Detection Of Bolt Loosening Research Articles

A low-cost self-powered triboelectric nanogenerator sensor for detecting loosening bolt under impact loading

Purpose The purpose of this study is to detect the bolt loosening under conditions of impact loading with a low-cost self-powered triboelectric nanogenerator sensor. Design/methodology/approach In this work, an Al/PTFE-based triboelectric nanogenerator (AP-TENG) is used as a sensor. A pendulum impact device and a force hammer were used to apply the impact loads. The bolt status and the applied torque can be monitored under impact loading conditions by using the output voltage results of the AP-TENGs. Findings The output voltage results of the current AP-TENG sensor under five different bolt torques, i.e. from 0.5 to 2.5 N m, were measured. The measurements revealed that a thicker buffer layer significantly contributed to the generation of higher voltages. Besides, the AP-TENG was also used to light ten commercial green LEDs in series, and the brightness of the LEDs was high enough even for the daytime, which showed that it can be used as the alarm device. In addition, a sudden loose test was also carried out, and the obvious voltage spikes can be seen without the external impact. The force hammer impact tests have expanded the application scope of the AP-TENG in the bolt loosening detection. Originality/value The bolt loosening monitoring is important and useful for the safe operation. The application of TENG technology for detecting bolt loosening remains relatively unexplored. In addition, ten commercial green LEDs can be driven by the AP-TENG sensor, which can be used for the early warning of the bolted loosening status. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0216/

CSSF-BL360: Color Segmentation-Based Bolt Localization and 0~360° Full-Scale Loosening Angle Detection in Single-Frame Images

Introduction: Bolt loosening detection plays a crucial role in ensuring structural safety. Existing research using deep learning methods has the drawback of being applicable to limited scenarios and typically requires at least two frames of images for comparison in subsequent angle detection, with a limited range of detectable angles. Method: In this study, a color segmentation method was first used to separate the red square gasket placed between the bolt and the base, thereby locating the bolt area; then, the image was perspective-corrected using the four corners of the red square gasket; finally, the red mark bars on the bolt and base were separated using the same color segmentation method, and the geometric moments of the connected domains of the mark bars were processed, combined with vector processing techniques, to achieve quantified detection of bolt loosening angles within 0~360 degrees using a single frame image. In the verification experiments, 150 images were collected from different shooting angles and distances, and the bolt areas to be tested were located using the color segmentation method, all with an IOU (Intersection over Union) greater than 0.9317. Subsequent experiments set different variables, including different shooting distances, bolt loosening angles, perspective angles, lighting conditions, bolt types, and image rotation angles. Result: The results demonstrated that the method presented in this study could accurately detect bolt loosening angles in multiple scenarios. Conclusion: Hence, it is capable of measuring loosening angles within the range of 0~360 degrees using only a single frame image. result: In the verification experiments, 150 images were collected from different shooting angles and distances, and the bolt areas to be tested were located using the color segmentation method, all with an IOU(Intersection over Union) greater than 0.9317. Subsequent experiments set different variables, including different shooting distances, bolt loosening angles, perspective angles, lighting conditions, bolt types, and image rotation angles.

Early Bolt Loosening Detection Method Based on Digital Image Correlation.

Bolt loosening can significantly impact the accuracy, stability, and safety of equipment. The detection of bolt loosening in a timely manner is crucial for ensuring the safety, reliability, performance, and service life of equipment, structures, and systems. Various methods exist for detecting bolt loosening, such as strain gauges and ultrasonic waves. However, these technologies have some limitations that impede their widespread application. In this paper, for the high-pressure pipe manifolds that may experience leakage accidents due to the loosening of bolts, an early bolt loosening detection method based on digital image correlation is proposed. Initially, a model is established through tensile tests to relate the average strain on the side of the bolt head to the axial force. Subsequently, an industrial camera captures images of bolts with random speckles under operational conditions. Using digital image correlation technology, the average strain in a specific region on the side of the bolt head is calculated. By integrating the average strain into the established relationship model between the average strain and axial force, the axial force of the bolt under operational conditions can be predicted, enabling the early assessment of bolt loosening. The findings show that the average strain on the side of the bolt head increases proportionally with the axial force, indicating a strong linear relationship. This method enables accurate prediction of the bolt's axial force, offering a new approach for identifying the early loosening of bolts in high-pressure manifolds and monitoring structural health.

