Actual Vibration Research Articles

A Novel Advancing Signal Processing Method Based on Coupled Multi-Stable Stochastic Resonance for Fault Detection

In recent years, methods for detecting motor bearing faults have attracted increasing attention. However, it is very difficult to detect the faults from weak motor bearing signals under the strong noise. Stochastic resonance (SR) is a popular signal processing method, which can process weak signals with the noise, but the traditional SR is burdensome in determining its parameters. Therefore, in this paper, a new advancing coupled multi-stable stochastic resonance method, with two first-order multi-stable stochastic resonance systems, namely CMSR, is proposed to detect motor bearing faults. Firstly, the effects of the output signal-to-noise ratio (SNR) for system parameters and coupling coefficients are analyzed in-depth by numerical simulation technology. Then, the SNR is considered as the fitness function for the seeker optimization algorithm (SOA), which can adaptively optimize and determine the system parameters of the SR by using the subsampling technique. An advancing coupled multi-stable stochastic resonance method is realized, and the pre-processed signal is input into the CMSR to detect the faults of motor bearings by using Fourier transform. The faults of motor bearings are determined according to the output signal. Finally, the actual vibration data of induction motor bearings are used to prove the effectiveness of the proposed CMSR. The comparison results with the MSR show that the CMSR can obtain a higher output SNR, which is more beneficial to extract weak signal features and realize fault detection. At the same time, this method also has practical application value for engineering rotating machinery.

Performance of similitude methods for structural vibration analyses of rectangular plates

Engineers often use similitude analyses to design small scale models for experimental tests or to design size ranges of mechanical structures such as drive technology systems. This paper is concerned with similitude analysis methods for vibration analyses of rectangular plates. If their geometry is scaled by different factors (distorted similitude), the scaling laws approximate the actual vibration responses with a certain accuracy only. This paper introduces a performance measure that reliably assesses how well the scaling laws approximate the actual vibration responses of rectangular plates. This measure, the so-called Mahalanobis distance, applies in a-posteriori analyses, where the vibration responses obtained from the scaling laws are compared to the actual ones. Numerical and experimental investigations on vibrating rectangular plates validate that the Mahalanobis distance is suitable to assess the performance of similitude analyses. The Mahalanobis distance can be linked to the geometrical properties of the rectangular plates in order to define a maximum permissible distortion of the geometry. Scaling laws approximate the vibration responses of the rectangular plates sufficiently well up to this maximum permissible distortion. Furthermore, the performance of two different state-of-the-art similitude analysis methods is compared. Both similitude analysis methods are found to perform well up to the maximum permissible amount of geometrical distortion.

Influence of spanwise flexibility on steady and dynamic responses of airfoils vs hydrofoils.

There is growing interest in using lighter and more flexible foils with active and/or passive smart actuation mechanisms to increase efficiency and maneuverability. For efficient operations, airfoils and hydrofoils tend to have an effective aspect ratio greater than two, which permits spanwise flexibility depending on the choice of material and architectural design. The fluid density plays an important role in the steady and dynamic performance, including the governing hydroelastic instability mechanism. To understand the influence of spanwise flexibility, we examine the response of a rectangular cantilevered foil as a canonical proxy for more complex lift generating devices. This research aims to investigate the influence of key geometric, material, and flow parameters on the steady and dynamic responses of the spanwise flexible airfoils vs hydrofoils. The results are obtained using an inviscid frequency- and time-domain fluid-structure interaction model that accounts for flow-induced bend-twist coupling terms and are compared with inviscid theory results without the flow-induced bend-twist coupling terms. The results show that the flow-induced bend-twist coupling effects impact the natural frequencies and damping coefficients, and such effects grow with increasing fluid density and flow speed. Hence, the flow-induced bend-twist coupling effects are more critical for hydrofoils than airfoils, particularly for the twisting mode. Ignoring the flow-induced bend-twist coupling terms leads to an incorrect prediction of flutter speed and over-prediction of the damping, which is dangerous because the actual vibrations and dynamic load amplification may be higher than the prediction, which could lead to accelerated fatigue. The results demonstrate that flexible hydrofoils have much lower natural frequencies and higher damping coefficients than airfoils due to higher fluid inertial and damping forces, both of which are proportional to the fluid density. While all components of the fluid forces are proportional to the fluid density, the fluid damping forces grow with the velocity, and the fluid disturbing forces grow with velocity square. Therefore, divergence tends to be the governing instability mode for hydrofoils, while flutter is typically the governing instability mode for airfoils. The maximum aero/hydro-elastic bending and twisting deformations are limited by the corresponding reduced stable velocity to avoid divergence or flutter.

