Vibration Displacement Measurement Research Articles

Process Stability Analysis during Trochoidal Milling of AZ91D Magnesium Alloy Using Different Toolholder Types

Trochoidal milling is one of the solutions for increasing the efficiency of machining processes. A decreased cutting tool’s arc of contact leads to a reduction in the generated cutting forces, thus improving process stability. Vibration is an inherent part of any machining process, affecting the accuracy and quality of the manufactured components, but it can also pose a danger to machine operators. Chatter is particularly detrimental, leaving characteristic marks on shaped surfaces and potentially leading to catastrophic tool damage. Therefore, it is important to ensure the stability of machining and also reduce vibration. The primary purpose of the conducted research is to evaluate the stability of the milling process of the AZ91D magnesium alloy performed through a trochoidal strategy. An additional objective is to establish the effect of the variation in machining parameters and toolholder types on milling stability. Three types of toolholders most commonly used in industry are used in the study. The basis of the investigation is the measurement of vibration displacement and acceleration analysed in the time domain. A spectral analysis of the signals is also performed based on Fast Fourier Transform, to identify signal components and detect the susceptibility to chatter occurrence. An important part of the study is also an attempt to use the Composite Multiscale Entropy as an indicator to determine the stability of the machining processes. Entropy does not exceed the values of 1.5 for cutting speed and 2.5 for feed per tooth, respectively. Vibration acceleration does not exceed (in most cases) the value of 20 m/s2 for the peak-to-peak parameter and the shrinkfit toolholder. For vibration displacement (peak-to-peak parameter), there are oscillations around the value of 0.9 mm for all kinds of toolholders.

Vibration displacement measurement method based on vision Gaussian fitting and edge optimisation for rotating shafts

The vibrations of rotating machinery contain crucial information for monitoring the operational status of machines and diagnosing potential faults. Measuring vibration displacement using machine vision is currently a promising approach for machine monitoring in industries. However, the existing vision-based vibration displacement measurement methods cannot satisfy the high-accuracy requirements for detecting micrometre-level displacements generated by low-amplitude vibrations of rotating machinery. Therefore, this study proposes an improved noncontact-type vibration displacement measurement method based on Gaussian fitting and edge optimisation for rotating shafts. The real number-type edge positions of the rotating shafts within the selected region of interest (ROI) are accurately extracted via Gaussian edge fitting. Subsequently, edge-linking schemes, double-threshold detection and edge fitting are preformed to optimise the real number-type position of the edges. Finally, continuous displacement extraction of rotating shafts is achieved by monitoring the edge positions in the ROI. The coefficient of determination is used to evaluate a series of simulated and actual experiments quantitatively. The experimental results verify the feasibility, effectiveness, and robustness of the proposed method. An eddy current sensor is used in the actual experiments to verify the accuracy of the proposed method. The result shows that the coefficient of determination reaches 0.9792 when the amplitude of the measured displacement signals is only 22.7 μm.

A single-stage anchor-free rotating target visual detection algorithm suitable for flexible body vibration displacement measurement

The phenomenon of angular inclination of flexible structures during vibration poses a significant challenge to the applicability of visual vibration measurement methods because the target locked in the captured image will produce unknown geometric deformations such as scale, displacement, and angle in the time domain space, and the horizontal rectangular frame used for matching during target detection will also increase the false detection rate of the target due to the introduction of more background information. Such subtle geometric deformations and false detections can lead to severe fit errors in the displacement curves regressed by the visual vibration measurement algorithm. To effectively improve the accuracy and robustness of vibration image target recognition, this article takes the flexible body captured by a high-speed camera as the target of vibration displacement measurement. It introduces the rotating target detection method based on deep learning into the field of visual vibration measurement, which verifies the feasibility of the deep learning method in flexible body vibration measurement, and based on the deep convolutional neural network framework, a high-precision displacement measurement algorithm based on single-stage anchor-free rotating target detection is proposed. The algorithm in this article first uses the CSPDarknet backbone network to extract multi-scale features of flexible structural image sequences. It then uses PANet to fuse the top-down and bottom-up bidirectional feature maps of the four bridge target feature maps obtained through the backbone network. The shallow and deep information is used for semantic feature fusion and combined with the Coordinate Attention mechanism to achieve target finding and fine positioning on the feature map. Finally, we use the coordinates of the bounding box obtained from the test to regress the position offset of the object’s center point. To verify the accuracy of the algorithm in this article, we conducted experimental validation on the cable-stayed bridge model and the actual bridge and compared the performance with the traditional template matching algorithm, differential optical flow method, and various deep learning algorithms with different localization principles, as well as the displacement signals collected and processed by accelerometers. The experimental results of time-frequency characteristics analysis show that the vibration displacement trajectories regressed by the algorithm in this paper have the best overlap with the displacement measurements collected by the accelerometer, which verifies that the algorithm in this article has good application potential and implementation space in the field of condition monitoring of flexible structural bodies.