Guided wave based SHM for loosening detection in bolted lap joints with the application of Neural Networks

Bolted and riveted connections are a mainstay in many aerospace, mechanical, and civil engineering applications. Early detection of bolt loosening is essential for ensuring structural integrity and preventing catastrophic failures. Guided wave-based structural health monitoring (SHM) systems are well-suited for damage detection in plate-like structures. The present study aims at an experimental investigation of Lamb wave propagation in an aluminum lap joint with two bolts using piezoelectric transducers and compares the effectiveness of different damage indices based on wave energy transmission between plates in looseness detection. Torque values ranging from 5 Nm to 12 Nm at 1 Nm intervals are applied, along with a hand-tightened condition as well as free plates without bolts. Torquing conditions are evaluated for both bolted joints individually as well as simultaneously. The experiment is currently in progress, and the preliminary results are being examined. Further to this, the experimental findings will be compared with the finite element (FE) modelling results. Relatively few studies have explored the use of deep learning algorithms to enhance the performance of guided wave-based SHM systems for bolt loosening detection. The central goal of this study is to develop a neural network architecture for a Lamb wave propagation-based SHM system for bolted connections. This network will be trained using damage indices from the FE modelling results and later validated with the experimental data.

Image-Based Bolt-Loosening Detection Using a Checkerboard Perspective Correction Method.

In this paper, a new image-correction method for flange joint bolts is proposed. A checkerboard is arranged on the side of a flange node bolt, and the homography matrix can be estimated using more than four feature points, which include the checkerboard corner points. Then, the perspective distortion of the captured image and the deviation of the camera position angle are corrected using the estimated homography matrix. Due to the use of more feature points, the stability of homography matrix identification is effectively improved. Simultaneously, the influence of the number of feature points, camera lens distance, and light intensities are analyzed. Finally, based on a bolt image taken using an iPhone 12, the prototype structure of the flange joint in the laboratory is verified. The results show that the proposed method can effectively correct image distortion and camera position angle deviation. The use of more than four correction points not only effectively improves the stability of bolt image correction but also improves the stability and accuracy of bolt-loosening detection. The analysis of influencing factors shows that the proposed method is still effective when the number of checkerboard correction points is reduced to nine, and the average error of the bolt-loosening detection result is less than 1.5 degrees. Moreover, the recommended camera shooting distance range is 20 cm to 60 cm, and the method exhibits low sensitivity to light intensity.

Early bolt looseness monitoring using the leading waves energy in piezoelectric active sensing

Bolt monitoring plays a vital role in ensuring the safe operation of engineering structures. The utilization of piezoelectric (PZT) active sensing and analysis of ultrasonic energy transmitted through the interface of bolted connections has demonstrated high feasibility for monitoring bolt looseness. However, the ultrasonic energy saturation effect (i.e. the response signal energy changes slightly as the bolt preload variation) near the rated bolt preload restricts its applicability in early detection of bolt loosening. In this paper, for the energy saturation in the PZT active sensing method, a new bolt looseness indicator with the energy of leading waves (i.e. the first several wave packets) in the response signal is proposed for bolt loosening monitoring, especially for the early bolt loosening monitoring since the energy of the leading wave packets has the linear relationship with bolt preload. The experimental results show that the energy of the first several wave packets in the response signals can be as a looseness indicator of bolt preload. Within the entire range of bolt preload, the indicator exhibits a linear relationship with the bolt preload. Moreover, this method successfully resolves the challenge of energy saturation, providing an effective approach for monitoring bolt preload.

Image-based bolt-loosening detection using an improved homography-based perspective rectification method

Image-based bolt-loosening detection using an improved homography-based perspective rectification method

Non-destructive detection of high-strength wind turbine bolt looseness using digital image correlation

Looseness of high-strength wind turbine bolts is one of the main types of mechanical failure that threaten the quality and safety of wind turbines, and how to non-destructively detect bolt loosening is essential to accurate assessment of operational reliability of wind turbine structures. Therefore, to address the issue of looseness detection of high-strength wind turbine bolts, this paper proposes a non-destructive detection method based on digital image correlation (DIC). Firstly, the mathematical relationships between the inplane displacement component of the bolt’s nut surface, the bolt’s preload force loss and the bolt loosening angle are both deduced theoretically. Then, experimental measurements are respectively conducted with DIC with different small bolt loosening angles. The results show that the bolt loosening angle detection method based on DIC has a detection accuracy of over 95%, and the bolt’s preload force loss evaluated by the deduced relationship has a good agreement with the empirical value. Therefore, the proposed DIC-based bolt loosening angle detection method can meet the requirements of engineering inspection, and can achieve quantitative assessment of preload forces loss of wind turbine bolt.