The Potential Influence of Tree Crown Structure on the Ginkgo Harvest

Ginkgo biloba L. has significant health benefits and considerable economic value, but harvesting the fruit is highly labor-intensive. Mechanical vibration harvesting has been shown effective in harvesting various fruit types. In the study of vibration harvesting, the research on the vibration characteristics of fruit trees focuses on the natural frequency (resonance frequency), model, and damping coefficient, which are the main factors affecting the vibration characteristics of trees. But field harvesting experiments have shown that the tree structure may have an impact on the vibration characteristics of the fruit tree and the efficiency of mechanical harvesting. In addition, the research on the damping coefficient of fruit trees is mainly low-frequency damping, and the relevant results cannot be applied to the actual vibration harvesting frequency range. Applying a natural frequency with low damping coefficient to excite a tree can reduce additional energy dissipation. This study explored the influence of ginkgo crown structure on the vibration characteristics and the law of damping changes with frequency. After counting 273 ginkgo trees, two typical ginkgo crown structures, monopodial branching and sympodial branching, were selected to be analyzed for vibration spectrum and damping coefficient. The vibration models for different crown-shaped ginkgo trees were simulated to analyze the vibration state at different frequencies. For sympodial branching ginkgo trees, the consistency of natural frequencies at different branches was better than monopodial branching ginkgo trees. The finite element model analysis shows that monopodial branching ginkgo trees have mainly partial vibrations at different branches when vibrating at high frequencies. The high-frequency vibrations in sympodial branching reflect the better overall vibration of the canopy. The damping coefficients for the two crown types decreased with the increase in frequency. The monopodial branching damping coefficient was 0.0148–0.0298, and the sympodial branching damping coefficient was slightly smaller at 0.0139–0.0248. Based on the test results, the sympodial branching ginkgo tree has better vibration characteristics. The results indicate that controlling the crown structure of fruit trees to be sympodial branching by pruning may help improve the overall vibration characteristics of fruit trees.

An accurate deicing method by utilizing the piezoelectric materials based on the active mode control theory

Icing has a great influence on the dynamic characteristics of structures. One of an effective deicing method is to utilize the shear stresses caused by the transverse vibration of the structure, and this is usually realized by exciting the proper natural vibration mode according to the icing position. However, the natural mode cannot always be consistent with the icing position, and on the other hand, although there is vibration at the icing position, it will also cause vibration at other positions of the structure. To solve these problems, an accurate and efficient deicing method is proposed in this study. In this method, piezoelectric materials are used. They are bonded on the surface of the structure to act as the actuators. According to the icing positions, an appropriate mode shape is assumed, the peak value of which is consistent with the position of the ice. To achieve this goal, it is essential to obtain the input voltages of the piezoelectric actuators. The genetic algorithm is introduced, through which the optimal control voltage of each actuator can be found. As a result, the actual vibration of the structure will tend to the designed deicing vibration mode. Experimental and simulation results show that the present active mode control method possesses better deicing effect compared with the former natural vibration mode method. In addition, there is almost no vibration where there is no ice, which ensures the stability of the structure.

Blasting pattern optimization using gene expression programming and grasshopper optimization algorithm to minimise blast-induced ground vibrations

Blast-induced ground vibration is considered as one of the most hazardous phenomena of mine blasting, which can even cause casualties and severe damages to the adjacent properties. Measuring peak particle velocity (PPV) is helpful to know the actual vibration level but prediction of blast vibration prior to the blast is a tedious job due to involvement of blast design, explosive and rock parameters. Nowadays, efficient application of intelligent systems has been approved in different branches of science and technology. In this paper, a gene expression programming (GEP) model was developed to predict PPV using various blasting patterns as model inputs, which showed a high level of accuracy for the implemented model. Also, to optimize blast pattern attaining minimum ground vibration during blasting operation, the developed functional GEP model was taken as objective function for grasshopper optimization algorithm (GOA). Construction of GOA model was performed using a trial and error mechanism to find out the best possible pertinent GOA parameters. Finally, it was observed that utilizing GOA technique, PPV can be reduced by 67% with optimized blast parameters including burden of 3.21 m, spacing of 3.75 m, and charge per delay of 225 kg. A sensitivity analysis was also performed to understand the influence of each input parameters on the blast vibrations.

