Displacement Signal Research Articles

Early detection and classification of internal leakage in boom actuator of mobile hydraulic machines using SVM

Mobile hydraulic machines are used in various operations like construction, material handling and mining. High powers to weight ratio and manoeuvrability in rough terrains are striking features that help in edging out their electrical and mechanical counterparts. Internal leakage in hydraulic actuators is a faulty condition frequently observed in hydraulic machines. Internal leakage in the actuator affects the system’s dynamic performance and decreases its energy efficiency. Also, internal leakage is apparent only when the leakage is extreme and the actuator stops responding to command signals. Thus, detecting internal leakage in its early stages is a difficult task. Early detection and corrective action save energy, reduce component degradation, and reduce machine downtime. There are many existing techniques for internal leakage detection of hydraulic actuators, but they are intended for actuators working in a laboratory environment. The main focus of this paper is to present a practical method for early detection of internal leakage fault present in boom actuator of mobile hydraulic machines by analysing the machine work-cycle data with minimum hardware. The method trains and validates a Support Vector Machine (SVM) classifier using pressure and boom angle displacement signals. The time-series signals are processed using ’event-based’ feature extraction method. The binary version of Particle Swarm Optimisation is used for feature selection. The trained classifier can detect and classify internal leakage faults with more than 95% accuracy, which is sufficient for taking appropriate preventive maintenance steps on time.

Measurement Capability of Laser Ultrasonic Inspection System for Evaluation of Ball-Grid Array Package Solder Balls

Abstract Laser ultrasonic inspection is a novel, noncontact, and nondestructive technique to evaluate the quality of solder interconnections in microelectronic packages. In this technique, identification of defects or failures in solder interconnections is performed by comparing the out-of-plane displacement signals, which are produced from the propagation of ultrasonic waves, from a known good reference sample and sample under test. The laboratory-scale dual-fiber array laser ultrasonic inspection system has successfully demonstrated identifying the defects and failures in the solder interconnections in advanced microelectronic packages such as chip-scale packages, plastic ball grid array packages, and flip-chip ball grid array packages. However, the success of any metrology system depends upon precise and accurate data to be useful in the microelectronic industry. This paper has demonstrated the measurement capability of the dual-fiber array laser ultrasonic inspection system using gage repeatability and reproducibility analysis. Industrial flip-chip ball grid array packages have been used for conducting experiments using the laser ultrasonic inspection system and the inspection data are used to perform repeatability and reproducibility analysis. Gage repeatability and reproducibility studies have also been used to choose a known good reference sample for comparing the samples under test.

An S-band zero-IF SFCW through-the-wall radar for range, respiration rate, and DOA estimation

This study presents a Zero-IF structured Stepped Frequency Continuous Wave (SFCW) radar system for range, respiration rate, and DOA estimation of the targets behind the walls. The radar hardware is a combination of SFCW and Uniform Linear Array (ULA) techniques. Four main improvements are done in signal processing. Averaging Delay Line Cancellation (ADLC) is proposed for moving-target indication. As a post-processing solution to the disruptive effects, a self-acting SVD-based algorithm is offered. The displacement signals of the targets are obtained after applying an additional phase extraction and wavelet-based filtering to lessen the phase discontinuities and noises. The Direction of Arrival (DOA) is estimated over the positive and negative range profiles of two separate antenna pairs. The median of these four angle values is used as the final DOA estimation. The performance of the system is tested with more than 150 experiments and compared with state-of-the-art studies.

Experimental study on vertical vibration characteristics of medium-low speed maglev vehicle when standing still on steel beams

Levitation stability is the very basis for the dynamic operation of Electromagnetic Suspension (EMS) medium-low speed maglev trains (MSMT). However, self-excited vibration tends to occur when the vehicle is standing still above the lightweight lines, which remains a major constraint to the promotion of medium-low speed maglev technology. In order to study the vertical vibration characteristics of the coupled system of MSMT when it is standing still above lightweight lines, levitation tests were carried out on two types of steel beams: steel beam and active girder of the turnout, with a newly developed maglev vehicle using levitation frames with mid-set air spring. Firstly, modal tests were carried out on the steel beam to determine its natural vibration characteristics; secondly, the acceleration signals and the dynamic displacement signals of the air spring obtained at each measurement point were analyzed in detail in both the time and frequency domains, and the vertical ride comfort was assessed by means of the calculated Sperling index. Subsequently, theoretical explanations were given for the occurrence of self-excited vibration of coupled system from the perspective of the vehicle-to-guideway vibration energy input. Results show that the eigen frequencies of the vehicle on the steel beam and the turnout are 9.65 Hz and 2.15 Hz, respectively, the former being close to the natural frequency of the steel beam while the latter being close to the natural frequency of the air spring suspension system, thus causing the self-excited vibration of the coupled system. It is recommended to either avoid the main eigen frequencies of the coupled system or to increase the damping of the corresponding vibration modes to guarantee a reliable coupled system for its long-term performance. These results may provide valuable references for the optimal design of medium-low speed maglev systems.