A visual measurement algorithm for vibration displacement of rotating body using semantic segmentation network

Compared with the traditional vibration displacement measurement methods, visual vibration measurement offers several advantages such as long-distance capability, non-contact operation and easy installation. However, the phenomenon of low fitting accuracy of the bounding box often occurs when detecting rotating objects, resulting in a slight deviation in the relative offset of the center vibration point of the target between frames, which will cause a serious deviation in the regression of vibration displacement offset. In this paper, a high-speed industrial camera is employed as the image acquisition medium, and a deep learning-based semantic segmentation method is introduced to address visual vibration measurement challenges in rotating body. Specifically, the CSP module integrates different depth semantic information which is introduced into the Mobiledets backbone network in a targeted manner. This is not only strengthens the performance of the network for segmenting vibration objects, but also dramatically improves the practical performance of the algorithm. The conventional Relu activation function is substituted with Mish activation function, making the network more adept at segmenting rotating body in challenging backgrounds with varying illumination, blur, and similarity. The CSP+Mobiledets backbone network constructed in this study outperforms the U-Net network in terms of feature extraction effectiveness. Adding Dice-loss to the original loss function can more effectively solve the severe imbalance problem of samples caused by long-distance image acquisition. We take the most representative rotating body-rotor as the experimental subject. The displacement curve obtained by the existing algorithm has the best degree of fit with the signal curve collected by the eddy sensor. The results of different segmentation algorithms and detection algorithms on time domain curve plot, frequency domain plot and axis orbit plot are collectively compared. Furthermore, the results also provide valuable guidance for visual measurement of the vibration displacement of the rotating body in specific industrial scenarios.

Research on full-field vibration displacement measurement based on grid CCD moiré method

The development of full-field, accurate vibration testing techniques has been vastly valued by the academic and engineering community. However, due to the limited data transmission speed of digital cameras, the contradiction between speed and resolution remains in the traditional full-field vibration measurement systems based on image measurement, failing to acquire high-resolution images at high-speed simultaneously. In this paper, a high-speed and high-resolution full-field vibration displacement measurement system is established based on the grid CCD moiré method. A periodic grid is arranged on the surface of the sample as a deformation carrier, and the periodic pixel of the digital camera is used as a reference grating that ‘enlarges’ the grid on the surface of the sample to form a grid CCD moiré with a geometry significantly larger than the original, capturing images for deformation analysis. This approach lowers the requirement of digital image resolution for high-resolution deformation measurement, reducing the contradiction between speed and resolution in full-field vibration measurement. In this paper, the principle and implementation of full-field vibration measurement using the CCD moiré method are introduced, and the experiment results of full-field in-plane vibration displacement measurement are included, verifying the effectiveness and reliability of the system.

Spatial displacement tracking of vibrating structure using multiple feature points assisted binocular visual reconstruction

Current vision-based displacement measurement methods have limitations of requiring manual target and parameter adjustment, along with significant user involvement to achieve the desired results. This paper proposes an advanced and comprehensive vision system based on ZED cameras for full-field three-dimensional (3D) vibration displacement measurement of civil structures. Compared to existing binocular systems consisting of multiple monoculars, ZED binoculars offer greater flexibility on instrument setup and data acquisition. A novel keypoint detection and matching algorithm based on deep learning is used to achieve target-free measurement, which is capable of capturing feature points with high precision. The performance of the proposed vision-based approach is evaluated through experimental studies on a six-story scaled frame structure. Statistical analysis of the comparative results in sinusoidal cases demonstrates that the displacement responses obtained from the proposed vision-based approach show consistent results in compliance with displacement sensor measurements. System identification of the target structure is achieved through a seismic case and the error analysis is also concluded in detail.

