Loosening Detection Research Articles

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.

An evaluation of the diagnostic performance of the triphasic bone scintigraphy in patients suspected of aseptic total knee arthroplasty loosening

BackgroundTotal Knee Arthroplasty (TKA) is a widely conducted and successful orthopedic procedure. However, aseptic loosening, a common cause of TKA failure, necessitates revision surgery. Diagnostic accuracy of triphasic bone scintigraphy, a common imaging modality for aseptic loosening detection, remains controversial. This study investigated the diagnostic accuracy of bone scintigraphy when separately evaluated by a nuclear physicist and an orthopedic surgeon, and the interrater reliability between the two. MethodsPatients undergoing knee revision surgery due to suspected aseptic loosening at three medical centers from 2006 to 2023 were included. Relevant demographic, clinical, and procedural data were extracted from the records. The bone scintigraphy results as noted by the nuclear physicist and orthopedic surgeon were used as index test and intraoperative findings of loosening were used as reference tests. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated, and kappa's agreement was assessed. ResultsOut of 611 revision TKAs, 59 cases were analyzed. The nuclear physicist's evaluation of bone scintigraphy had a sensitivity of 73 %, specificity of 0 %, positive predictive value of 93 %, negative predictive value of 0 %, and diagnostic accuracy of 69 %. The orthopedic surgeon's evaluation showed higher sensitivity, specificity, positive and negative predictive values, and an accuracy of 84 %. Agreement levels were moderate (kappa = 0.46) between the nuclear physicist's and orthopedic surgeons evaluation. InterpretationThe diagnostic accuracy of bone scintigraphy for aseptic loosening is 84 % when evaluated by the orthopedic surgeon compared to 69 % for the nuclear physicist's evaluation. Kappa's agreement between the two was moderate.

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 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.

Multimodal loosening detection for threaded fasteners based on multiscale cross fuzzy entropy

In various mechanical systems, threaded fasteners are widely used to connect two or more separated components. Loosening in threaded fasteners is prone to occur due to the exposure of vibration environment for time. Regular loosening detection cannot be overemphasized. Traditional single-modal loosening detection method easily generates insufficient feature representation due to the limitation of information. Thus, the detection accuracy and reliability are decreased. This study is the first attempt to conduct multimodal loosening detection exploiting ultrasonic and audio response signals simultaneously. A novel loosening detection method is proposed making use of the complementarity of multimodal signals. In the method, the concept of multiscale cross fuzzy entropy (MCFE) is proposed, and the multimodal information is mapped into a unified feature space to construct more representative and effective loosening features. Linear discriminant analysis method is applied to remove redundant features and a random tree is used to detect loosening severities of threaded fasteners. The detection performances are both excellent in the applications of two different types of threaded fasteners (i.e., lap joint and globe-cone joint), which validates that our proposed multimodal loosening detection method shows great application potentials in industry. In addition, it demonstrates that our proposed method outperforms other loosening detection methods and MCFE shows great advantages in extracting representative loosening features.

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.

A bolt loosening detection method based on patch antenna with overlapping sub-patch

Bolts are widely used in civil engineering, and the detection of bolt loosening is of great significance to ensure the safety of a structure. This paper introduces a new method for detecting bolt loosening using a customized detachable strain sensor based on a patch antenna. A patch antenna with overlapping sub-patch is proposed to measure the longitudinal elongation of the entire bolt shaft, indicating the loosening state of the bolt. When the bolt is fastened, the elongation of the bolt under tension will change the combined length of the underlying patch and the radiation sub-patch, consequently increasing or decreasing the resonant frequency of the antenna. The resonant frequency of the antenna can be measured by the vector network analyzer. Furthermore, with wireless interrogation of the strain sensor based on the patch antenna, the proposed method can also be used in the wireless detection of bolt loosening. The authors conducted a finite element analysis of the bolt and the electromagnetic simulations of the antenna. They designed the detection sensor and conducted a series of experimental tests to demonstrate how a bolt under different applied preloads can be effective and feasible under the proposed method.