Structural vibration mode identification from high-speed camera footages using an adaptive spatial filtering approach

As a non-contact and full-field testing method, high-speed camera-based modal analysis has become a feasible and acknowledged approach. However, extracting small displacements from noisy images has experienced high level of difficulty, especially in high frequency range. This paper proposes a novel adaptive spatial filtering (beamforming) algorithm to extract the displacement signals using high-speed camera. In the proposed algorithm, one pixel is considered as a sensor measuring displacement and a set of pixels are therefore taken as the elements of sensor array. Then, an adaptive spatial filtering acting on this sensor array is proposed. The proposed approach mainly includes three steps. Firstly, a set of pixels are selected to compose a sensor array according to signal to distortion and noise (SINAD). Secondly, a node/antinode searching scheme is proposed based on sinusoid-based piecewise functions, which works as an adaptive filter to match mode shape and enhance modal displacement. Finally, the output of spatial filtering is adopted as the system response for the identification of modal parameters. To validate the performance of the proposed method, simulation and experiment studies are conducted based on measuring the vibration model properties of a free-free beam, which includes a comparison with LK optical flow method and conventional accelerometer-based method. The results show that the SNR of estimated displacement and computational efficiency is significantly improved without using additional sensors. The proposed method paves a way for broadening the applications of using high-speed camera for full-field vibration measurements.

Measurement and identification of the nonlinear dynamics of a jointed structure using full-field data; Part II - Nonlinear system identification

The dynamic responses of assembled structures are greatly affected by the mechanical joints, which are often the cause of nonlinear behavior. To better understand and, in the future, tailor the nonlinearities, accurate methods are needed to characterize the dynamic properties of jointed structures. In this paper, the nonlinear characteristics of a jointed beam is studied with the help of multiple identification methods, including the Hilbert Transform method, Peak Finding and Fitting method, Dynamic Mode Decomposition method, State-Space Spectral Submanifold, and Wavelet-Bounded Empirical Mode Decomposition method. The nonlinearities are identified by the responses that are measured via accelerometers in a series of experiments that consist of hammer testing, shaker ringdown testing, and response/force-control stepped sine testing. In addition to accelerometers, two high-speed cameras are used to capture the motion of the whole structure during the shaker ringdown testing. Digital Image Correlation (DIC) is then adopted to obtain the displacement responses and used to determine the mode shapes of the jointed beam. The accuracy of the DIC data is validated by the comparison between the identification results of acceleration and displacement signals. As enabled by full-field data, the energy-dependent characteristics of the structure are also presented. The setup of the different experiments is described in detail in Part I (Chen et al., 2021) of this research. The focus of this paper is to compare nonlinear system identification methods applied to different measurement techniques and to exploit the use of high spatial resolution data.

Ratiometric Fluorescence Imaging of Intracellular MicroRNA with NIR-Assisted Signal Amplification by a Ru-SiO2@Polydopamine Nanoplatform.

Accurate and sensitive fluorescence imaging of intracellular miRNA is essential for understanding the mechanism underlying some physiological and pathological events, as well as the prevention and diagnosis of diseases. Herein, a highly sensitive ratiometric fluorescent nanoprobe for intracellular miRNA imaging was fabricated by integrating a Ru-SiO2@polydopamine (Ru-SiO2@PDA) nanoplatform with a near-infrared light (NIR)-assisted DNA strand displacement signal amplification strategy. The Ru-SiO2@PDA spheres have excellent biosafety, high photothermal effect, and unique photophysical properties that can both emit a stable red fluorescence and well quench the fluorophores getting closer to them. So, when the fuel DNA and carboxyfluorescein (FAM)-labeled signal DNA are co-assembled on their outer surfaces, the FAM's green fluorescence is quenched, and a low ratiometric signal is obtained. However, in the presence of miRNA, the target displaces the signal DNA from the capture DNA, releasing the signal DNA far away from the Ru-SiO2@PDA. Then, the green fluorescence recovers and leads to an enhanced Igreen/Ired value. Under NIR light irradiation, the Ru-SiO2@PDA increases the local temperature around the probe and triggers the release of fuel DNA, which thus recycles the target miRNA and effectively amplifies the ratiometric signal. Using A549 cells as a model, the nanoprobe realizes the highly sensitive ratiometric fluorescence imaging of miRNA let-7a, as well as its in vivo up- and down-regulation expressions. It provides a facile tool for highly sensitive and accurate intracellular miRNA detection through one-step incubation and may pave a new avenue for single-cell analysis.

