Multi-fault diagnosis for rolling element bearings based on intrinsic mode function screening and optimized least squares support vector machine

Multi-fault diagnosis of rolling element bearing is significant to avoid serious accidents and huge economic losses effectively. However, due to the vibration signal with the character of nonstationarity and nonlinearity, the detection, extraction and classification of the fault feature turn into a challenging task. This paper presents a novel method based on redundant second generation wavelet packet transform (RSGWPT), ensemble empirical mode decomposition (EEMD) and optimized least squares support vector machine (LSSVM) for fault diagnosis of rolling element bearings. Firstly, this method implements an analysis combining RSGWPT-EEMD to extract the crucial characteristics from the measured signal to identify the running state of rolling element bearings, the vibration signal is adaptively decomposed into a number of modified intrinsic mode functions (modified IMFs) by two step screening processes based on the energy ratio; secondly, the matrix is formed by different level modified IMFs and singular value decomposition (SVD) is used to decompose the matrix to obtain singular value as eigenvector; finally, singular values are input to LSSVM optimized by particle swarm optimization (PSO) in the feature space to specify the fault type. The effectiveness of the proposed multi-fault diagnosis technique is demonstrated by applying it to both simulated signals and practical bearing vibration signals under different conditions. The results show that the proposed method is effective for the condition monitoring and fault diagnosis of rolling element bearings

Health condition assessment of ball bearings using TOSELM

The health condition assessment of Electric Multiple Unit (EMU) traction motor ball bearing is one of the key issues of high-speed train running safety. In order to assess health condition of EMU traction motor ball bearing, an online-sequential extreme learning machine algorithm based on TensorFlow (TOSELM) is proposed. Samples data set is divided into normal condition and fault condition using vibration data of ball bearings. This paper uses health condition accuracy rate index to evaluate TOSELM algorithm performance. The proposed approach is verified by public data set and private data set. The experiment results show the proposed method is an effective method for ball bearing health status assessment

Sparse decomposition based on ADMM dictionary learning for fault feature extraction of rolling element bearing

Sparse decomposition is a novel method for the fault diagnosis of rolling element bearing, whether the construction of dictionary model is good or not will directly affect the results of sparse decomposition. In order to effectively extract the fault characteristics of rolling element bearing, a sparse decomposition method based on the over-complete dictionary learning of alternating direction method of multipliers (ADMM) is presented in this paper. In the process of dictionary learning, ADMM is used to update the atoms of the dictionary. Compared with the K-SVD dictionary learning and non-learning dictionary method, the learned ADMM dictionary has a better structure and faster speed in the sparse decomposition. The ADMM dictionary learning method combined with the orthogonal matching pursuit (OMP) is used to implement the sparse decomposition of the vibration signal. The envelope spectrum technique is used to analyze the results of the sparse decomposition for the fault feature extraction of the rolling element bearing. The experimental results show that the ADMM dictionary learning method can updates the dictionary atoms to better fit the original signal data than K-SVD dictionary learning, the high frequency noise in the vibration signal of the rolling bearing can be effectively suppressed, and the fault characteristic frequency can be highlighted, which is very favorable for the fault diagnosis of the rolling element bearing

On-line prediction remaining useful life for ball bearings via grey NARX

The Huge vibration data are generated continuously by many sensors in daily high-speed rotating machinery operations. Accurate online prediction based on big vibration data streaming can reduce the risks related to failures and avoid service disruptions. This paper presents a hybrid nonlinear autoregressive network with exogenous inputs (NARX) model to forecast the remaining useful life of ball bearings through health index based on big vibration data streaming. This approach is validated by a real data from PRONOSTIA experimentation platform and industrial test rig compared with backpropagation neural network (BP), Elman and general regression neural network (GRNN) prediction model. Root mean square error, mean absolute error and correlation coefficient were used as performance indexes to evaluate the prediction accuracy of these models. The mean absolute error, the root mean square error and the correlation coefficient of hybrid NARX model evaluation index are 2.04, 2.85 and 0.98 respectively. It shows that the model presented in this paper has higher prediction accuracy. It can meet the needs of actual ball bearing remaining useful life prediction and also provide reference in other fields

Wavelength dependence of electron localization in the laser-driven dissociation of H2+_2^+

We theoretically investigate the laser wavelength dependence of asymmetric dissociation of H$_2^+$. It is found that the electron localization in molecular dissociation is significantly manipulated by varying the wavelength of the driving field. Through creating a strong nuclear vibration in the laser-molecular interaction, our simulations demonstrate that the few-cycle mid-infrared pulse can effectively localize the electron at one of the dissociating nuclei with weak ionization. Moreover, we show that the observed phase-shift of the dissociation asymmetry is attributed to the different population transfers by the remaining fields after the internuclear distances reach the one-photon coupling point.Comment: 11 pages, 7 figure