Blind Source Separation Method of Dual-Channel Single Vibration Signal Based on Power Spectral Density

In this paper,a convolution-mixing model of vibration signal is established according to the actual vibration signals collected by the sensor. This model is simulated and separated in P-waves and S-waves by using the frequency domain convolution blind source separation algorithm based on power spectral density matrix and diagonalization, and separated the vertical and horizontal waves of the actual two-channel underground vibration signals, which lays a technical foundation for the study of seismic characteristics and the inversion of source location.

Wireless Sensor Network–Based Structural Health Monitoring of Bridges Using Advanced Signal Processing Techniques

Abstract Continuous growth in the networks of roads and bridges pose a continuously rising challenge to maintenance agencies for ensuring accident-free operations of transportation network. This challenge exacerbates in developing economies. Specifically, in port/harbor areas, the challenge increases many folds because of the corrosive environment. Assessment of aging civil infrastructure integrity is imperative to guarantee safe operations. Wireless sensor networks (WSNs) offer reliable and cost-effective automated applications for structural health monitoring (SHM) of civil infrastructure. This article presents a vibration analysis–based method for SHM of civil infrastructures using WSNs. In the proposed research work, advanced signal processing techniques are applied on actual vibration data acquired using low-power WSNs to classify healthy and degraded civil structures. The proposed scheme has successfully been implemented on a 20-year-old concrete bridge situated in the harbor area of Karachi, Pakistan. Micro electro mechanical system–based accelerometers connected to low-power–consuming wireless nodes are installed on the bridge under study. The acquired vibration data are uploaded on the cloud. Subsequent application of time-frequency energy analysis of the recorded vibration data enabled the discrimination between healthy and degraded structures. The proposed scheme can be used for civil infrastructure monitoring at remote sites using low-power WSNs with the least intervention.

Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis

Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index.

Ultrasonic vibration-assisted grinding of silicon carbide ceramics based on actual amplitude measurement: Grinding force and surface quality

The actual vibration amplitude (AVA) during ultrasonic vibration-assisted face grinding (UVAFG) is an essential parameter that affects grinding force and surface quality. However, an effective method for the real-time measurement of AVA during grinding remains unavailable, hindering research on UVAFG. In this study, an experimental setup for measuring AVA is firstly established using an eddy current sensor. Ultrasonic amplitude curves under the condition of tool rotation without load are measured and analysed. Then, amplitude attenuation with different grinding forces is evaluated during the UVAFG of silicon carbide (SiC) ceramics. The influences of AVA on grinding force and surface quality are investigated through comparative experiments of the UVAFG and conventional grinding (CG) of SiC ceramics. Finally, experimental results indicate that AVA exhibits a negative correlation with grinding force. Compared with CG, UAVFG has lower grinding force when grinding SiC ceramics. AVA is the key factor that affects surface roughness during UVAFG, the AVA of UVAFG is less than 4.3 μm; thus, surface roughness during UAVFG will be less than that during CG. Moreover, vibration can reduce the scratches made by the tools on the grinding surface.

Development of Vibration and Stress Estimation Technology for Mechanical Structures by Virtual Sensing

In this paper, we propose a virtual sensing technology combined with model order reduction. The virtual sensing is a simulation technology that integrates a numerical model and sensor data with a Kalman filter. In addition, the virtual sensing enables to perform various analysis using the estimated states. However, since the virtual sensing is computed in the time domain, the use of finite element models with large degrees of freedom increases the computational cost. This issue can be solved by the model order reduction which is a technique to reduce the computational cost by reducing the degree of freedom of the finite element model while maintaining the analytical accuracy. The proposed methodology is validated experimentally by which the states are estimated by the virtual sensing using the finite element model reduced by the model order reduction and the actual vibration measured by a small number of sensors when a motor is driven. The virtual sensing estimates 3-axis acceleration accurately. Then, the Mises stress map in the piping is calculated by using estimated vibrational displacement.