Weld-Quality Prediction Algorithm Based on Multiple Models Using Process Signals in Resistance Spot Welding

An efficient nondestructive testing method of resistance spot weld quality is essential in evaluating the weld quality of all welded joints in the automotive components of a car body production line. This study proposes a quality prediction algorithm for resistance spot welding that can predict the geometrical and physical properties of a spot-welded joint and evaluate weld quality based on quality acceptance criteria. To this end, four statistical models that predict the main geometrical and physical properties of a spot-welded joint, including tensile shear strength, indentation depth, expulsion occurrence, and failure mode, were estimated based on material information, dynamic resistance, and electrode displacement signals. The significance of the estimated models was then verified through an analysis of variance. The prediction accuracies of the models were 94.3%, 93.4%, 97.5%, and 85.0% for the tensile shear strength, indentation depth, expulsion occurrence, and failure modes, respectively. A weld quality evaluation methodology that can predict the properties of a spot-welded joint and evaluate the overall quality requirements based on authorized welding standards was proposed using the four statistical models.

Observation of Surface Displacement Associated with Rapid Urbanization and Land Creation in Lanzhou, Loess Plateau of China with Sentinel-1 SAR Imagery

Lanzhou is one of the cities with the higher number of civil engineering projects for mountain excavation and city construction (MECC) on the China’s Loess Plateau. As a result, the city is suffering from severe surface displacement, which is posing an increasing threat to the safety of the buildings. However, up to date, there is no comprehensive and high-precision displacement map to characterize the spatiotemporal surface displacement patterns in the city of Lanzhou. In this study, satellite-based observations, including optical remote sensing and synthetic aperture radar (SAR) sensing, were jointly used to characterize the landscape and topography changes in Lanzhou between 1997 and 2020 and investigate the spatiotemporal patterns of the surface displacement associated with the large-scale MECC projects from 2015 December to March 2021. First, we retrieved the landscape changes in Lanzhou during the last 23 years using multi-temporal optical remote sensing images. Results illustrate that the landscape in local areas of Lanzhou has been dramatically changed as a result of the large-scale MECC projects and rapid urbanization. Then, we optimized the ordinary time series InSAR processing procedure by a “dynamic estimation of digital elevation model (DEM) errors” step added before displacement inversion to avoid the false displacement signals caused by DEM errors. The DEM errors and the high-precision surface displacement maps between December 2015 and March 2021 were calculated with 124 ascending and 122 descending Sentinel-1 SAR images. By combining estimated DEM errors and optical images, we detected and mapped historical MECC areas in the study area since 2000, retrieved the excavated and filling areas of the MECC projects, and evaluated their areas and volumes as well as the thickness of the filling loess. Results demonstrated that the area and volume of the excavated regions were basically equal to that of the filling regions, and the maximum thickness of the filling loess was greater than 90 m. Significant non-uniform surface displacements were observed in the filling regions of the MECC projects, with the maximum cumulative displacement lower than −40 cm. 2D displacement results revealed that surface displacement associated with the MECC project was dominated by settlements. From the correlation analysis between the displacement and the filling thickness, we found that the displacement magnitude was positively correlated with the thickness of the filling loess. This finding indicated that the compaction and consolidation process of the filling loess largely dominated the surface displacement. Our findings are of paramount importance for the urban planning and construction on the Loess Plateau region in which large-scale MECC projects are being developed.