Monitoring of bolt loosening using vibro-acoustic modulation (VAM)

Detection of bolt loosening using vibro-acoustic modulation (VAM) has been increasingly investigated in the past decade. However, conventional nonlinear coefficients, derived from theoretical analysis, are usually based on the assumption of ideal wave-surface interactions at the joint interfaces. Such coefficients show a poor correlation with the tightening torque when the joint is under the combined influences from structural and material nonlinearities. A reliable inspection method of residual bolt torque was proposed in this study using Support Vector Regression (SVR) with acoustic features from VAM. By considering material intrinsic nonlinearity (MIN) and dissipative nonlinearity (DN), responses of aluminum-aluminum and composite-composite bolted joints during the VAM test were accurately simulated. SVR were subsequently established based on the database built by a mixing of simulated and experimental nonlinear spectral features when the joints were inspected at different scenarios. The results show that the evaluation of residual torque using the SVR models driven by the acoustic nonlinear responses show a higher accuracy compared with the conventional nonlinear coefficients. Requiring limited experiment data, the proposed method can achieve reliable inspection of bolt torque by including the simulated data into machine training.

Bolt body axial stress detection based on nut axial stress

Accurate detection of bolt body axial stress (σb) is a significant means for bolt loosening judgment and bolt fatigue analysis in engineering structures. Strain gauges are widely used in the stress detection of engineering structures and process equipment because of their easy installation, compact structure, and applicability. This paper proposes a method to detect σb using strain gauges based on the nut axial stress. Through theoretical analysis and finite element simulation, it is found that there is a linear relationship between σb and nut surface axial stress (σnx), which is verified by experimental investigations. The results show that the relationship is linearly distributed under static load. However, under dynamic load, the relationship is a flat closed curve due to the relative hysteresis effect of the bolt and nut. The closed curve is less related to the excitation frequency. However, the slope of the curve tangent decreases with the increase of dynamic load, and the decrease gradually slows down. This method is applied to the discharge valve bolt (40CrMoNiA, 10.9) of a compressor in a chemical plant. The detection results show that there is a better linear relationship between σb and σnx. Numerical analysis to suggest nut stress detection positions based on stress signals and dispersion. In summary, the proposed method provides a new way to monitor the bolt connection status, detect bolt loosening, and overcome the short board that the bolt body is inside the machine and cannot directly realize stress measurement.

A Passive Wireless Acceleration Sensing System Based on Patch Antenna and FMCW Radar

Traditional acceleration sensors use cables to transport data and batteries to supply power, which may increase failure risk, deployment inconvenience and maintenance difficulty. This paper proposes a passive and wireless acceleration sensing system, namely a newly designed patch-antenna-based sensing node interrogated by frequency-modulated continuouswave (FMCW) radar. The acceleration is correlated with the location of a cantilever beam, whose oscillation alter the additional capacitance of the patch antenna and consequently the resonant frequencies. The testing range can be adjusted by change the material and dimensions of the cantilever beam, which is designed up to 1 110m/s2 with a sampling rate of 500Hz to meet the requirements of different conditions. The working mechanism of the sensing system is analyzed theoretically and then verified by the simulation in COMSOL Multiphysics. Then an experiment is carried out to evaluate the performance of the sensing system. Both the resonant frequency and remaining power can be utilized to extract the acceleration response, and results show a good fit with the actual response (i.e., an average error of 4.5%). Empowered by FMCW Radar, the passive accelerometer may have great potential in identifying structure strain mode, impedance-based detection of bolt loosening, automation in construction, etc.

A Support Vector Machine-Based Approach for Bolt Loosening Monitoring in Industrial Customized Vehicles.

Machine learning techniques have progressively emerged as important and reliable tools that, when combined with machine condition monitoring, can diagnose faults with even superior performance than other condition-based monitoring approaches. Furthermore, statistical or model-based approaches are often not applicable in industrial environments with a high degree of customization of equipment and machines. Structures such as bolted joints are a key part of the industry; therefore, monitoring their health is critical to maintaining structural integrity. Despite this, there has been little research on the detection of bolt loosening in rotating joints. In this study, vibration-based detection of bolt loosening in a rotating joint of a custom sewer cleaning vehicle transmission was performed using support vector machines (SVM). Different failures were analyzed for various vehicle operating conditions. Several classifiers were trained to evaluate the influence of the number and location of accelerometers used and to determine the best approach between specific models for each operating condition or a single model for all cases. The results showed that using a single SVM model with data from four accelerometers mounted both upstream and downstream of the bolted joint resulted in more reliable fault detection, with an overall accuracy of 92.4%.

