Vibration and Stress Response of High-Speed Train Gearboxes under Different Excitations

The vibration data of the gearbox on a high-speed train was measured, and the vibration characteristics were analyzed in this paper. The dynamic stress of the gearbox under the internal and external excitation was examined by a railway vehicle dynamic model with a flexible gearbox and a flexible wheelset. The ideal 20th polygonal wear was considered, and dynamic stresses of the gearbox under different polygonal wear amplitudes were calculated. The gear transmission model was established to study the dynamic stress of the gearbox under the influence of the time-varying stiffness of the gear meshing. Based on the rigid–flexible coupling model, and considering the influence of wheel polygonization, gear meshing time-varying stiffness, and wheelset elastic deformation, the dynamic stress of the gearbox was investigated with consideration of the measured polygonal wear and measured rail excitation. The results show that the dynamic stress of the gearbox is dominated by the wheel polygonization. Moreover, not only the wheel polygonization excites the resonance of the gearbox, but also the flexible deformation of the wheelset leads to the deformation of the gearbox, which also increases the dynamic stress of the gearbox. Within the resonant bandwidth of the frequency, the amplitude of the dynamic stresses in the gearbox will increase considerably compared with the normal case.

A Characterization Method for Pavement Structural Condition Assessment based on the Distribution Parameter of the Vehicle Vibration Signal

A pavement structural survey plays a vital role in road maintenance and management. This study was intended to explore the feasibility of a non-stop pavement structure assessment method by analyzing the vibration data from a vehicle sensor. In this study, three falling weight deflectometer (FWD) tests and four vehicle vibration tests were conducted on five pavement structures. The FWD test results show that the continuously reinforced composite pavement has a higher structural stiffness than the semi-rigid base asphalt pavement. According to the statistical distribution of vehicle acceleration, a distribution parameter, the peak probability density (PPD), was proposed. The correlation coefficient (−0.722) of the center deflection (D1) and PPD indicates a strong correlation between the two variables. Therefore, PPD is strongly correlated with pavement structural stiffness. This study proposed a novel characterization method for pavement structural conditions based on the distribution parameter of the vehicle vibration signal.

A Predict Method of Water Pump Operating State Based on Improved Particle Swarm Optimization of Support Vector Machine

In order to improve prediction accuracy of water pump operating state, a chaotic prediction model of the pump vibration data based on improved particle swarm optimization of support vector machine is proposed in this paper. Firstly, a grouping optimization strategy particle swarm algorithm based on cosine function is proposed. Then, the training set is obtained on the time series of vibration data by phase space reconstruction. Secondly, The improved particle algorithm is used to optimize the penalty parameters, insensitive loss coefficient and width parameters of support vector machine. Then, a prediction model of vibration data is established by using support vector machine combined with training set and optimal parameters. Finally, the operating state of the pump is predicted according to pump vibration measurement and evaluation method. Compared with the method of linear decreasing weight strategy, the method proposed in this paper is more accurately.

Research on intelligent diagnostic techniques for rolling bearings based on unbalanced data sets

In this paper, based on the combination of comprehensive sampling and one-dimensional convolutional neural network, a bearing fault intelligent diagnosis technique is proposed for the classification of rolling bearing vibration data. At first, the fault data set is expanded by ADASYN method. Then, the data is cleaned up by Tomek link under sampling technique, the risk of overfitting caused by overlap of different classes is reduced and the data of different categories is more apparent, and finally the normal data set and fault data set after comprehensive sampling are classified by one-dimensional convolutional neural network algorithm. Compared with random forests and support vector machines, the results show that the method has a high accuracy in identifying classifications and can effectively solve the classification problem of unbalanced bearing data.

Reliable Fault Diagnosis of Bearings Using an Optimized Stacked Variational Denoising Auto-Encoder

Variational auto-encoders (VAE) have recently been successfully applied in the intelligent fault diagnosis of rolling bearings due to its self-learning ability and robustness. However, the hyper-parameters of VAEs depend, to a significant extent, on artificial settings, which is regarded as a common and key problem in existing deep learning models. Additionally, its anti-noise capability may face a decline when VAE is used to analyze bearing vibration data under loud environmental noise. Therefore, in order to improve the anti-noise performance of the VAE model and adaptively select its parameters, this paper proposes an optimized stacked variational denoising autoencoder (OSVDAE) for the reliable fault diagnosis of bearings. Within the proposed method, a robust network, named variational denoising auto-encoder (VDAE), is, first, designed by integrating VAE and a denoising auto-encoder (DAE). Subsequently, a stacked variational denoising auto-encoder (SVDAE) architecture is constructed to extract the robust and discriminative latent fault features via stacking VDAE networks layer on layer, wherein the important parameters of the SVDAE model are automatically determined by employing a novel meta-heuristic intelligent optimizer known as the seagull optimization algorithm (SOA). Finally, the extracted latent features are imported into a softmax classifier to obtain the results of fault recognition in rolling bearings. Experiments are conducted to validate the effectiveness of the proposed method. The results of analysis indicate that the proposed method not only can achieve a high identification accuracy for different bearing health conditions, but also outperforms some representative deep learning methods.