마커 및 필터 설계를 통한 다양한 조도 조건에서의 영상 위상기반 2차원 변위 측정 기법

The advanced displacement measurement technique using computer vision has shown several advantages, such as a high spatial resolution and no mass loading effect, compared to the conventional contact-sensing techniques. However, the accuracy and robustness of vision-based techniques are subjected to various illumination conditions, including uneven illumination and insufficient lighting. This study introduced an accurate displacement measurement technique, with high robustness with respect to the illumination changes, that used two complex Gabor filters and a specially designed marker with orthogonal features. The parameters of the two complex Gabor filters, including the center frequency, bandwidth, and direction, were selected based on the marker size and attitude to extract the accurate phase motion information on two directions with dense features. The noise caused by insufficient light and/or uneven illumination could also be reduced by emphasizing the orthogonal features using the proper filter parameters. Further, motion phase-based optical flow was applied to calculate the 2D displacement signal of the marker. The excellent performance of displacement measurement was confirmed by comparing to that by a laser doppler vibrometer(LDV) under various lighting conditions. Tracking of multiple markers on a plate was performed to demonstrate the capability of measuring the out-plane motion using the proposed technique.

Ground displacements revealed by A-DInSAR analysis in the Germano iron mining complex before and after the Fundão Dam collapse using Sentinel-1 data

Advanced differential SAR interferometry (A-DInSAR) has been used as an effective monitoring tool in mining environments. This work presents an investigation of surface displacement in the Germano iron mining complex (Mariana, Minas Gerais, Brazil), where on November 5, 2015, the Fundão tailings dam collapsed. Initially, the analysis was performed with 14 Sentinel-1 images, acquired from May 8, 2015, to November 4, 2015, and focused on the Fundão dam before its rupture to detect prior signs of surface deformations. The analysis after the Fundão dam collapse was conducted in the Germano area using 43 Sentinel-1 images, acquired from November 16, 2015 to May 15, 2017, and aimed at detecting surface displacement on the remaining infrastructure, the Sela&Tulipa and Selinha dikes and the main Germano tailings dam, which were directly or indirectly affected by the accident. The results reveal precursor signals of surface displacement at the Fundão dam prior to rupture, particularly on the left abutment setback sector where the collapse started according to a witness, reaching accumulated values of up to −60 mm. The results provided by the A-DInSAR analysis after the dam collapse indicate a stability in the Sela&Tulipa and Selinha dikes as well as in the main Germano dam, which is corroborated by in situ topographic measurements. On the auxiliary dikes within the Germano reservoir, accumulated displacement of up to −90 mm caused by settlements and continuous traffic of vehicles was detected. In addition, the detected accumulate displacement within the reservoir showed values of up to −120 mm, in general caused by settlements of the waste dry material due to the inactivity of the mining process. The investigation emphasized the important role that satellite-based A-DInSAR can play in mitigating possible accidents and for operational planning and risk assessment in mining operations.

Reconstruction of cutting forces through fusion of accelerometer and spindle current signals

Analysis of cutting forces is critical for understanding, control and optimization of machining operations. Table-dynamometers are the most common tools to measure the cutting forces directly. Workpiece geometry restrictions, high cost, altered dynamics, and limited frequency bandwidth are challenges associated with the direct measurement of the cutting forces using table-dynamometers. In this study, utilization of cost-effective spindle current sensors along with accelerometers is proposed to indirectly predict the cutting forces at the tool tip during milling operations. First, receptance coupling (RC) method is used to obtain dynamics between the tool tip and the accelerometers which are located on the spindle housing. The application of the RC method requires a series of impact hammer tests along with finite element simulations to identify the dynamics of machine tool structure. The measured accelerations are then integrated in frequency domain to obtain displacement of the spindle housing where the accelerometers are attached. A Kalman filter is then designed and applied to the displacement signals to reconstruct the AC component of the cutting forces. Next, the spindle current signals measured through the hall-effect sensors are processed to acquire the tangential and radial cutting forces. Finally, due to different frequency bandwidth of the measured signals through the accelerometers and hall-effect sensors, the reconstructed cutting forces are fused to improve the cutting force measurement accuracy. The proposed method is also verified by conducting a series of half and full-immersion milling tests, and the reconstructed results are compared to the measurements of a reference piezoelectric-based table-dynamometer. Results show that the proposed sensing scheme can predict the cutting forces effectively only at a fraction of the cost of traditional table-dynamometers without affecting overall workpiece geometries.