Proof-of-concept study of high-order sideband for bolt loosening detection using vibroacoustic modulation method

The significance of continuous loosening detection for bolted joints in engineering structures cannot be overemphasized. Vibroacoustic modulation (VAM) is a popular loosening detection method exploiting nonlinear features. Existing left and right sidebands can only reflect partial contact nonlinearity caused by loose bolted joint. In this paper, we proposed the concept of high-order sideband and established the theoretical model of high-order sideband for the first time. Multi-order sidebands were regarded to contain more information of contact nonlinearity and a novel loosening index (HSLI) integrating the first four high-order sidebands was developed. The VAM experiment was conducted to clearly display multi-order sidebands in the response spectrum for the concept validation and the good universality of high-order sideband was also proved by changing the material and roughness of clamped plates. Moreover, the experiment also found that the HSLI decreased almost linearly with the increase of tightening torques, demonstrating the excellent loosening detection performance compared with the existing nonlinear and linear loosening indexes. Taking a step further, the effects of input voltages of high-frequency and high-frequency excitations on high-order sidebands were discussed in the experiment. The proposed high-order sideband and nonlinear loosening index provide a reliable method of continuous evaluation of bolt loosening, as well as other contact-type damage, such as crack, debonding and bond-slip.

Quantitative loosening detection of threaded fasteners using vision-based deep learning and geometric imaging theory

With the development of signal processing and deep learning, vision-based methods for loosening detection have progressed in recent years. However, existing visual inspection methods are mainly used to measure the loosening angle, which requires prior knowledge of the initial tightening states, and is only applicable to hexagon bolts and nuts with clear edges. This paper proposes a method to quantitatively calculate the length of the exposed bolt for detecting loosening using vision-based deep learning and geometric imaging theory. The newly proposed method consists of three modules: the region of interest (RoI) location, keypoint detection, and length calculation modules. The RoI location module applies a faster regional convolutional neural network (Faster-RCNN)-based deep learning algorithm to locate the exposed bolt, whereas the keypoint detection module employs a cascaded pyramid network (CPN)-based deep learning algorithm to detect five keypoints on the exposed bolt. An accurate expression is derived from the geometric imaging theory, and the coordinates of the five keypoints and camera parameters are imported into the expression to calculate the length of the exposed bolt. In the experiment, the resolution of input image was 640 × 640 pixels, and the test speed of the proposed method was approximately 200 milliseconds per image. Experimental results showed an average measurement error of 0.61 mm using our method, outperforming other measurement methods and the state-of-the-art networks of human pose estimation. Moreover, the robustness of the proposed method was validated by varying the shooting conditions, bolt sizes and colours. Finally, some factors influencing detection and measurement accuracies are discussed, and prospects for further research are proposed.

A Modified Time Reversal Method for Guided Wave Detection of Bolt Loosening in Simulated Thermal Protection System Panels

In this work, a modified time reversal method is proposed for guided wave detection and localizing loosened bolt in a complicated multibolt‐jointed structure. Different from the traditional time reversal guided wave method, the response signal due to a tone burst input received at the healthy state is time reversed and recorded as a standard reemitting signal. In the detection process, this recorded standard signal is used for all damage cases to yield time reversal‐focalized reconstruction signals. This largely improves the sensitivity of the focalized signal to damage state. In this paper, the peak amplitude of the focalized wave packet in the reconstructed signal is calculated and utilized as tightness index. By bonding PZT transducers at different joint locations inside the structure, multiple tightness indices, where each tightness index presents the correlation between the current joint condition to its healthy condition at the joint, can be obtained. To analyze a large number of tightness indices, a principle component analysis method is introduced, and a neural network‐based loosening detection method is proposed. The proposed method is experimentally validated in a simulated double‐layer bolt‐jointed thermal protection system panel. Experimental results illustrate that the proposed method is effective to identify and localized the bolt loosening in complicated multibolt‐jointed structure. The detection and identification of the location of multibolt loosening is realized.

