A Fast Signal Estimation Method Based on Probability Density Functions for Fault Feature Extraction of Rolling Bearings

Fault diagnosis of rolling bearings is essential to ensure the efficient and safe operation of mechanical equipment. The extraction of fault features of the repetitive transient component from noisy vibration signals is key to bearing fault diagnosis. However, the bearing fault-induced transients are often submerged by strong background noise and interference. To effectively detect such fault-related transient components, this paper proposes a probability- and statistics-based method. The maximum-a-posteriori (MAP) estimator combined with probability density functions (pdfs) of the repetitive transient component, which is modeled by a mixture of two Laplace pdfs and noise, were used to derive the fast estimation model of the transient component. Subsequently, the LapGauss pdf was adopted to model the noisy coefficients. The parameters of the model derived could then be estimated quickly using the iterative expectation–maximization (EM) algorithm. The main contributions of the proposed statistic-based method are that: (1) transients and their wavelet coefficients are modeled as mixed Laplace pdfs; (2) LapGauss pdf is used to model noisy signals and their wavelet coefficients, facilitating the computation of the proposed method; and (3) computational complexity changes linearly with the size of the dataset and thus contributing to the fast estimation, indicated by analysis of the computational performance of the proposed method. The simulation and experimental vibration signals of faulty bearings were applied to test the effectiveness of the proposed method for fast fault feature extraction. Comparisons of computational complexity between the proposed method and other transient extraction methods were also conducted, showing that the computational complexity of the proposed method is proportional to the size of the dataset, leading to a high computational efficiency

Research on the Sparse Representation for Gearbox Compound Fault Features Using Wavelet Bases

The research on gearbox fault diagnosis has been gaining increasing attention in recent years, especially on single fault diagnosis. In engineering practices, there is always more than one fault in the gearbox, which is demonstrated as compound fault. Hence, it is equally important for gearbox compound fault diagnosis. Both bearing and gear faults in the gearbox tend to result in different kinds of transient impulse responses in the captured signal and thus it is necessary to propose a potential approach for compound fault diagnosis. Sparse representation is one of the effective methods for feature extraction from strong background noise. Therefore, sparse representation under wavelet bases for compound fault features extraction is developed in this paper. With the proposed method, the different transient features of both bearing and gear can be separated and extracted. Both the simulated study and the practical application in the gearbox with compound fault verify the effectiveness of the proposed method

Shape matching and object recognition using common base triangle area

Shape matching has always been a key issue in the field of computer vision. To obtain high recognition accuracy with low time complexity and to reduce the influence of contour deformation due to noise in shape matching, a novel shape matching method based on common base triangle area (CBTA) is proposed. First, a CBTA descriptor of each contour point is defined based on the area functions of the triangles formed by its two neighbour points and other contour points. Then, the descriptor is locally smoothed to keep it more compact and robust to noise. Secondly, a match cost matrix is obtained by computing the CBTA descriptors of all the contour points on two shapes. Finally, the similarity between the two shapes is measured on the basis of the match cost matrix by a dynamic programming algorithm. The experimental results on MPEG‐7, Kimia and an articulation shape database indicate that this method is robust to contour deformation, and both the computational efficiency and the retrieval rate are essentially improved

Sparse Representation of Transients Based on Wavelet Basis and Majorization-Minimization Algorithm for Machinery Fault Diagnosis

Vibration signals captured from faulty mechanical components are often associated with transients which are significant for machinery fault diagnosis. However, the existence of strong background noise makes the detection of transients a basis pursuit denoising (BPD) problem, which is hard to be solved in explicit form. With sparse representation theory, this paper proposes a novel method for machinery fault diagnosis by combining the wavelet basis and majorization-minimization (MM) algorithm. This method converts transients hidden in the noisy signal into sparse coefficients; thus the transients can be detected sparsely. Simulated study concerning cyclic transient signals with different signal-to-noise ratio (SNR) shows that the effectiveness of this method. The comparison in the simulated study shows that the proposed method outperforms the method based on split augmented Lagrangian shrinkage algorithm (SALSA) in convergence and detection effect. Application in defective gearbox fault diagnosis shows the fault feature of gearbox can be sparsely and effectively detected. A further comparison between this method and the method based on SALSA shows the superiority of the proposed method in machinery fault diagnosis