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element of rolling bearings due to long transmission path, a novel fault diagnosis method based on variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD), namely VMD-MCKD-FD is proposed for rolling elements of rolling bearings in this paper. In the proposed VMD-MCKD-FD, the vibration signal of rolling element of rolling bearings is decomposed into a series of Intrinsic Mode Functions (IMFs) by using VMD method. Then the number of modes with outstanding fault information is determined by Kurtosis criterion in order to calculate the deconvolution period T. The periodic fault component of reconstructed signal is enhanced by using sensitivity MCKD method. Finally, the power spectrum of the reconstructed signal is analyzed in detail in order to obtain the fault frequency and diagnose the rolling element fault of rolling bearings. The simulation signal and actual vibration signal are selected to verify the effectiveness of the VMD-MCKD-FD method. The experimental results show that the VMD-MCKD-FD method can effectively diagnose the rolling element fault of rolling bearings and obtain better fault accuracy.

A New Performance Degradation Evaluation Method Integrating PCA, PSR and KELM

In order to better characterize the performance degradation trend of rolling bearings, a new performance degradation evaluation method based on principal component analysis (PCA), phase space reconstruction (PSR) and kernel extreme learning machine (KELM), namely PAPRKM is proposed to evaluate the performance degradation of rolling bearings in this paper. In the PAPRKM method, the time-domain and frequency-domain features of the vibration signal are extracted to construct the high-dimension feature matrix. Then the PCA is used to reduce the dimension of the feature matrix in order to represent the running state and the declining trend of rolling bearings, so as to eliminate the redundancy and information conflict among these features. Nextly, the PSR is adopted to obtain more relevant information from the time series. By determining the delay time and embedding dimension, the time series are reconstructed to obtain a new performance degradation index, which is regarded as the input data to input into KELM, and the degradation trend prediction model is established to realize the performance degradation trend prediction. Finally, the actual vibration signals of rolling bearings are applied to prove the effectiveness of the PAPRKM. The obtained experimental results show that the PAPRKM method can effectively predict the performance degradation trend of rolling bearings. The predicted results are more accurate than the other compared methods.

Experimental dynamic analysis of rod-fastening rotor bearing system with a transverse crack

PurposeThis study aims to obtain the dynamic behaviours of cracked rod-fastening rotor bearing system (RFBS), and experimental investigation was carried out to examine the dynamic characteristics of this kind of assembled rotor bearing system with a transverse crack passing through the critical speed.Design/methodology/approachAn experimental test rig of cracked RFBS was established for examining the vibration behaviours between intact and cracked system. The crack on the surface of a fastening rod was simulated by wire-electrode cutting processing method. The comprehensive analysis method of vibration was used to obtain the dynamic characteristics such as vibration amplitude, acceleration and whirling orbits before and after the critical speed as well as the instantaneous response in the process of speed up.FindingsSome experimental vibration datum is obtained for cracked RFBS. The appearance of a crack will introduce the initial bending and make the vibration amplitude, acceleration and instant response in the process of speed up increase greatly as well as the change of whirling orbits.Originality/valueThe actual vibration characteristics for this complex assembled rotor system with a transverse crack are given passing through the critical speed. It can provide some useful help for monitoring the vibration behaviours of this kind of assembled rotor system as well as the detection of the crack fault.Peer reviewThe peer review history for this article is available at:https://publons.com/publon/10.1108/ILT-07-2020-0260/

An improved phenomenological model of vibrations for planetary gearboxes

Interpreting and understanding vibration characteristics are the cornerstone for health condition monitoring schemes of a planetary gearbox. Phenomenological modeling provides a concise and efficient mathematical description of sensor-related vibration measurements, thus allowing prior guidance for spectrum analysis to be relied on. In this paper, some overlooked problems during the modeling process in previous researches are pointed out and analyzed. Some discoveries are consequently obtained: First, the asymmetry of spectrum structure should be attributed to the magnitude randomness of gear meshing vibration rather than different phase angles of multiple-planet gears; Second, the basic frequency component to constitute a model, namely a product of the rotational frequency of a planet carrier and the number of planet gears, is far from sufficient to describe the actual vibrations; Finally, improper function the phases of multiple-planet gears will lose some sidebands that should have been. For these reasons, we map the phase variation of multiple-planet gears into the time-varying propagating distance, therefore, solving the neutralization phenomena of the frequency components. With consideration of the attenuation effect and random gear meshing magnitudes, an improved model is proposed to provide a more reasonable representation of the vibration behaviors, both for the dominant frequency components and structure. Simulation and experimental studies both demonstrate the higher fidelity and descriptive capability than the traditional models.

