Showing papers in "Shock and Vibration in 2021"

Vibration Analysis for Machine Monitoring and Diagnosis: A Systematic Review

Abstract: Untimely machinery breakdown will incur significant losses, especially to the manufacturing company as it affects the production rates. During operation, machines generate vibrations and there are unwanted vibrations that will disrupt the machine system, which results in faults such as imbalance, wear, and misalignment. Thus, vibration analysis has become an effective method to monitor the health and performance of the machine. The vibration signatures of the machines contain important information regarding the machine condition such as the source of failure and its severity. Operators are also provided with an early warning for scheduled maintenance. Numerous approaches for analyzing the vibration data of machinery have been proposed over the years, and each approach has its characteristics, advantages, and disadvantages. This manuscript presents a systematic review of up-to-date vibration analysis for machine monitoring and diagnosis. It involves data acquisition (instrument applied such as analyzer and sensors), feature extraction, and fault recognition techniques using artificial intelligence (AI). Several research questions (RQs) are aimed to be answered in this manuscript. A combination of time domain statistical features and deep learning approaches is expected to be widely applied in the future, where fault features can be automatically extracted from the raw vibration signals. The presence of various sensors and communication devices in the emerging smart machines will present a new and huge challenge in vibration monitoring and diagnosing.

Optimal Design of Diversion Piers of Lateral Intake Pumping Station Based on Orthogonal Test

Abstract: To explore the rectification parameters of the diversion piers optimized for the forebay of the pump station with a lateral angle of 45°, the orthogonal experiment and computational fluid dynamics methods are used to analyze the flow characteristics of the diversion piers under different parameter combinations The flow pattern in the forebay of the side water inlet is improved The rectification effect of the diversion piers under 16 schemes is analyzed, considering the length, width, radian, and relative height of the diversion piers Combined with numerical simulation, a better rectification scheme is provided, and finally, a reasonable range of values for the rectification parameters of the forebay diversion pier of the side 45° bend angle pump station is obtained

Multipoint Optimal Minimum Entropy Deconvolution Adjusted for Automatic Fault Diagnosis of Hoist Bearing

Abstract: Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a powerful method that can extract the periodic characteristics of signal effectively, but this method needs to evaluate the fault cycle a priori, and moreover, the results obtained in a complex environment are easily affected by noise. These drawbacks reduce the application of MOMEDA in engineering practice greatly. In order to avoid such problems, in this paper, we propose an adaptive fault diagnosis method composed of two parts: fault information integration and extracted feature evaluation. In the first part, a Teager energy spectrum amplitude factor (T-SAF) is proposed to select the intrinsic mode function (IMF) components decomposed by ensemble empirical mode decomposition (EEMD), and a combined mode function (CMF) is proposed to further reduce the mode mixing. In the second part, the particle swarm optimization (PSO) taking fractal dimension as the objective function is employed to choose the filter length of MOMEDA, and then the feature frequency is extracted by MOMEDA from the reconstructed signal. A cyclic recognition method is proposed to appraise the extracted feature frequency, and the evaluation system based on threshold and weight coefficient removes the wrong feature frequency. Finally, the feasibility of the method is verified by simulation data, experimental signals, and on-site signals. The results show that the proposed method can effectively identify the bearing state.

Early Fault Detection Method of Rolling Bearing Based on MCNN and GRU Network with an Attention Mechanism

Abstract: Aiming at the problem of early fault diagnosis of rolling bearing, an early fault detection method of rolling bearing based on a multiscale convolutional neural network and gated recurrent unit network with attention mechanism (MCNN-AGRU) is proposed. This method first inputs multiple time scales rolling bearing vibration signals into the convolutional neural network to train the model through multiscale data processing and then adds the gated recurrent unit network with an attention mechanism to make the model predictive. Finally, the reconstruction error between the actual value and the predicted value is used to detect the early fault. The training data of this method is only normal data. The early fault detection in the operating condition monitoring and performance degradation assessment of the rolling bearing is effectively solved. It uses a multiscale data processing method to make the features extracted by CNN more robust and uses a GRU network with an attention mechanism to make the predictive ability of this method not affected by the length of the data. Experimental results show that the MCNN-AGRU rolling bearing early fault diagnosis method proposed in this paper can effectively detect the early fault of the rolling bearing and can effectively identify the type of rolling bearing fault.