3D vision-based bolt loosening assessment using photogrammetry, deep neural networks, and 3D point-cloud processing

Structural bolts are essential structural elements. Detection of structural bolt loosening is of great importance to provide earlier warnings of structural damages and prevent catastrophic system-level collapse. Most existing studies about bolt loosening assessment were built in 2D computer vision, where the assessment may be restricted based on the camera views. In this paper, a novel 3D vision-based methodology is proposed for autonomous bolt loosening assessment. First, a 3D point cloud of bolted connection is created using readily available 2D images. Second, a new convolutional neural network (CNN)-based method is developed to localize structural bolts in the 3D point cloud. Further, a 3D point cloud processing algorithm is developed to recognize and quantify bolt loosening. Parameter studies were conducted to investigate the effectiveness of the proposed pipeline. Finally, a real-world implementation has been conducted to quantify bolt loosening on a steel column base connection with bolts. The results indicate that the proposed bolt loosening assessment methodology can effectively localize and quantify bolt loosening at high accuracy and low cost.

Image-based bolt self-localization and bolt-loosening detection using deep learning and an improved homography-based prospective rectification method

The bolt loosening detection method has been paid attention by engineering and academic scholars. The presented methods only focus on the identification of the bolt based on deep learning, and the problem of bolt localization and the influence of shadow on distortion correction is seldom studied. In this paper, a bolt self-localization method based on YOLOv4 deep learning algorithm is proposed. A bolt numbering rule is established and the deep learning is introduced to identify the number and locate the bolt. A new square bolt gasket is proposed and four corner points are used for distortion correction. In order to reduce the influence of shadows, a grayscale enhancement strategy is proposed to improve the correction stability. Finally, a laboratory flange joint is used to verify the proposed method. The results show that the bolt self-localization method is feasible and the new bolt gasket can effectively improve the stability of distortion correction and bolt loosening detection.

Bolt Loosening Detection Using Key-Point Detection Enhanced by Synthetic Datasets

Machine vision based on deep learning is gaining more and more applications in structural health monitoring (SHM) due to the rich information that can be achieved in the images. Bolts are widely used in the connection of steel structures, and their loosening can compromise the safety of steel structures and lead to serious accidents. Therefore, this paper proposes a method for the automatic detection of the bolt loosening angle based on the latest key point detection technology using machine vision and deep learning. First, we built a virtual laboratory in Unreal Engine5 that could automatically label and generate synthetic datasets, and the datasets with bolts were collected. Second, the datasets were trained using the YOLOv7-pose framework, and the resulting model was able to accurately detect key points of bolts in images obtained under different angles and lighting conditions. Third, a bolt loosening angle calculation method was proposed according to the detected key points and the position relationship between neighboring bolts. Our results demonstrate that the proposed method is effective at detecting the bolt loosening angle and that the use of synthetic datasets significantly improves the efficiency of datasets establishment while also improving the performance of model training.

Artificial Intelligence (AI)-Based Evaluation of Bolt Loosening Using Vibro-Acoustic Modulation (VAM) Features from a Combination of Simulation and Experiments

The detection of bolt loosening using vibro-acoustic modulation (VAM) has been increasingly investigated in the past decade. However, conventional nonlinear coefficients, derived from theoretical analysis, are usually based on the assumption of ideal wave–surface interactions at the joint interfaces. Such coefficients show a poor correlation with the tightening torque when the joint is under the combined influences of structural and material nonlinearities. A reliable inspection method of residual bolt torque is proposed in this study using support vector regression (SVR) with acoustic features from VAM. By considering the material intrinsic nonlinearity (MIN) and dissipative nonlinearity (DN), the responses of aluminum–aluminum and composite–composite bolted joints during the VAM test were accurately simulated. The SVRs were subsequently established based on the database built by combining simulated and experimental nonlinear spectral features when the joints were inspected at different scenarios. The results show that the evaluation of residual torque using the SVR models driven by the acoustic nonlinear responses had higher accuracy compared to the conventional nonlinear coefficients. Requiring limited experimental data, the proposed method can achieve a reliable inspection of bolt torque by including the simulated data in the machine training.