An adaptive boundary determination method for empirical wavelet transform and its application in wheelset-bearing fault detection in high-speed trains

Wheelset bearing plays a fairly important role in maintaining the safe and stable operation of high-speed trains. Thus, its fault detection is of great significance. Conventional boundary determination methods cannot assess fault information capacity contained in a decomposed band and thus provide a higher number of false boundaries than actually needed. A time-delayed kurtosis-based (TDK) adaptive boundary determination method (TDKABDM) is presented. The proposed TDK, is a measurement index for assessing fault information capacity within a noisy bandwidth. The boundaries determined by TDKABDM are then used to construct Meyer wavelets to extract fault-related resonance bands. The proposed method is validated by fault simulation signals and actual wheelset-bearing vibration signals. The results show that the proposed method can adaptively determine the reasonable boundaries of resonance bands containing fault information. Comparative analyses indicate that the proposed TDKABDM exhibits more excellent performance on extracting wheelset bearing fault characteristics.

Sagnac Vibration Sensing System With Nested Pulse Method

A novel vibration location method with nested pulses for distributed optical fiber vibration sensing system is proposed. The interferometric structure for vibration detection and location with nested pulse method is firstly designed. The continuous-wave light is modulated into a series of periodic nested pulses in the linear Sagnac interferometer. Each nested pulse contains a certain number of sub pulses to locate the position of vibration signal with the help of envelope derivation and threshold judgment. Experimental results show the location feasibility for vibration signals at different positions of long-range optical fiber, with the positioning error of 10m at 0.5 km and the positioning error of 26 m at 45.314 km. This method provides one solution for the location of actual vibration without the limit of frequency band.

Modal analysis on swinging structure of hydraulic press

In the actual work of hydraulic press, in order to ensure the design of auxiliary punching structure, that is, swing punching structure runs smoothly, the modal analysis of swing punching structure is carried out by using ANSYS software. The first 40 natural frequencies are determined, and the first 10 modes are analyzed. The shape variables and deformation modes are determined. Thus, the actual vibration response of the structure under the action of various external or internal vibration sources in each frequency band can be predicted to avoid resonance phenomenon in the work. At last, the aim of safety production, material saving and cost reduction is achieved.

Sparse feature extraction based on periodical convolutional sparse representation for fault detection of rotating machinery

Sparse fault transient extraction is the primary step in rotating machine fault detection. In the present paper, periodical convolutional sparse representation (PCSR) is proposed for reliable separation of fault transients imbedded in raw vibration signals. Specifically, a sparse optimization problem of PCSR is constructed, in which periodical fault transients and harmonic components are sparsely represented by a learned dictionary and Fourier dictionary, and the periodicity and group sparsity of sparse coefficients related to sparse fault transients are also incorporated. Meanwhile, to further promote the sparsity of the sparse coefficients, a non-convex function is also introduced into the optimization problem. In addition, an iterative algorithm is developed to resolve the constructed sparse optimization problem, and the parameter selection method is also investigated to ensure the fault transient extraction ability of PCSR. The performance of the proposed PCSR is assessed via a synthetic and actual vibration signal. The results illustrate that the proposed PCSR has an excellent ability in fault transient extraction and machine fault detection.

Designing Hand-Held Vibration Measuring Device For Industrial Machines

Evaluating the quality of industrial machines, the vibration meter is used to measure the machine’s actual vibration. The two most important parameters describing machine vibration, amplitude, and frequency, are the key factors for determining the cause of vibrations. Spectral analysis of the vibration signal will give information about the vibration level and choose which part of the machine leads to the observed vibration signal. This paper presents a self-made machine vibration measuring device with a simple structure, compact size, high accuracy at a reasonable price. The device can display the measured vibration signals’ real-time value and analyze the computer’s spectral to produce the required parameters.