A New Joint Denoising Algorithm for High-G Calibration of MEMS Accelerometer Based on VMD-PE-Wavelet Threshold

Abstract: Recently, the High-G MEMS accelerometer (HGMA) has been used in navigation, mechanical property detection, consumer electronics, and other fields widely. As the core component of a measuring system, it is very crucial to enhance the calibration accuracy of the accelerometer. In order to remove the noises in the accelerometer output signals to enhance its calibration accuracy, a combined denoising method which combines variational mode decomposition (VMD) with permutation entropy (PE) and wavelet threshold is given in this article. For the sake of overcoming the defect of signal distortion caused by the traditional denoising methods, this joint denoising method combines the good decomposition characteristics of VMD and the good denoising ability of wavelet threshold and introduces PE as a judgment criterion to achieve a good balance between denoising effect and signal fidelity. The combination of PE and VMD not only avoids the phenomenon of mode aliasing but also improves the ability to identify the noise components, which makes the wavelet threshold denoising more specific. Firstly, some intrinsic mode functions (IMFs) are obtained by using VMD to decompose the complex signal containing noise which is outputted from the accelerometer. Secondly, the IMF components can be divided into noise IMF components, mixed IMF components, and useful IMF components by PE algorithm. Thirdly, the noise IMF components can be discarded directly, and then the mixed IMF components can be denoised by wavelet threshold to obtain the noiseless IMF components; in addition, the useful IMF components need to be retained. Finally, the final denoising signal can be obtained by reconstructing the IMF components which have been denoised by the wavelet threshold and the useful IMF components retained before denoising. The experimental results prove that the combined denoising algorithm combines the merits of VMD, PE, and wavelet threshold, and this new algorithm has a good performance in the calibration denoising of accelerometer. Compared with the serious signal distortion caused by using only EMD or wavelet threshold, this method not only has a good denoising effect (the noises in the static part are eliminated by 99.97% and the SNR of the dynamic part is raised to 18.56) but also can maintain a good signal fidelity (the error of shock peak amplitude is 3.4%, the error of vibration peak amplitude is 0.4%, and the correlation coefficient between the denoising signals and dynamic part is as high as 0.982).

Deep Transfer Learning Method Based on 1D-CNN for Bearing Fault Diagnosis

Abstract: In mechanical fault diagnosis, it is impossible to collect massive labeled samples with the same distribution in real industry. Transfer learning, a promising method, is usually used to address the critical problem. However, as the number of samples increases, the interdomain distribution discrepancy measurement of the existing method has a higher computational complexity, which may make the generalization ability of the method worse. To solve the problem, we propose a deep transfer learning method based on 1D-CNN for rolling bearing fault diagnosis. First, 1-dimension convolutional neural network (1D-CNN), as the basic framework, is used to extract features from vibration signal. The CORrelation ALignment (CORAL) is employed to minimize marginal distribution discrepancy between the source domain and target domain. Then, the cross-entropy loss function and Adam optimizer are used to minimize the classification errors and the second-order statistics of feature distance between the source domain and target domain, respectively. Finally, based on the bearing datasets of Case Western Reserve University and Jiangnan University, seven transfer fault diagnosis comparison experiments are carried out. The results show that our method has better performance.

Vibration Images-Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise

Abstract: Deep learning-based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important. In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network- (CNN-) and transfer learning- (TL-) based fault diagnosis method is proposed. Firstly, four kinds of vibration image generation method with different characteristics are put forward, and the corresponding pure vibration image samples are obtained according to the original data. Secondly, using CNN as the adaptive feature extraction and recognition model, the influences of main sensitive parameters of CNN on the network recognition effect are studied, such as learning rate, optimizer, and L1 regularization, and the best model is determined. In order to obtain the pretraining parameters, the training and fault classification test for different image samples are carried out, respectively. Thirdly, the Gaussian white noise with different levels is added to the original signals, and four kinds of noised vibration image samples are obtained. The previous pretrained model parameters are shared for the TL. Each kind of sample research compares the impact of thirteen data sharing schemes on the TL accuracy and efficiency, and finally, the test accuracy and time index are introduced to evaluate the model. The results show that, among the four kinds of image generation method, the classification performance of data obtained by empirical mode decomposition-pseudo-Wigner–Ville distribution (EP) is the best; when the signal to noise ratio (SNR) is 10 dB, the model test accuracy obtained by TL is 96.67% and the training time is 170.46 s.

Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks

Abstract: Remaining useful life (RUL) prediction is necessary for guaranteeing machinery’s safe operation. Among deep learning architectures, convolutional neural network (CNN) has shown achievements in RUL prediction because of its strong ability in representation learning. Features from different receptive fields extracted by different sizes of convolution kernels can provide complete information for prognosis. The single size convolution kernel in traditional CNN is difficult to learn comprehensive information from complex signals. Besides, the ability to learn local and global features synchronously is limited to conventional CNN. Thus, a multiscale convolutional neural network (MS-CNN) is introduced to overcome these aforementioned problems. Convolution filters with different dilation rates are integrated to form a dilated convolution block, which can learn features in different receptive fields. Then, several stacked integrated dilated convolution blocks in different depths are concatenated to extract local and global features. The effectiveness of the proposed method is verified by a bearing dataset prepared from the PRONOSTIA platform. The results turn out that the proposed MS-CNN has higher prediction accuracy than many other deep learning-based RUL methods.

Application of Histogram Equalization for Image Enhancement in Corrosion Areas

Abstract: In this paper, an image enhancement algorithm is presented for identification of corrosion areas and dealing with low contrast present in shadow areas of an image. This algorithm uses histogram equalization processing under the hue-saturation-intensity model. First of all, an etched image is transformed from red-green-blue color space to hue-saturation-intensity color space, and only the luminance component is enhanced. Then, part of the enhanced image is combined with the original tone component, followed by saturation and conversion to red-green-blue color space to obtain the enhanced corrosion image. Experimental results show that the proposed method significantly improves overall brightness, increases contrast details in shadow areas, and strengthens identification of corrosion areas in the image.

A Review of Model Order Reduction Methods for Large-Scale Structure Systems

Abstract: The large-scale structure systems in engineering are complex, high dimensional, and variety of physical mechanism couplings; it will be difficult to analyze the dynamic behaviors of complex systems quickly and optimize system parameters. Model order reduction (MOR) is an efficient way to address those problems and widely applied in the engineering areas. This paper focuses on the model order reduction of high-dimensional complex systems and reviews basic theories, well-posedness, and limitations of common methods of the model order reduction using the following methods: center manifold, Lyapunov–Schmidt (L-S), Galerkin, modal synthesis, and proper orthogonal decomposition (POD) methods. The POD is a powerful and effective model order reduction method, which aims at obtaining the most important components of a high-dimensional complex system by using a few proper orthogonal modes, and it is widely studied and applied by a large number of researchers in the past few decades. In this paper, the POD method is introduced in detail and the main characteristics and the existing problems of this method are also discussed. POD is classified into two categories in terms of the sampling and the parameter robustness, and the research progresses in the recent years are presented to the domestic researchers for the study and application. Finally, the outlooks of model order reduction of high-dimensional complex systems are provided for future work.

Mechanism of Hard-Roof Rock Burst Control by the Deep-Hole Blasting: Numerical Study Based on Particle Flow

Abstract: In underground coal mines, the deep-hole blasting (DHB) technology is generally adopted for thick hard-roof control. This technology uses the energy released by explosives to weaken the energy storage capacity of hard roof so as to prevent hard-roof rock burst disasters. In this paper, a numerical simulation model of roof DHB was established based on particle flow and the damage range of single-hole blasting with concentrated cylindrical charge was studied. The temporal and spatial evolutions of overlying strata, the distribution of the force chain structure, and the working resistance of hydraulic pressure in the mining process before and after the application of DHB were contrastively analyzed. The following beneficial conclusions were drawn. The blasting-induced single-hole damage range is generally characterized by annular zoning. After the application of DHB, overall the collapse morphology of the key strata in the mining process changes from long-distance instantaneous slipping instability to stratified short-arm stepped synergistic subsidence. The density and strength of force chains in the overburden are notably reduced; the peak value of compressive force chain strength in the key strata in the mining process falls by 17.85% as a result of DHB. The monitoring results of the working resistance of hydraulic support reveal that the DHB technology can effectively shorten the step distance of periodic weighting and reduce the variation amplitude of overburden load during weighting. In summary, the mechanism of hard-roof rock burst control by DHB is reflected by both static load reduction and dynamic load reaction.

Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder

Abstract: Structural health monitoring (SHM) is a hot research topic with the main purpose of damage detection in a structure and assessing its health state. The major focus of SHM studies in recent years has been on developing vibration-based damage detection algorithms and using machine learning, especially deep learning-based approaches. Most of the deep learning-based methods proposed for damage detection in civil structures are based on supervised algorithms that require data from the healthy state and different damaged states of the structure in the training phase. As it is not usually possible to collect data from damaged states of a large civil structure, using such algorithms for these structures may be impractical. This paper proposes a new unsupervised deep learning-based method for structural damage detection based on convolutional autoencoders (CAEs). The main objective of the proposed method is to identify and quantify structural damage using a CAE network that employs raw vibration signals from the structure and is trained by the signals solely acquired from the healthy state of the structure. The CAE is chosen to take advantage of high feature extraction capability of convolution layers and at the same time use the advantages of an autoencoder as an unsupervised algorithm that does not need data from damaged states in the training phase. Applications on the two numerical models of IASC-ASCE benchmark structure and a grid structure located at the University of Central Florida, as well as the full-scale Tianjin Yonghe Bridge, prove the efficiency of the proposed algorithm in assessing the global health state of the structures and quantifying the damage.

Impact of Particle Sizes on Flow Characteristics of Slurry Pump for Deep-Sea Mining

Abstract: As the core device of the deep-sea mining transport system, the slurry pump and its internal solid-liquid two-phase flow are extremely complicated; especially, the migration characteristics of particles have a great influence on the flow and wear of the pump. In order to grasp the particle motion law inside the slurry pump, this paper took into consideration the collision effects of the particles with particles and particles with walls and calculated the unsteady flow of the solid-liquid two-phase by CFD-DEM coupling algorithm. Then, the focus was put on the spatial distribution and movement characteristics of different particle diameters (namely, 5 mm, 10 mm, and 15 mm, while volume concentration Cv is constant 5%). The results show that the stratification phenomenon gradually disappears with the increase of particle diameter, and the intensity and scale of the vortex in the guide vane also increase obviously. Besides, as the particle diameter increases, the velocity changes more drastically, and the intensity and scale of the vortex increase significantly. Under low concentration conditions, the presence of particles has a limited influence on the hydraulic performance of the pump. By comparing with the experimental results, the simulation results are in good agreement with it, which proves that the CFD-DEM simulation in this paper is effective, and the conclusions can provide theoretical support for the design and analysis of the slurry pump in engineering application.

Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning

Abstract: In actual working conditions, the initial faults of rolling bearings are difficult to effectively predict due to the lack of evolution knowledge, weak fault information, and strong noise interference. In this paper, a rolling bearing initial fault prediction model that is based on transfer learning and the DCAE-TCN is presented. Firstly, a deep autoencoder (DAE as the first two hidden layers and CAE as the last hidden layer) is used to extract fault features from the rolling bearing vibration signal data. Then, the balanced distributed adaptation (BDA) is used to minimise the distribution difference and class spacing between extracted fault features, and a common feature set is constructed. The temporal features of the original vibration signal in the target domain are extracted using the advantages of the TCN. The experiments are conducted on the publicly available XJTU-SY dataset. The experimental results show that the proposed method can effectively learn the transferable features and compensate the differences between the source and target domains and has a promising application with higher accuracy and robustness for the prediction of early failures of rolling bearings.

Research on Adaptive Friction Compensation of Digital Hydraulic Cylinder Based on LuGre Friction Model

Abstract: This paper aims to eliminate nonlinear friction from the performance of the digital hydraulic cylinder to enable it to have good adaptive ability. First, a mathematical model of a digital hydraulic cylinder based on the LuGre friction model was established, and then a dual-observer structure was designed to estimate the unobservable state variables in the friction model. The Lyapunov method is used to prove the global asymptotic stability of the closed-loop system using the adaptive friction compensation method. Finally, Simulink is used to simulate the system performance. The simulation results indicate that the addition of adaptive friction compensation control can effectively reduce system static error, suppress system limit loop oscillation, “position decapitation,” “speed dead zone,” and low-speed creep phenomena, and improve the overall performance of the digital hydraulic cylinder. The control method has practical application value for improving the performance index of the digital hydraulic cylinder.