A Strain‐Model Based InSAR Time Series Method and Its Application to The Geysers Geothermal Field, California

AbstractA new time series interferometric synthetic aperture radar method is presented based on a strain model. Traditionally, the interferometric phases are optimized based only on a constant set of statistically homogeneous points throughout the observing period, and thus the decorrelated phase cannot be well recovered and possible incoherent (or coherent) phases are (are not) used to optimize the target phase. In the proposed method, a strain model, representing the geophysical relationship of spatially adjacent points' deformations, is employed to optimize the interferometric phases interferogram‐by‐interferogram. The unwrapped phase time series can be estimated from the wrapped interferograms based on the linear iterative weighted least squares method, which is simple and can effectively suppress the decorrelation noise. Both simulations and analysis of data over The Geysers geothermal field (TG), California are conducted, validating that the proposed method can reduce the decorrelations and simultaneously preserve the deformation signals. The displacement results of TG from 2007–2011 L‐band ascending ALOS‐1 data and 2015–2020 C‐band ascending/descending Sentinel‐1 data reveal that (a) the displacement rate of TG is decreasing from 2007–2011 to 2015–2020, and (b) temporally variable displacement signals in addition to steady displacement can be observed from 2015 to 2020 for both the east‐west and the vertical components.

Improving the Resolving Power of InSAR for Earthquakes Using Time Series: A Case Study in Iran

AbstractInterferometric Synthetic Aperture Radar (InSAR) is an established method to measure earthquake surface displacements. However, due to decorrelation and atmospheric noise, only a certain fraction of earthquakes is readily observable with single interferograms. To enhance the potential of retrieving InSAR earthquake observations, we apply InSAR time series analysis and use several recent earthquakes (Mw 5.6–6.3, 2018–2019) in Iran as case studies. We find that the coseismic displacement signals of these earthquakes, which might not be discernible within single interferograms, are better resolved using our approach. We reconstruct the coseismic deformation fields by fitting surface displacements using a time series approach. We find that the reconstructed coseismic deformation fields yield more robust and seismologically consistent earthquake modeling results when compared to single coseismic interferograms. Our work suggests that a time series approach is an effective way to improve the resolving power of InSAR for earthquake studies.

A novel transient balancing technology of the rotor system based on multi modal analysis and feature points selection

In this paper, a novel transient balancing technology for identifying the unbalance parameters of the rotor system is proposed. Through basic coordinate transformation and multi modal analysis of the dynamical equations of rotor system, the calculation method for transient excitation forces is proposed. The transient excitation forces are calculated by the transient responses and modal parameters at several special positions of the rotor model. Then the “feature points” are introduced in the identification procedure and the unbalance parameters are identified by selecting the “feature points” of the calculated excitation forces. The accuracy of the proposed method is demonstrated by numerical simulations and experiments of the rotor system. Identified results of the proposed method are presented for different unbalance parameters and rotor models. Effects of different random noise levels to the displacement signals, varieties in damping and support stiffness are also presented to verify the adaptability and efficiency of the proposed method. The results show that the unbalance parameters estimations are robust with respect to the above influence factors.

Vertical Ground Reaction Force Estimation From Benchmark Nonstationary Kinematic Data.

Time-differentiating kinematic signals from optical motion capture amplifies the inherent noise content of those signals. Commonly, biomechanists address this problem by applying a Butterworth filter with the same cutoff frequency to all noisy displacement signals prior to differentiation. Nonstationary signals, those with time-varying frequency content, are widespread in biomechanics (eg,those containing an impact) and may necessitate a different filtering approach. A recently introduced signal filtering approach wherein signals are divided into sections based on their energy content and then Butterworth filtered with section-specific cutoff frequencies improved second derivative estimates in a nonstationary kinematic signal. Utilizing this signal-section filtering approach for estimating running vertical ground reaction forces saw more of the signal's high-frequency content surrounding heel strike maintained without allowing inappropriate amounts of noise contamination in the remainder of the signal. Thus, this signal-section filtering approach resulted in superior estimates of vertical ground reaction forces compared with approaches that either used the same filter cutoff frequency across the entirety of each signal or across the entirety of all signals. Filtering kinematic signals using this signal-section filtering approach is useful in processing data from tasks containing an impact when accurate signal second derivative estimation is of interest.