Monocular vision based 3D vibration displacement measurement for civil engineering structures

Vibration displacement of civil structures are crucial information for structural health monitoring (SHM). However, the challenges and costs associated with traditional physical sensors make displacement measurement difficult. In recent years computer vision (CV) techniques have been employed for measuring vibration displacement in civil structures. There has been a growing interest in CV-based three-dimensional (3D) displacement measurement, as it provides comprehensive information for structural health assessment. Most existing methods use multi-view geometry, requiring multiple cameras for depth measurement. This paper proposes a new system for measuring the 3D vibration displacement utilising a single camera. Instead of using multi-view geometry, deep neural networks are utilised to learn the depth of scenes from monocular images. Compared with the multi-view methods, the proposed 3D measurement system with monocular vision is more cost-effective and much more convenient to set up and use in practice, avoiding the complicated calibration and object matching between multiple cameras. Experimental tests are conducted in the laboratory to investigate the feasibility of the proposed system. Physical displacement sensors are equipped with the testing structure to provide the ground truth data. The results demonstrate that the proposed monocular 3D displacement system is able to produce reasonable 3D full-field displacement measurement, which makes monocular image based CV system a promising approach to achieve 3D displacement measurement, with its obvious advantages in cost and convenience compared to the traditional sensor-based or multi view CV-based methods.

Rotating box multi-objective visual tracking algorithm for vibration displacement measurement of large-span flexible bridges

Visual displacement measurement methods for flexible structural bodies like large-span bridges has gained wide popularity in recent years, but practical applications still have some limitations. For instance, when acquiring images of large-span flexible bridges at a distance, the slight angular tilt of the detection target due to irregular vibrations can cause extremely serious misfit errors in the displacement curves returned by the vision measurement algorithm. To improve the reliability of vibration displacement measurement of flexible structural bodies, this paper takes the bridge subjected to external excitation in the acquired image sequence as the object of vibration displacement measurement and uses a designed high-precision displacement measurement algorithm for a single-stage rotating target tracking anchor-free box to track the vibration displacement of the target in the flexible structural body. We first extract multi-scale feature information of bridge model image sequences using the improved YOLOv5-s backbone network and combine the Transformer self-attention mechanism with PANet to perform a top-down and bottom-up bi-directional fusion of target feature maps at three different scales to achieve semantic feature fusion of shallow and deep information. Second, the improved Efficient Decoupled Head performs the detection of rotating target centroid offset and bounding box size. Finally, the detected results are passed into the multi-objective tracking algorithm ByteTrack, which strengthens the spatio-temporal correlation between frames and obtains a better-fitting vibration displacement curve. The validation and comparison of traditional visual measurement methods and deep learning measurement methods on cable-stayed bridge models, small arch bridges, and large span bridges show that the vibration displacement trajectories regressed by the algorithm in this paper have the best fit with the actual vibration displacement trajectories, which also verifies that the algorithm in this paper has good potential for engineering applications and implementation space in the field of condition monitoring of flexible structural bodies.

VDDNet: An Object Detection Model for Measuring the Vibration Displacement of Rotating Structure

The signal acquisition and analysis of the structure vibration can effectively diagnose the operation status and fault type. The visual inspection method has the advantage of non-destructive testing and providing full-field vibration information for monitoring the vibration of rotating machinery. However, the accuracy requirements for measuring the small displacements generated by the low-amplitude vibration of high-speed rotating machinery are extremely high. It remains a challenge to achieve accurate, precise, and fast extraction of high-frequency and small-amplitude micro-vibrations using visual-based displacement measurement methods. Therefore, this paper proposes the object detection model, the Vibration Displacement Detection Network (VDDNet), for measuring vibration displacement of rotating bodies. VDDNet reduces the required computational complexity of the actual detection target and optimizes the quality of vibration displacement measurement. Additionally, by introducing an attention module and adopting different learning strategies to share rotating body target features, the real-time and accuracy of vibration monitoring are ensured. Furthermore, the evaluation method used in this paper comprehensively considers detection accuracy, real-time performance in deep learning methods, and displacement capture accuracy in vibration displacement monitoring. Actual vibration experimental results demonstrate that the proposed method is minimally affected by illumination and can achieve a better real-time capture accuracy of rotor micro-displacements, further proving its effectiveness and feasibility, which is helpful in advancing related research in this field.