Effect of Parameters on the Design of a Suspension System with Four Oblique Springs

Abstract: This paper presents the fundamental static and dynamic characteristics of a suspension system consisting of four linear springs arranged in an X-shaped configuration to achieve geometric nonlinearity. The particular interest is towards the design of a softening spring geometry realizing a quasi-zero stiffness behaviour at large deflections, and the influence of the system parameters is investigated. The static performance is studied in terms of the force-deflection curve and the dynamic performance in terms of the frequency response curve. The softening-hardening behaviour of the suspension leads to a frequency response which bends to the lower frequencies reaching a well-defined minimum. It is found that both the static and dynamic behaviours may be described in terms of a single parameter, and a simple closed-form expression is determined which links the damping in the system to the excitation amplitude to achieve the lowest possible resonance frequency.

Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables

Abstract: Remaining useful life (RUL) estimation is one of the main objectives of prognostics and health management (PHM) frameworks. For the past decade, researchers have explored the application of deep learning (DL) regression algorithms to predict the system’s health state behavior based on sensor readings from the monitoring system. Although the state-of-art results have been achieved in benchmark problems, most DL-PHM algorithms are treated as black-box functions, giving little-to-no control over data interpretation. This becomes an issue when the models unknowingly break the governing laws of physics when no constraints are imposed. The latest research efforts have focused on applying complex DL models to achieve low prediction errors rather than studying how they interpret the data’s behavior and the system itself. This paper proposes an open-box approach using a deep neural network framework to explore the physics of a complex system’s degradation through partial differential equations (PDEs). This proposed framework is an attempt to bridge the gap between statistic-based PHM and physics-based PHM. The framework has three stages, and it aims to discover the health state of the system through a latent variable while still providing a RUL estimation. Results show that the latent variable can capture the failure modes of the system. A latent space representation can also be used as a health state estimator through a random forest classifier with up to a 90% performance on new unseen data.

Detecting and Evaluating Defects in Beams by Correlation Coefficients

Abstract: This research proposes a correlation coefficient for detecting and evaluating defects in beams, which brings about a positive outcome in terms of accuracy and efficiency. This parameter surpasses other parameters, such as natural frequency and damping coefficient, thanks to its sensitivity to structural changes. Our results show that although the damping coefficient had more variation than the natural frequency value in the same experiment, its changes were insufficient and unstable at different levels of defects. In addition, the proposed correlation coefficient parameter has a linear characteristic and always changes significantly according to increasing levels of defects. The results outweigh damping coefficient and natural frequency values. Furthermore, this value is always sensitive to measurement channels, which could be an important factor in locating defects in beams. The testing index is statistically evaluated by a normal distribution of the amplitude value of vibration measurement signals. Changes and shifts in this distribution are the basis for evaluating beam defects. Thus, the suggested parameter is a reliable alternative for assessing the defects of a structure.

Improving the Comfort of the Vehicle Based on Using the Active Suspension System Controlled by the Double-Integrated Controller

Abstract: When the vehicle moves on the road, many external factors affect the vehicle. These effects can cause oscillation and instability for the vehicle. The oscillation of the vehicle directly affects the safety and comfort of passengers. The suspension system is used to control and extinguish these oscillations. However, the conventional passive suspension system is unable to fully meet the vehicle’s requirements for stability and comfort. To improve these problems, these are much modern suspension system models that have been used in the vehicle to replace the passive suspension system. The modern suspension systems are used as the air suspension system, semiactive suspension system, and active suspension system. These systems which are controlled automatically by the controller were established based on the control methods. There are a lot of control methods which are used to control the operation of the active suspension system. These methods have their advantages and disadvantages. Almost, conventional control methods such as PID, LQR, or SMC are commonly used. However, they do not provide optimal efficiency in improving a vehicle’s oscillation. Therefore, it is necessary to establish a novel solution for the active suspension system control to improve the vehicle’s oscillation. In this paper, the method of using the double-integrated controller is proposed to solve the above problem. The double-integrated controller consists of two hydraulic actuators which are controlled completely separately. This is a completely novel and original method that can provide positive effects. This research focuses on establishing, simulating, and evaluating the novel control method (the double-integrated control) for the active suspension system. The results of the research have shown that when the vehicle is equipped with the active suspension system which is controlled by the double-integrated controller, the maximum values of displacement and acceleration of the sprung mass are significantly reduced. They reach only 6.25% and 9.10% (case 1) and 6.00% and 6.12% (case 2) compared to the conventional passive suspension system. Besides, its average values which are calculated by RMS are only about 3.91% and 4.67% (case 1) and 4.48% and 4.77% (case 2) compared to the above case. Therefore, the comfort and stability of the vehicle have been improved. This paper provides new concepts and knowledge about the double-integrated control method which will become the trend to be used in the next time for the systems of the vehicle. In the future, experimental procedures also need to be conducted to be able to more accurately evaluate the results of this research.