Detection of Building Response Changes Using Deconvolution Interferometry: A Case Study in Bogota, Colombia

Seismic structural health monitoring allows for the continuous evaluation of engineering structures by monitoring changes in the structural response that can potentially localize associated damage that has occurred. For the first time in Colombia, a permanent and continuous monitoring network has been deployed in a 14-story ecofriendly steel-frame building combined with a reinforced concrete structure in downtown Bogota. The six three-component ETNA-2 accelerometers recorded continuously for 225 days between July 2019 and February 2020. We use deconvolution-based seismic interferometry to calculate the impulse response function (IRF) using earthquake and ambient-vibration data and a stretching technique to estimate velocity variations before and after the Ml 6.0 Mesetas earthquake and its aftershock sequence. A consistent and probably permanent velocity variation (2% reduction) is detected for the building using ambient-vibration data. In contrast, a 10% velocity reduction is observed just after the mainshock using earthquake-based IRFs showing a quick recovery to about 2%. A combination of both earthquake-based and ambient-vibration-based deconvolution interferometry provides a more complete picture of the state of health of engineering structures.

INVESTIGATION OF EFFECT OF USING NANO COATING ON WOODEN SHEDS ON DYNAMIC PARAMETERS

In this article, the dynamic parameters (frequencies, mode shapes, damping ratios) of the uncoated wooden shed and the coated by silicon dioxide are compared using the operational modal analysis method. Ambient excitation was provided from micro tremor ambient vibration data on ground level. Enhanced frequency domain decomposition (EFDD) was used for output. Very best correlation was found between mode shapes. Nano-SiO2 gel applied to the entire outer surface of the red oak shed has an average of 14.54% difference in frequency values and 13.53% in damping ratios, proving that nanomaterials can be used to increase internal rigidity in wooden slabs. High adherence of silicon dioxide to wooden surfaces was observed as another important result of this study.

Repetitive Transient Extraction Using the Optimized SES Entropy Wavelet for Fault Diagnosis of Rotating Machinery

Repetitive transients are usually generated in the monitoring data when a fault occurs on the machinery. As a result, many methods such as kurtogram and optimized Morlet wavelet and kurtosis method are proposed to extract the repetitive transients for fault diagnosis. However, one shortcoming of these methods is that they are constructed based on the index of kurtosis and are sensitive to the impulsive noise, leading to failure in accurately diagnosing the fault of the machinery operating under harsh environment. To address this issue, an optimized SES entropy wavelet method is proposed. In the proposed method, the optimized parameters including bandwidth and central frequency of Morlet wavelets are selected. Then, based on the wavelet coefficients decomposed using the optimized Morlet wavelet, the SES entropy is calculated to select the scales of wavelet coefficients. Finally, the repetitive transients are reconstructed based on the denoising wavelet coefficients of the selected scales. One simulation case and vibration data collected from the experimental setup are used to verify the effectiveness of the proposed method. The simulated and experimental analyses showed that the signal-to-noise ratio (SNR) of the proposed method has the largest value. Specifically, the SNR in the experimental analysis of the proposed method is 0.6, while that of the other three methods is 0.043, 0.0065, and 0.0045, respectively. Therefore, the result shows that the proposed method is superior to the traditional methods for repetitive transient extraction from the vibration data suffered from impulsive noise.

Numerical and Experimental Evaluation of Structural Changes Using Sparse Auto-Encoders and SVM Applied to Dynamic Responses

The present work evaluates the deep learning algorithm called Sparse Auto-Encoder (SAE) when applied to the characterization of structural anomalies. This study explores the SAE’s performance in a supervised damage detection approach to consolidate its application in the Structural Health Monitoring (SHM) field, especially when dealing with real-case structures. The main idea is to use the SAE to extract relevant features from the monitored signals and the well-known Support Vector Machine (SVM) to classify such characteristics within the context of an SHM problem. Vibration data from a numerical beam model and a highway viaduct in Brazil are considered to assess the proposed approach. In both analyzed examples, the efficiency of the implemented methodology achieved more than 99% of correct damage structural classifications, supporting the conclusion that SAE can extract relevant characteristics from dynamic signals that are useful for SHM applications.

Use of the Correlation Coefficient for Rotating Machine Monitoring

The use of electronics and computer technology in production systems has greatly improved the quality of our industrial products. The productivity of these installations is a function of the maintenance quality applied to the equipment. Several methods are used to monitor the functioning of industrial installations. One of these methods is vibration analysis. The vibration signals from the rotating machines support several types of information related to the working state of the production tool. The processing of this information makes it possible to have decision tools for maintenance. In this work, we propose a method of anticipating the maintenance of rotating machines. The algorithm we propose starts with the removal of 512 point windows during the running time of the ball bearing. Each signal is decomposed by DWT: we obtain the approximation coefficients. These coefficients make it possible to determine the correlation coefficient between the so-called reference window and the other windows following the functioning of the ball bearing. The correlation coefficient is then the fundamental element of the decision. This algorithm has been applied to real vibration data and the results are encouraging.