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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.

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.

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.

Seismic isolation is a technique that has been widely used around the world to decouple the superstructure from the ground motions during earthquakes. However, the attention of seismic isolation is mostly focused on the protection of the building structures. Acceleration-sensitive devices or equipment, which are in desperate need of seismic protection, are still not fully emphasized. Meanwhile, the stiffness and frequencies of the conventional rolling- and sliding-type isolation bearings demonstrate an upward trend as the isolation layer displacement increases, which may bring self-centering and resonance issues. Thus, a novel variable-frequency rolling pendulum bearing is developed for the protection of acceleration-sensitive equipment. The rolling-type isolation bearing is selected to enhance the self-centering capacity, and additional viscous dampers are incorporated to improve the system damping. Moreover, the theoretical formulas of several typical variable-frequency rolling pendulum bearings are derived and presented to figure out the dynamic characterization of the device. The isolation efficiency of the proposed device under different parameters is also validated using shake table tests. Test results demonstrate that the newly proposed devices show excellent isolation performance at reducing both acceleration and displacement responses. Finally, the numerical model of this isolation system is proposed in detail. The simulated results, including relative acceleration responses, relative displacement responses and movement locus of the upper plates, are consistent with test results, which demonstrates this simplified model could be used for further studies.

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.

Fault state is central to the achievement of equipment operation stability and security. On the basis of the analysis of the general process, basic characteristics and evolution of rolling bearing fault formation, according to the uncertainty of rolling bearing fault generation mechanism, highly nonlinear of fault evolution and diversity of fault modes, establishing a rolling bearing fault evolution model based on vibration time domain parameters.

The randomness and fuzziness that exist in rolling bearings when faults occur result in uncertainty in acquisition signals and reduce the accuracy of signal feature extraction. To solve this problem, this study proposes a new method in which cloud model characteristic entropy (CMCE) is set as the signal characteristic eigenvalue. This approach can overcome the disadvantages of traditional entropy complexity in parameter selection when solving uncertainty problems. First, the acoustic emission signals under normal and damage rolling bearing states collected from the experiments are decomposed via ensemble empirical mode decomposition. The mutual information method is then used to select the sensitive intrinsic mode functions that can reflect signal characteristics to reconstruct the signal and eliminate noise interference. Subsequently, CMCE is set as the eigenvalue of the reconstructed signal. Finally, through the comparison of experiments between sample entropy, root mean square and CMCE, the results show that CMCE can better represent the characteristic information of the fault signal.

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.