Influence of Variable Nonlocal Parameter and Porosity on the Free Vibration Behavior of Functionally Graded Nanoplates

Abstract: This paper studies the influence of the variable nonlocal parameter and porosity on the free vibration behavior of the functionally graded nanoplates with porosity. Four patterns of distribution of the porosity through the thickness direction are considered. The classical nonlocal elasticity theory is modified to take into account the variation of the nonlocal parameter through the thickness of the nanoplates. The governing equations of motion are established using simple first-order shear deformation theory and Hamilton’s principle. The closed-form solution based on Navier’s technique is employed to solve the governing equations of motion of fully simply supported nanoplates. The accuracy of the present algorithm is proved via some comparison studies in some special cases. Then, the effects of the porosity, the variation of the nonlocal parameter, the power-law index, aspect ratio, and the side-to-thickness ratio on the free vibration of nanoscale porous plates are investigated carefully. The numerical results show that the porosity and nonlocal parameter have strong effects on the free vibration behavior of the nanoplates.

Hotel Construction Management considering Sustainability Architecture and Environmental Issues

Abstract: Sustainable development and environment in the activities of the construction industry has attracted the attention of experts in most countries of the world. One of the obvious and problematic features of the construction industry of countries is the use of modern building materials using traditional construction methods. Changing the paradigm for sustainable buildings requires a change in the architectural design process. Today, smart buildings are buildings that are at a lower level in terms of energy consumption and operate in a dynamic and integrated environment, creating a perfect harmony between management, system, services, and structure. These qualities make plastics ideal products for construction and an essential component for a sustainable built environment. In the design of smart and sustainable buildings, the use of environmentally friendly materials increases the lifespan of the building and an effective step is taken towards the design of sustainable architecture. In this paper, we evaluate hotel construction based on sustainability issues with MCDM. The results show that alternative A4 is the best alternative in sustainable issues. With the increasing population and its concentration in large cities, the concern of energy supply and energy efficiency in buildings is one of the main concerns of urban planners, officials, and city residents. Construction projects mainly consume large amounts of materials and leave a huge amount of waste, and this problem sometimes includes existing buildings that cannot be demolished and need to be rebuilt and maintained.

Hydraulic Pump Fault Diagnosis Method Based on EWT Decomposition Denoising and Deep Learning on Cloud Platform

Abstract: An axial piston pump fault diagnosis algorithm based on empirical wavelet transform (EWT) and one-dimensional convolutional neural network (1D-CNN) is presented. The fault vibration signals and pressure signals of axial piston pump are taken as the analysis objects. Firstly, the original signals are decomposed by EWT, and each signal component is screened and reconstructed according to the energy characteristics. Then, the time-domain features and the frequency-domain features of the denoised signal are extracted, and features of time domain and frequency domain are fused. Finally, the 1D-CNN model was deployed to the WISE-Platform as a Service (WISE-PaaS) cloud platform to realize the real-time fault diagnosis of axial piston pump based on the cloud platform. Compared with ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD), the results show that the axial piston pump fault diagnosis algorithm based on EWT and 1D-CNN has higher fault identification accuracy.