Using Semantic Segmentation Network to Measure Vibration Displacement of Rotating Body

Compared with traditional vibration measurement sensors, visual vibration displacement measurement technology has many advantages, such as long-distance, non-contact and non-interference. However, when it comes to complex working conditions or inconvenient pre-processing scene, visual measuring techniques usually can’t produce results with the high precision needed for vibration analysis. In this paper, we proposed a visual measurement algorithm for vibration displacement of rotating body using semantic segmentation network by taking a high-speed industrial camera as the image acquisition medium and rotor as the experimental object. To make the network better applicable to segmentation of rotating body targets in different illumination, blur and similarity contexts, the cross stage partial (CSP) connections, concat channel fusion module and shuffle attention (SA) are purposefully incorporated into proposed CMCS convolution module. During calculating the loss in the training network stage, we add the binary cross-entropy loss and Dice-loss to solve the problem of serious imbalance between positive and negative samples caused by remote photography. The vibration signal synchronously collected by eddy current sensor is exploited as standard signal to compare the different segmentation algorithms quantitatively and qualitatively. The experimental results show that the RMSE between vibration displacement signal regressed by our segmentation algorithm and standard signal is the smallest, and the time domain diagram, frequency domain diagram and shaft orbit diagram are also more fitted to the standard signal curve, which provides valuable guidance for the visual vibration displacement measurement of rotating body in complex industrial setting.

A visual measurement method of structural body vibration displacement combined with image deblurring

The visual vibration displacement measurement task is limited by the acquisition equipment or the acquisition environment, and there are still defects in the recognition rate. In this paper, starting from the aspect of enhancing the feature information of the input image, a visual measurement method for structural body vibration displacement is constructed combining deblurring and image feature enhancement. Aiming at problems such as blurring that may be encountered during image data acquisition, this paper designs a Multi-Scale Structural Body Image Deblurring Network (MSDNet) based on the Encoder–Decoder architecture to enhance the target feature details in the structural body image. MSDNet obtains more detailed feature information of the structural body by aggregating feature information at different scales in the Encoder–Decoder architecture. Meanwhile, this paper uses the super-resolution image reconstruction method in the Decoder stage instead of the upsampling method to better preserve the feature information of the underlying image. To verify the effectiveness of the method proposed in this paper, a variety of structural body vibration displacement image datasets were produced, and the sensor simultaneously collected structural body displacement data as standard data. The follow-up experiments can also be concluded that the image data after deblurring processing can have a better measurement accuracy of structural body vibration displacement.

Piezoelectric atomization of liquids with dynamic viscosities up to 175 cP at room temperature

Piezoelectric atomization has been applied in the field of respiratory medicine delivery and chemistry. However, the wider application of this technique is limited by the viscosity of the liquid. High-viscosity liquid atomization has great potential for applications in aerospace, medicine, solid-state batteries and engines, but the actual development of atomization is behind expectations. In this study, instead of the traditional model of single-dimensional vibration as a power supply, we propose a novel atomization mechanism that uses two coupled vibrations to induce micro-amplitude elliptical motion of the particles on the surface of the liquid carrier, which produces a similar effect as localized traveling waves to push the liquid forward and induce cavitation to achieve atomization. To achieve this, a flow tube internal cavitation atomizer (FTICA) consisting of a vibration source, a connecting block and a liquid carrier is designed. The prototype can atomize liquids with dynamic viscosities up to 175 cP at room temperature with a driving frequency of 507 kHz and a voltage of 85 V. The maximum atomization rate in the experiment is 56.35 mg/min, and the average atomized particle diameter is 10 µm. Vibration models for the three parts of the proposed FTICA are established, and the vibration characteristics and atomization mechanism of the prototype were verified using the vibration displacement measurement experiment and the spectroscopic experiment. This study offers new possibilities for transpulmonary inhalation therapy, engine fuel supply, solid-state battery processing and other areas where high-viscosity microparticle atomization is needed.