Bolt-loosening detection using vision technique based on a gray gradient enhancement method

The bolt-loosening detection method using vision-based technique is summarized and a new gray gradient enhancement method is proposed to improve the stability of the nut edge detection and bolt-loosening detection. The Influence of the thresholds in Canny edge detector is studied and a new gray gradient enhancement method is proposed to enhance the gray gradient at the pixels on the outer boundary of the nut. Meanwhile, the suggestion on the high threshold value in Canny edge detector is given and can be used to reduce the subjectivity of parameter determination in Canny detector and the interference of uninterested edge lines. The proposed method is verified using a bolt connection in laboratory. The results show that the proposed method can effectively highlight the outer boundary of the nut and improve the stability of the nut edge detection and bolt-loosening detection.

Bolt-Loosening Detection Using 1D and 2D Input Data Based on Two-Stream Convolutional Neural Networks.

At present, the detection accuracy of bolt-loosening diagnoses is still not high. In order to improve the detection accuracy, this paper proposes a fault diagnosis model based on the TSCNN model, which can simultaneously extract fault features from vibration signals and time-frequency images and can precisely detect the bolt-loosening states. In this paper, the LeNet-5 network is improved by adjusting the size and number of the convolution kernels, introducing the dropout operation, and building a two-dimensional convolutional neural network (2DCNN) model. Combining the advantages of a one-dimensional convolutional neural network (1DCNN) with wide first-layer kernels to suppress high-frequency noise, a two-stream convolutional neural network (TSCNN) is proposed based on 1D and 2D input data. The proposed model uses raw vibration signals and time-frequency images as input and automatically extracts sensitive features and representative information. Finally, the effectiveness and superiority of the proposed approach are verified by practical experiments that are carried out on a machine tool guideway. The experimental results show that the proposed approach can effectively achieve end-to-end bolt-loosening fault diagnoses, with an average recognition accuracy of 99.58%. In addition, the method can easily achieve over 93% accuracy when the SNR is over 0 dB without any denoising preprocessing. The results show that the proposed approach not only achieves high classification accuracy but also has good noise immunity.

Temperature compensation to guided wave-based monitoring of bolt loosening using an attention-based multi-task network

Online monitoring of bolt torque is critical to ensure the safe operation of bolted structures. Guided waves have been intensively explored for bolt loosening monitoring. Nevertheless, guided waves are excessively sensitive to fluctuation of ambient temperature. As a result of the complexity of wave transmitting across a bolted joint, it is highly challenging to compensate for the effect of temperature. To this end, an attention-based multi-task network is developed towards accurate detection of bolt loosening in multi-bolt connections over a wide range of temperature variation. By integrating improved attention gate modules in a modified U-Net architecture, an attention U-Net is configured for temperature compensation. A two-layer convolutional subnetwork is connected in series behind the attention U-Net to identify bolt loosening. Experimental validation is carried out on a bolt jointed lap plate simulating a real aircraft structure. The results have proved that the developed multi-task network achieves temperature compensation and accurate bolt loosening identification. To further understand the multi-task network, the Integrated Gradients method and a simplified structure of the bolt lap plate are used to interpret the developed network. It is proved that the A0 mode is sensitive to bolt loosening, while the S0 mode is not.

Bolt Loosening Detection of Rocket Connection Structure Based on Variational Modal Decomposition and Support Vector Machines

This paper designed a bolt-loosening Support Vector Machines’ conduct detection method with feature vectors comprising eigenvalue decomposition based on Variational Modal Decomposition (VMD) and Singular Value Decomposition (SVD), combined with permutation entropy. Particle Swarm Optimization-Support Vector Machines (PSO-SVMs) are used for small-sample machine learning and can effectively identify and judge the state of bolt preload. The effectiveness of the proposed method is verified in a typical example of a connection structure under random-amplitude impulse loads and Gaussian white noise with different signal-to-noise ratios. The effect of other bolt numbers being arranged is also discussed in the results. This method’s bolt-loosening identification rate is close to 90% under both equal-amplitude and variable-amplitude loads. Following the interference, with a signal-to-noise ratio of 20 dB, the method also has a recognition rate higher than 70% under various working conditions and bolt equipment schemes. The effectiveness of the method was verified by experiments.