Theoretical study of the dielectronic recombination process of Li-like Xe

The dielectronic recombination of Li-like Xe51+ (2s) ions was studied using the flexible atomic code based on the relativistic configuration interaction method. The resonance energies, radiative and autoionization rates, and resonance strengths were calculated systematically for the doubly excited states (2p1/2nlj)J(n = 18−32) and (2p3/2n′lj)J(n′ = 9−27) of Be-like Xe50+ ions. For the higher Rydberg resonance states with n ≥ 33 and n′ ≥ 28, the resonance energies and strengths were obtained by extrapolation based on quantum defect theory. The theoretical rate coefficients, covering the center-of-mass energy range 0–505 eV, are in a better agreement with the experimental results measured at the heavy-ion storage ring ESR than the Multi-Configuration Dirac-Fock calculations, especially at the resonance energy range close to the series limits

Chilling Accumulation Is Not an Effective Predictor of Vegetation Green-Up Date in Inner Mongolian Grasslands

Chilling accumulation (CA) might be reduced under a warming climate, which raises a critical question: is vegetation green-up date (VGD) altered by varing CA? If the answer is yes, existing thermal-time models should be modified to include a chilling component. By collating observations from eight long-term (1982-2019) field experiments across Inner Mongolia, we quantitatively assessed the responses of VGD of 27 grass species to CA in this region. The results indicated that effect of CA on VGD is in general negligible for most grass species. As excepted, rather than CA, VGD was predominantly determined by other climatic attributes such as heat requirement (HR) and precipitation. These results demonstrate the robustness of existing thermal-time models without a chilling component to predict variations in VGD of major plant species in Inner Mongolian temperate grasslands. Plain Language Summary Low temperature in winter (i.e., chilling) is recognized as an important regulator on plant spring phenology in the following year. Growing studies have highlighted the need to include a chilling factor in thermal-time phenology models. To date, the effect of chilling on vegetation green-up date (VGD) has seldom been explored in temperate grasslands. In this study, we collated a comprehensive data set of field observations of VGD from eight long-term experimental sites in Inner Mongolia, China, to address this issue. It was found that chilling accumulation (CA) has very limited impact on dynamics of VGD, particularly for individual grass species. Introducing CA into models cannot significantly improve the performances of models treating heat requirement (HR) and precipitation as predictor variables in predicting VGD dynamics. Consequently, there is no need to incorporate a chilling component into existing VGD phenology models in the study region

Preseason heat requirement and days of precipitation jointly regulate plant phenological variations in Inner Mongolian grassland

Under global climate change, particularly warming, plant phenology may vary significantly thereby influencing a series of ecosystem functionalities. However, observational evidences of the variation of plant phenology and its association with climate change in temperate grassland are limited. In this study, we collated plant phenological records during the period from 1982 to 2019 at 26 sites in Inner Mongolian temperate grassland to elucidate the association of plant phenology with a series of environmental variables. The results showed that a trend of warming, particularly during May-September, occurred over the study period. However, this warming did not significantly influence plant phenology (e.g., green-up, flowering and brown-down) of four dominant plant species (i.e., Stipa, L. Chinensis, A. Cristatum and A. Frigida). Rather, multivariate regression considering a series of climatic and edaphic factors revealed that preseason climate predominantly regulates the dynamics of plant phenology. Specifically, heat requirement (HR) and days of precipitation (DOP) during the preseason were the two most influential controls on plant phenology. Our findings highlight the importance of incorporating precipitation as an additional predictor variable in current temperature-based phenology models for application in temperate grassland

Optimization of grounding resistance in multitrain DC subway system based on MOEA/D‐DE

Abstract Currently, in multitrain DC subway system, abnormal increase of rail potential (RP) and stray current (SC) has seriously threatened the safe operation of the system. Over voltage protection device (OVPD) is generally chosen to control the RP, but its action process may increase the amplitude of SC seriously. Here, the grounding resistance of OVPD is optimized by a proposed multi‐objective decomposition algorithm based on differential evolution (MOEA/D‐DE) to suppress the rise of RP and SC synergistically. Firstly, the simulation model of the DC subway system with multitrain is built, the power flow calculation is conducted, and the dynamic RP and leakage current (LC) at the location of traction substation are obtained. Secondly, the double objective optimization model of maximum RP and LC is established and solved by MOEA/D‐DE. Finally, the effectiveness of the proposed method is verified based on the data of Guangzhou Metro Line 2. Results show that the SC of the system can be controlled effectively with OVPD grounding mode after optimization, and integrated control of RP and SC can be accomplished