Target-free vision-based approach for modal identification of a simply-supported bridge

Accurate and efficient vibration response measurement is one of the key tasks in structural health monitoring (SHM) of civil engineering structures. Normally, dynamic responses such as displacement responses, are acquired by physical sensors and data acquisition systems installed on the structures. However, the installation of sensors may be time-consuming and costly. In recent decades, computer vision-based methods have been introduced into civil engineering to provide alternatives for traditional displacement measurements. However, the camera specifications, such as limitations on field of view (FOV) and relatively low resolution of economic lens, have prevented the vision-based method from being widely used for vibration displacement measurement on relatively large-scale structures. This paper proposes a target-free vision-based approach for dynamic displacement measurement and modal identification of simply-supported bridges using a single camera. In this study, a series of videos are captured in separate experimental tests using a consumer-class GoPro on a long-span beam subjected to a moving load. The radial distortion of videos caused by the wide-angle lens is offset by distortion adaption, and the scale factor is fitted using a static video shooting experiment. The tracking process consists of three basic steps, namely, region of interest (ROI) selection, natural feature extraction and features tracking. The results are compared with those measured by linear variable differential transducer (LVDT), demonstrating a high accuracy by the proposed approach. Vibration characteristics, including natural frequencies and mode shapes of the bridge, are identified from the measured displacement. The overall mode shapes are obtained by combining the mode shapes identified from different segmental videos of the bridge obtained by roving the single camera along the bridge for measurements. Comparisons of the natural frequencies and mode shapes between the results from the proposed approach and LVDTs demonstrate the effectiveness and accuracy of the proposed approach for displacement measurement and modal identification of long bridges under moving loads.

Blade Crack Diagnosis Based on Blade Tip Timing and Convolution Neural Networks

The diagnosis of blade crack faults is critical to ensuring the safety of turbomachinery. Blade tip timing (BTT) is a non-contact vibration displacement measurement technique, which has been extensively studied for blade vibration condition monitoring recently. The fault diagnosis methods based on deep learning can be summarized as studying the internal logical relationship of data, automatically mining features, and intelligently identifying faults. This research proposes a crack fault diagnostic method based on BTT measurement data and convolutional neural networks (CNNs) for the crack fault detection of blades. There are two main aspects: the numerical analysis of the rotating blade crack fault diagnosis and the experimental research in rotating blade crack fault diagnosis. The results show that the method outperforms many other traditional machine learning models in both numerical models and tests for diagnosing the depth and location of blade cracks. The findings of this study contribute to the real-time online crack fault diagnosis of blades.

Accurate Measurement of Bridge Vibration Displacement via Deep Convolutional Neural Network

Accurate Measurement of Bridge Vibration Displacement via Deep Convolutional Neural Network

A Dual Transformer Super-Resolution Network for Improving the Definition of Vibration Image

Visual measurement methods are gaining more and more attention in the field of structural body health monitoring due to the advantages of long-range, noncontact, and multipoint monitoring. However, the imaging system is usually affected by many factors, such as distortion, blurring, and noise, which lead to displacement measurement errors after the degradation of the acquired image quality. Therefore, in this article, we propose a structural body image super-resolution network based on a dual transformer architecture to improve the clarity of the collected structural body vibration displacement image to better capture the vibration displacement information of the target. Meanwhile, we design a dual transformer block based on an encoder–decoder architecture for the characteristics of vision-based structural body vibration displacement measurement tasks to better extract structural body image details and edge feature information. In this module, we introduce two different transformers. In addition, modules based on the encoder–decoder architecture focus more on the input and output image information and often ignore the feature information in different layers. Therefore, we introduce an attention mechanism in the network and interact with the feature information in different layers of the encoder–decoder architecture to obtain a better structural body image super-resolution effect. After comparison tests with the rest of the latest and most classical networks as well as the current optimal networks, it is shown that our network obtains excellent image reconstruction results under different structural body vibration image datasets (SETs), which also provides a strong guarantee for the task of accurate vision-based structural body vibration displacement measurement.

Target-free 3D tiny structural vibration measurement based on deep learning and motion magnification

