especially the impact signal, can be tracked by calculating SK. In the present article, SK combined with Autoregressive AR model, was applied into the fault diagnosis and condition monitoring of bearings. Accelerated life test of rolling bearings in Hangzhou Bearing Test & Research Center (HBRC) was performed to collect vibration data over their entire lifetime (normal-fault-failure). The result shows that SK can detect early incipient fault by eliminating some other interfering frequency components. In addition, it can detect fault 5 min earlier than root mean value (RMS). This fault detection in advance is significant for condition monitoring.

Rolling bearings are key components of rotary machines. To ensure early effective fault diagnosis for bearings, a new rolling bearing fault diagnosis method based on variational mode decomposition (VMD) and an improved kernel extreme learning machine (KELM) is proposed in this paper. A fault signal is decomposed via VMD to obtain the intrinsic mode function (IMF) components, and the approximate entropy (ApEn) of the IMF component containing the main fault information is calculated. An eigenvector is created from the approximate entropy of each component. A bearing diagnosis model is created via a KELM; the KELM parameters are optimized using the particle swarm optimization (PSO) algorithm to obtain a KELM diagnosis model with optimal parameters. Finally, the effectiveness of the diagnosis method proposed in this paper is verified via a fan bearing fault diagnosis test. Under identical conditions, the result is compared with the results obtained using a back propagation (BP) neural network, a conventional extreme learning machine (ELM), and a support vector machine (SVM). The test result shows that the method proposed in this paper is superior to the other three methods in terms of diagnostic accuracy.

When low-speed rolling bearings fail, it is hard to diagnose the extent of their damage. We developed a test rig to simulate the lowspeed rolling bearing operating condition, where bearings with various fault states are installed on the test wheelset and subjected to the same external loading condition. The collected bearing box acceleration time histories are processed with the Empirical mode decomposition (EMD) method combined with kurtosis criterion to filter the trend and noise components. Five characteristic parameters of Alpha stable distribution (ASD) are identified by fitting the ASD distribution to the vibration acceleration signals and computing the Probability density function (PDF). To highlight the advantage of ASD method in feature extraction, kurtosis also has be calculated. Through sensitivity and stability analysis of the six parameters and utilization of Least squares support vectors machine (LSSVM) with Particle swarm optimization (PSO), three most sensitive and stable feature parameters including the characteristic exponent α , the scale factor γ and the peak value of the PDF h are located and applied to evaluate the low-speed rolling bearings’ damage position and damage extent. The proposed method was validated by test data, and the results demonstrated that the ASD characteristics combined with PSO-LSSVM can not only achieve fault diagnosis of low-speed rolling bearings' damage position and damage extent, but also have better diagnosis accuracy and operational efficiency than other methods.

The nonlocal means (NL-Means) method that has been widely used in the field of image processing in recent years effectively overcomes the limitations of the neighborhood filter and eliminates the artifact and edge problems caused by the traditional image denoising methods. Although NL-Means is very popular in the field of 2D image signal processing, it has not received enough attention in the field of 1D signal processing. This paper proposes a novel approach that diagnoses the fault of a rolling bearing based on fast NL-Means and the envelop spectrum. The parameters of the rolling bearing signals are optimized in the proposed method, which is the key contribution of this paper. This approach is applied to the fault diagnosis of rolling bearing, and the results have shown the efficiency at detecting roller bearing failures.

Although there are a large number of studies on large rolling bearings, analyses are seldom for the effects of size and raceway hardness on their fatigue life. In this study, taking the four contact-point ball bearings as an example, the fatigue lives of large rolling bearings with different structure sizes were calculated through the stress life ( σ-N ), strain life ( ε-N ) and international standard (ISO) methods, respectively. The maximum contact force and subsurface stress in the raceway were obtained with the finite element method. At the same time, the effect of raceway hardening depth on bearing life was taken into account. Results showed that the effect of raceway hardening depth on the life of large rolling bearing was obvious. Thus, the raceway hardening depth cannot be ignored when calculating the fatigue life of large rolling bearing. When used with the load at the same level, the rolling bearing with larger size usually had less the fatigue life.