Simulation of the Compressive Strength of Cemented Tailing Backfill through the Use of Firefly Algorithm and Random Forest Model

Abstract: Cemented tailings backfill is widely used in worldwide mining areas, and its development trend is increasing due to the technical and economic benefits. However, there is no reliable and simple machine learning model for the prediction of the compressive strength. In the present study, the research process to use artificial intelligence algorithms to predict the compressive strength of cemented tailing backfill was conducted, overcoming the shortcomings of traditional empirical formulas. Experimental tests to measure the compressive strength of cemented tailing backfill were conducted to construct the dataset for the machine learning. Five input parameters (tailing to cement ratio, percentage of fine tailings, cement type, curing time, and solid to water ratio) were considered for the design of the laboratory tests. The firefly algorithm (FA) was used to tune the random forest (RF) hyperparameters, and it was adopted to combine the RF model to improve the accuracy and efficiency for the prediction of the compressive strength of the cemented tailing backfill. By comparing the predicted and actual results, the reliability and accuracy of the prediction model proposed are confirmed. Tailing to cement ratio and curing time are the two most important parameters to the compressive strength of the cemented tailing backfill.

Numerical Simulation and Experiment of the Effects of Blade Angle Deviation on the Hydraulic Characteristics and Pressure Pulsation of an Axial-Flow Pump

Abstract: Inadequate blade angle adjustment or manufacturing errors will cause inconsistencies in the blade angle of an axial-flow pump. In this study, the hydrodynamic characteristics of an axial-flow pump with inconsistent blade angle are investigated by analyzing hydraulic performance and pressure pulsation. The analysis is conducted by performing a numerical simulation combined with a model test. Results show that, relative to the case without blade angle deviation, the case with blade angle deviation exhibits changes in the periodicity of the flow field in the impeller. Such changes result in uneven pressure changes in the impeller passage. The pressure pulsation induced by the blade angle deviation is mainly low-frequency pulsation; that is, it is twice the rotation frequency. The amplitude of the main frequency pulsation is 1.5–3 times that of the blade without angle deviation. This low frequency that dominates the whole pump device easily causes the vibration and weakens the safety and stability of the pump. The blade angle deviation exerts great influence on the unsteady characteristics. Hence, blade angle deviation seriously affects the safe and stable operation of axial-flow pumps and pump stations.

A Simultaneous Fault Diagnosis Method Based on Cohesion Evaluation and Improved BP-MLL for Rotating Machinery

Abstract: In this paper, an improved simultaneous fault diagnostic algorithm with cohesion-based feature selection and improved backpropagation multilabel learning (BP-MLL) classification is proposed to localize and diagnose different simultaneous faults on gearbox and bearings in rotating machinery. Cohesion evaluation algorithm selects high sensitivity feature parameters from time and frequency domain in high-dimensional vectors to construct low-dimensional feature vectors. The BP-MLL neural network is utilized for fault diagnosis by classifying the feature vectors. An effective global error function is proposed in BP-MLL neural network by modifying distance function to improve both generalization ability and fault diagnostic ability of full-labeled and nonlabeled situations. To demonstrate the effectiveness of the proposed method, simultaneous fault diagnosis experiments are conducted via wind turbine drivetrain diagnostics simulator (WTDDS). The experiment results show that the proposed method has better overall performance compared with conventional BP-MLL algorithm and some other learning algorithms.

Intelligent Recognition Method of Turning Tool Wear State Based on Information Fusion Technology and BP Neural Network

Abstract: The multi-information data acquisition system of tool wear condition of CNC lathe is built by acquiring the acoustic emission and vibration acceleration signals. The data of acoustic emission and vibration acceleration signals during the process of CNC machine tool processing under the conditions of different tool wear degrees and different cutting conditions are acquired and analyzed using the orthogonal experimental method. The optimum characteristic frequency band of acoustic emission and vibration acceleration signals was extracted by the wavelet envelope decomposition method so as to recognize tool wear condition as the characteristic parameters. The characteristic information of acoustic emission and vibration acceleration signals during the process of CNC machine tool processing was fused. In addition, the intelligent recognition of tool wear condition during the process of machine tool processing was researched.