Analysis of Groundwater Depletion/Inflation and Freeze–Thaw Cycles in the Northern Urumqi Region with the SBAS Technique and an Adjusted Network of Interferograms

This work investigated the large-scale ground deformations threatening the Northern Urumqi district, China, which are connected to groundwater exploitation and the seasonal freeze–thaw cycles that characterize this frozen region. Ground deformations can be well captured by satellite data using a multi-temporal interferometric synthetic aperture radar (Mt-InSAR) approach. The accuracy of the achievable ground deformation products (e.g., mean displacement time series and related ground displacement time series) critically depends on the number and quality of the selected interferograms. This paper presents a straightforward interferogram selection algorithm that can be applied to identify an optimal network of small baseline (SB) interferograms. The selected SB interferograms are then used to produce ground deformation products using the well-known small baseline subset (SBAS) Mt-InSAR algorithm. The developed interferogram selection algorithm (ISA) permits the selection of the group of SB data pairs that minimize the relative error of the mean ground deformation velocity. Experiments were carried out using a group of 102 Sentinel-1B SAR data collected from 12 April 2017 to 29 October 2020. This research study shows that the investigated farmland region is characterized by a maximum ground deformation rate of about 120 mm/year. Periodic groundwater overexploitation, coupled with irrigation and freeze–thaw phases, is also responsible for seasonal (one-year) ground displacement signals, with oscillation amplitudes up to 120 mm in the zones of maximum displacement.

Predicting the dynamic behaviour of the turning tool vibrations using an experimental measurement, numerical simulation and analytical modelling for comparative study

The aim in the present work is studying and predicting the dynamic response of the cutting tool in turning processes through the calculation of the tool tip displacement by an experimental measurement, numerical simulation and analytical modelling study. The first study is based on an experimental measurement method in which the effect of cutting conditions (ap, f and Vc) on the measured cutting force components (Fx, Fy, Fz) and tangential tool tip accelerations are evaluated. Moreover, the study is extended to the numerical simulations based on the finite element method (FEM) utilizing I-deas software to compute the tool tip displacements. For this, the turning tool is designed by two CAD models, such as a cantilever beam and as an approached real tool geometry, both punctually excited at its free end by the measured cutting forces to compute tool acceleration and tool displacement signals. In the following, the analytical modelling formulates the cutting tool in transverse vibrations as a thick cantilever beam based on the Bernoulli beam theory, where the modified effect of rotary inertia term is integrated into the equations of movements in the tangential and axial direction of the cut. However, in the radial direction, the cutting tool is excited axially under longitudinal vibrations. The governing differential equations of movements are resolved using the modal decomposition method and the convolution theorem, taking as impulsive excitations the cutting forces (Fx, Fy, Fz). The general solution corresponding to Duhamel’s integral is evaluated numerically to calculate the tool displacements. For validations, different comparisons are performed, and good agreements are found between all types of results. Thereafter, the presented methodology of cutting tool vibrations contribute to solve some important metal machining troubles, such as chatter machining that affects directly the quality of the surface of parts and reducing the intensity of cutting edge wear rate by monitoring the excessive cutting forces for a long tool life.

New Eddy Current Probe for Vibration Signal Suppression

This paper proposes a new probe for suppressing the vibration component from the detection signal in eddy current testing. The work is motivated by the desire to develop a new inspection method that can suppress the effects of vibration generated in the manufacturing process of metal parts such as tubes, rods and wires in order to make the current manufacturing process faster. The novelty of the probe lies in its simultaneous use of self and mutual inductions. The proposed probe uses the different effects of the self and mutual inductions to suppress the vibration component. This paper simplifies the discussion by limiting the dynamic vibration of the test specimen to static displacement. The coil distance between the excitation and detection coils should be adjusted for each test specimen for the most effective suppression of the displacement signal. The simulation and measurement results show that the coil distance in the mutual induction probe can control the phase of the displacement signal. It was confirmed that the amplitude of displacement signals measured by the proposed probe was under 6 % of the amplitude measured by the self-induction probe when the optimal coil distance was used. The results obtained suggest that the proposed probe can suppress the vibration component.

Detection of Bolt Looseness Based on Average Autocorrelation Function

Bolted connections are widely used in multiple engineering fields including aerospace and mechanical engineering due to their numerous advantages like the ability to bear relatively heavy loads, low costs, easy installation, and implementation. Bolt looseness may lead to costly disasters in industries and some cases of injuries. A new method for bolt looseness detection based on average autocorrelation function is proposed. It does not usually directly extract the looseness damage feature from the original response signal to detect the bolt looseness, but it uses the average autocorrelation function value at time lag T = 0 of the vibration pixel displacement signal to establish the looseness damage index. In terms of structural arrangement of this paper, firstly, the theoretical background of the proposed method is given. Then, an experimental system for bolt looseness detection based on computer vision is designed, and a verification experiment is carried out with the bolted connection plate as the experimental object. The results show that the proposed method can effectively obtain the location of the looseness damage of the bolted connection plate, which provides a new technical reference for the online monitoring of the looseness damage of bolted connection plate.