Laser-polarization-dependent photoelectron angular distributions from polar molecules

Photoelectron angular distributions (PADs) of oriented polar molecules in response to different polarized lasers are systematically investigated. It is found that the PADs of polar CO molecules show three distinct styles excited by linearly, elliptically and circularly polarized lasers respectively. In the case of elliptical polarization, a deep suppression is observed along the major axis and the distribution concentrates approximately along the minor axis. Additionally, it is also found that the concentrated distributions rotate clockwise as the ellipticity increases. Our investigation presents a method to manipulate the motion and angular distribution of photoelectrons by varying the polarization of the exciting pulses, and also implies the possibility to control the processes in laser-molecule interactions in future work.Comment: 12 pages and 7 figure

A New De-Noising Method Based on Enhanced Time-Frequency Manifold and Kurtosis-Wavelet Dictionary for Rolling Bearing Fault Vibration Signal

The transient pulses caused by local faults of rolling bearings are an important measurement information for fault diagnosis. However, extracting transient pulses from complex nonstationary vibration signals with a large amount of background noise is challenging, especially in the early stage. To improve the anti-noise ability and detect incipient faults, a novel signal de-noising method based on enhanced time-frequency manifold (ETFM) and kurtosis-wavelet dictionary is proposed. First, to mine the high-dimensional features, the C-C method and Cao’s method are combined to determine the embedding dimension and delay time of phase space reconstruction. Second, the input parameters of the liner local tangent space arrangement (LLTSA) algorithm are determined by the grid search method based on Renyi entropy, and the dimension is reduced by manifold learning to obtain the ETFM with the highest time-frequency aggregation. Finally, a kurtosis-wavelet dictionary is constructed for selecting the best atom and eliminating the noise and reconstruct the defective signal. Actual simulations showed that the proposed method is more effective in noise suppression than traditional algorithms and that it can accurately reproduce the amplitude and phase information of the raw signal

Can patients with good tumor regression grading after neoadjuvant chemoradiotherapy be exempted from lateral lymph node dissection?

Abstract Objective To investigate whether lateral lymph node (LLN) dissection (LLND) can be exempted in patients with good tumor regression grading (TRG) after neoadjuvant chemoradiotherapy (nCRT)? Methods A retrospective study was conducted on consecutive patients with advanced rectal cancer who underwent nCRT and total mesorectal resection plus selective LLND at our institution. The primary outcomes are the relationship between LLN metastasis (LLNM) and magnetic resonance imaging TRG (mrTRG) and the relationship between LLNM and pathological TRG (pTRG). Results A total of 91 patients were included, of which 24 patients (26.4%) had LLNM, 67 patients (73.6%) had no LLNM. There were significant differences of the maximum short-axis of LLN before and after nCRT, short-axis reduction rate of the LLN with maximum short-axis, length diameter reduction rate of primary tumor, mrTRG, and pTRG between the two groups. Multivariate logistic regression showed that mrTRG (P = 0.026) and pTRG (P = 0.013) were independent predictors for LLNM. The combination used by mrTRG and the maximum short-axis of LLNs ≥ 8 mm before nCRT and the maximum short-axis of LLN ≥ 5 mm after nCRT achieved specificity of 0.970, positive predictive value (PPV) of 0.867, and negative predictive value (NPV) of 0.855. The same combination used by pTRG achieved the specificity of 0.970, PPV of 0.857 and NPV of 0.844. Conclusion The suspected positive LLNs tend to be sterilized by nCRT in patients who have a very good response to nCRT. It is rational to avoid LLND in patients whose primary tumor and LLNs both show good response to nCRT

Study on the Thermal Performance and Temperature Distribution of Ball Bearings in the Traction Motor of a High-Speed EMU

The transient thermal performance of rolling bearings affects the mechanical performance and system safety of traction motors. Most of the traditional empirical formulas used in temperature analysis have been simplified and cannot be completely applied to the calculation of heat generation and convection heat transfer coefficients. Based on the comparative analysis of finite element transient temperature and experimental data, this paper proposes a correction method of mathematical model and derives an accurate calculation formula for the heat generation and lubricant convection heat transfer coefficient of ball bearings applicable for the non-driving end in the traction motor of a high-speed EMU (Electric Multiple Unit). The accuracy of the results has been verified by durability experiment data. In addition, with changes in speed, radial load and other factors taken into account, we have analyzed the influence of these time-varying factors on ball bearing temperature, as well as the temperature distribution law of each component in a grease-lubricated bearing, in a bid to lay a foundation for follow-up research on the heat transfer laws of traction motors and rolling bearings