As a kind of complicated mechanical component, rolling element bearing plays a significant role in rotating machines, and bearing fault detection benefits decision-making of maintenance and avoids undesired downtime cost. However, extraction of fault signatures from a collected signal in a practical working environment is always a great challenge. This paper proposes an improved combination of the Hilbert and wavelet transforms to identify early bearing fault signatures. Real rail vehicle bearing and motor bearing data were used to validate the proposed method. A traditional combination of Hilbert and wavelet transforms was employed for comparison purpose. An indicator to evaluate fault detection capability of methods was developed in this research. Analysis results showed that the extraction capability of bearing fault signatures is greatly enhanced by the proposed method.

Defective rolling bearing response is often characterized by the presence of periodic impulses. However, the in-situ sampled vibration signal is ordinarily mixed with ambient noises and easy to be interfered even submerged. The hybrid approach combining the second generation wavelet denoising with morphological filter is presented. The raw signal is purified using the second generation wavelet. The difference between the closing and opening operator is employed as the morphology filter to extract the periodicity impulsive features from the purified signal and the defect information is easily to be extracted from the corresponding frequency spectrum. The proposed approach is evaluated by simulations and vibration signals from defective bearings with inner race fault, outer race fault, rolling element fault and compound faults, respectively. Results show that the ambient noises can be fully restrained and the defect information of the above defective bearings is well extracted, which demonstrates that the approach is feasible and effective for the fault detection of rolling bearing.

Foil gas bearings are self-acting hydrodynamic bearings made from sheet metal foils comprised of at least two layers. The innermost \"top foil\" layer traps a gas pressure film that supports a load while a layer or layers underneath provide an elastic foundation. Foil bearings are used in many lightly loaded, high-speed turbomachines such as compressors used for aircraft pressurization and small microturbines. Foil gas bearings provide a means to eliminate the oil system leading to reduced weight and enhanced temperature capability. The general lack of familiarity of the foil bearing design and manufacturing process has hindered their widespread dissemination. This paper reviews the publicly available literature to demonstrate the design, fabrication, and performance testing of both first- and second-generation bump-style foil bearings. It is anticipated that this paper may serve as an effective starting point for new development activities employing foil bearing technology.

This paper is the second in a series that examines grease lubrication mechanisms and failure in rolling element bearings. The aim of the work was to understand the grease condition changes during use and the relationship to lubrication performance and failure. R0F bearing tests were carried out with two lithium hydroxystearate greases and the effects of the temperature, the speed, and the additive package on lubrication life was studied. Post-test, one pair of bearings (fail and non-fail) was dismantled and grease distribution and condition assessed. IR spectroscopy was then used to determine the lubricant composition and the oxidation level of the grease remaining on the shields, the inner raceway, and the cage pockets. The additive package increased the grease life by 100-700% depending on the test condition. Most of the grease remaining in the bearing was found on the shields, with only trace amounts in the cage pockets or close to the rolling track. The IR analysis showed that the composition of the shield sample was similar to the fresh grease although the base oil oxidation was evident and this increased with the running time. The cage pocket and inner raceway films contained a number of chemical species; these included the base oil and the thickener and their oxidation products. The study concludes that after an initial running-in period the \"active\" lubricant is heavily degraded grease, which contains oxidized species from the base oil and the thickener. Different failure mechanisms are identified depending on the test condition. High-speed tests that fail relatively quickly are due to poor boundary lubrication performance or cage failure rather than the lubricant reaching its \"oxidation\" limit. Long-term tests at slower speeds suffer considerable base oil oxidation. Under these conditions, failure is due to a reduction in the amount and/or mobility of the raceway lubricant.