• A novel target-free tiny vibration displacement measurement approach is proposed. • It is based on deep learning and motion magnification techniques. • Binocular vision system is used for measuring 3D vibration measurement. • Deep learning techniques are used for key-points detection, matching and tracking. • Experimental and in-field studies are conducted to validate the accuracy. In the field of structural health monitoring (SHM), computer vision based methods have been usually developed for 2-dimensional (2D) vibration displacement measurement and crack identification of civil engineering structures. However, the accurate measurement of tiny 3-dimensional (3D) vibrations for real civil engineering structures remains a very difficult task. To overcome this challenge, this paper proposes a target-free full-field 3D tiny vibration measurement approach for civil engineering structures by using a binocular vision system. The proposed approach is based on deep learning and motion magnification. A phase-based video motion magnification algorithm is employed to achieve a high measurement accuracy of tiny vibrations at the submillimeter level. The advanced key point detection, matching and tracking algorithms via deep learning techniques are employed to achieve target-free tiny vibration displacement measurement. The accuracy and performance of the proposed approach are evaluated through experimental tests on a steel cantilever beam in the laboratory. In-field experimental tests are conducted on a pedestrian bridge on a university campus to investigate the accuracy of the proposed approach in practical applications. The measured 3D tiny vibration displacement from the proposed approach is compared with those measured by laser displacement sensors, and the derived acceleration responses are compared with those measured from the installed accelerometers on the testing structures. The results demonstrate that the 3D tiny vibration measurements are obtained accurately by the proposed approach.

Analyzing vibration parameters of a modern high-speed engine during operation

The paper presents results of experimental research into vibration parameters of a modern electronic control-enabled main high-speed engine, Caterpillar CAT 3516B as obtained during a running trial of a sea tug. CAT 3500 series Caterpillar high-speed engines are extensively used in sea tugs; there are about 40 units installed in vessels operating in the Azov and Black Sea region. The research technique involved measurement of vibration displacement, vibration velocity, vibration acceleration at an engine and its turbocharger components. The conducted experimental research has shown that in order to evaluate the technical state of an engine, it is necessary to measure vibration displacement and vibration velocity in a frequency range from 1 to 1000 Hz at 0.1 Hz intervals. For turbochargers, the vibration acceleration shall be measured in a frequency range from 1 to 15000 Hz at 1 Hz intervals. The results of the experimental research allowed determining necessary conditions for measurement of vibration parameters of Caterpillar CAT-3500 series modern high-speed engines commonly used in sea tugs. The experimental research into vessel installations by in-place diagnostics at operating sea-going vessels forms a foundation of an autonomous ship control system.

Computer vision based target-free 3D vibration displacement measurement of structures

Vibration displacement response is widely used in the field of structural health monitoring (SHM) to monitor the health condition of civil engineering structures. Traditionally, the vibration displacement response is acquired by physical sensors, such as linear variable differential transformers (LVDT) and laser displacement sensors (LDS), or more commonly by accelerometers to measure accelerations instead of directly measuring the displacement due to the fact that direct displacement measurement requires a fixed platform to install the sensors. Recently, to overcome the difficulties in direct displacement measurement, computer vision based methods have become a research hotspot. This paper proposes an advanced binocular vision system for target-free full-field three-dimensional (3D) vibration displacement measurement of civil engineering structures. The state-of-the-art key point detection and matching algorithm based on deep learning is employed to achieve target-free measurement, which greatly improves the quantity and quality of matching natural key points with low contrast. The performance of the proposed vision based approach for 3D vibration displacement measurement is evaluated through an experimental test on a steel cantilever beam in the laboratory. The obtained vibration displacement responses obtained from the proposed approach are compared with those measured by LVDTs and LDSs. The results demonstrate that the displacement responses obtained from the proposed vision based approach are accurate compared with the traditional sensors, while the proposed approach is more cost effective and much easier to achieve accurate 3D vibration displacement measurement.

Vision-based transverse vibration displacement measurement of hoisting vertical ropes in mine hoists

At present, there is no appropriate way to measure the transverse vibration response of moving hoisting vertical rope in hoist. Therefore, a vision-based measurement method combining the digital image correlation (DIC) and digital image processing (DIP) algorithms is proposed in this paper. In this method, a reference line perpendicular to the vertical ropes is added in image sequence by DIP algorithm to form some virtual cross targets, which makes the improved DIC algorithm with adaptive template updating (ATU) rule can track the moving hoisting vertical rope without any labels. Then for distinguishing all ropes in the measuring area, a displacement threshold is set to locate the current measured rope and exclude the other ropes. The transverse vibration displacements of the hoisting vertical rope in an actual mine hoist was measured in three background situations, verifying the feasibility of the proposed method. Moreover, in a laboratory artificial vibration test, the measurement results from the proposed vision method and a laser displacement sensor yielded a very good agreement. The two experimental results indicate that it is fairly reasonable and effective to measure the transverse vibration displacements of hoisting vertical ropes.