Analysis of Contact Mechanical Characteristics of Flexible Parts in Harmonic Gear Reducer

Abstract: Harmonic gear reducer is widely used in industrial robots, aerospace, optics, and other high-end fields. The failure of harmonic gear reducer is mainly caused by the damage of flexible bearing and flexspline of thin-walled vulnerable components. To study the contact mechanical characteristics of flexible components such as flexible bearing and flexspline in harmonic gear reducer, the contact mechanical model of flexible bearing, vibration differential equation of flexspline, and finite element model of each component in harmonic gear reducer were established. Based on the established model of harmonic gear reducer, the influence of the length of flexspline cylinder and the thickness of cylinder bottom on the stress of flexspline is discussed, respectively, and the motion characteristics of flexible bearing are studied. At the same time, the spatial distribution of the displacement of the flexspline and the axial vibration response of the flexspline are studied. The correctness of the model established in this paper is verified by experiments. The results show that the increase of cylinder length can improve the stress of flexspline in harmonic gear reducer; the wall thickness of cylinder bottom mainly affects the stress at the bottom of flexspline but has little effect on the stress of gear ring and smooth cylinder. Along the axis direction of the flexspline, the radial displacement, circumferential displacement, and angular displacement increase linearly with the increase of the axial distance between the cylinder and the bottom. When the excitation frequency is high, the vibration mode of flexspline shell is mainly axial vibration. The research results will provide a theoretical reference for the optimal design of harmonic gear reducer and improving the service life of flexible parts.

Study on Characteristics of Mining Earthquake in Multicoal Seam Mining under Thick and Hard Strata in High Position

Abstract: Rock burst has become one of the most serious world’s problems in coal resources mining, and fracture and movement of thick and hard strata in high position is the main reason to induce strong mining earthquake and rock burst. Multicoal seam mining of 10302 working face in Baodian coal mine is selected as an engineering background, which has thick and hard strata in high position. Using SOS microseismic monitoring system to collect microseismic events and date during multicoal seam mining, characteristic and difference of microseismic in multicoal seam mining under thick and hard rock in high position is analyzed systematically. The main research work is as follows: reveal temporal and spatial distribution and evolution law of microseismic and analyze difference and correlation of microseismic in multicoal mining under thick and hard strata in high position, especially the relationship between mining earthquake with high energy and fracture and movement of thick and hard strata in high position. With the characteristics of microseismic, rock burst mechanism and difference induced by thick and hard strata in high position are discussed. The research and achievement could make guidance to multicoal seam mining safety under thick and hard strata in high position.

Combined finite-discrete element modelling of dynamic rock fracture and fragmentation during mining production process by blast

Abstract: A combined finite-discrete element method (FDEM) is proposed to model the dynamic fracture, fragmentation, and resultant muck-piling process during mining production by blast in underground mine. The key component of the proposed method, that is, transition from continuum to discontinuum through fracture and fragmentation, is introduced in detail, which makes the proposed method superior to the continuum-based finite element method and discontinuum-based discrete element method. The FDEM is calibrated by modelling the crater formation process by blast. The FDEM has well modelled the stress and fracture propagation and resultant fragmentation process. In addition, the proposed method has well captured the crushed zone, cracked zone, and the radial long crack zone. After that, the FDEM is employed to model the dynamic fracture and resultant fragmentation process by blast during sublevel caving process in an underground mine. Then the FDEM has well modelled the stress propagation process, as well as the fracture initiation and fragmenting process. Finally, the effects of borehole spacing and initial gas pressure are discussed. It is concluded that the FDEM is a value numerical approach to study the dynamic rock fracture process by blast.

Fault Diagnosis Approach for Rotating Machinery Based on Feature Importance Ranking and Selection

Abstract: The key to fault diagnosis of rotating machinery is to extract fault features effectively and select the appropriate classification algorithm. As a common signal decomposition method, the effect of wavelet packet decomposition (WPD) largely depends on the applicability of the wavelet basis function (WBF). In this paper, a novel fault diagnosis approach for rotating machinery based on feature importance ranking and selection is proposed. Firstly, a two-step principle is proposed to select the most suitable WBF for the vibration signal, based on which an optimized WPD (OWPD) method is proposed to decompose the vibration signal and extract the fault information in the frequency domain. Secondly, FE is utilized to extract fault features of the decomposed subsignals of OWPD. Thirdly, the categorical boosting (CatBoost) algorithm is introduced to rank the fault features by a certain strategy, and the optimal feature set is further utilized to identify and diagnose the fault types. A hybrid dataset of bearing and rotor faults and an actual dataset of the one-stage reduction gearbox are utilized for experimental verification. Experimental results indicate that the proposed approach can achieve higher fault diagnosis accuracy using fewer features under complex working conditions.