Non-invasive vibrometry-based diagnostic detection of acetabular cup loosening in total hip replacement (THR)

Total hip replacement is aimed at relieving pain and restoring function. Currently, imaging techniques are primarily used as a clinical diagnosis and follow-up method. However, these are unreliable for detecting early loosening, and this has led to the proposal of novel techniques such as vibrometry. The present study had two aims, namely, the validation of the outcomes of a previous work related to loosening detection, and the provision of a more realistic anatomical representation of the clinical scenario. The acetabular cup loosening conditions (secure, and 1 and 2 mm spherical loosening) considered were simulated using Sawbones composite bones. The excitation signal was introduced in the femoral lateral condyle region using a frequency range of 100–1500 Hz. Both the 1 and 2 mm spherical loosening conditions were successfully distinguished from the secure condition, with a favourable frequency range of 500–1500 Hz. The results of this study represent a key advance on previous research into vibrometric detection of acetabular loosening using geometrically realistic model, and demonstrate the clinical potential of this technique.

Integration of a wireless lock-in measurement of hip prosthesis vibrations for loosening detection

A new solution for the integration of a wireless measurement system intended for the detection of hip prosthesis loosening by vibration analysis is presented. Small vibration amplitudes in the presence of noisy sensor signals of the excited prosthesis are measured with a digital lock-in amplifier which is integrated in the prosthesis together with a controller and a passive telemetry working at 125 kHz. The integrated controller receives the necessary reference signal information to synchronize the lock-in amplifier from the external reader station via RF field. Measurements were taken at a bare femur and an artificial thigh where an electrodynamic audio shaker was fixed at or near the inner femur condyle. In order to explain and to predict the natural frequencies a problem oriented one-dimensional mechanical network model was developed.

Adopting Supervisor for Bolt Loosening Detection by Using Smart Washer

The objective of this study is the loosening detection of bolted joints without human involvement by applying the concept of structural health monitoring. The smart washer consists of a cantilevered plate bonded to a piezoelectric material, which provides self-sensing and actuation functions. The basic principle of how to detect bolt loosening is that the dynamics of a smart washer system vary depending on bolt tightening axial tension. The natural frequency of the smart washer was identified by using the sub-space state space identification algorithm in previous papers. However, the previous method has the problems that the fault detection accuracy deteriorates due to the influence of identification error and modeling error. This paper propose to apply the supervisor based on the model-based Fault Detection and Isolation technique for the detection of bolt loosening by using multiple observer. The numerical simulations and the experimentations were performed to verify the possibility of the observer-based loosening detection by using the smart washer. Improvement of bolt loosening detection accuracy was confirmed by comparing the proposed method with the previous method.

205 スーパーバイザを適用したスマートワッシャによるボルト締結体の緩み検知(セッション5 センサ)

The objective of this study is the loosening detection of bolted joints without human involvement by applying the concept of structural health monitoring. The smart washer consists of the cantilever available to detect bolt loosening by using flexibility of cantilever and self-sensing/actuation function. The loosening is detected by the dynamics change of a smart washer system. This paper propose to apply the supervisor based on the modelbased FDI (Fault Detection and Isolation) technique for the detection of loosening by using multiple observer. The experimentations were performed to verify the possibility of the observer-based loosening detection. Improvement of loosening detection accuracy was confirmed by comparing the proposed to previous method.

224 スーパーバイザを適用したスマートワッシャによるボルト締結体の緩み検知

224 スーパーバイザを適用したスマートワッシャによるボルト締結体の緩み検知

639 スマートワッシャによる緩み検知問題に対するスーパーバイザ手法の適用

639 スマートワッシャによる緩み検知問題に対するスーパーバイザ手法の適用

Bolt Loosening Detection by Using Smart Washer with 4SID and Evaluation of Temperature Characteristics

The objective of the present study is to apply the concept of structural health monitoring to the detection of loosening of a bolted joint without human involvement. This paper proposes a method of bolt loosening detection by adopting a smart washer which uses piezoelectric material with sub-space state space identification algorithm (4SID). The smart washer used in this study is in cantilever type with piezoelectric material, which adds the washer the self-sensing and actuation function. The principle used to detect the loosening of the bolts is a method of a bolt loosening detection noted that the natural frequency of a smart washer system is decreasing by the change of the bolt tightening axial tension. Therefore the study aims to identify natural frequency of a smart washer system adopting the system identification theory. The natural frequency of the smart washer was identified using sub-space state space identification method. Strong correlation was observed between the change of the natural frequency of the smart washer system and the decrement of bolt tightening axial tension. For more precise bolt loosening detection, the influence of the environmental temperature is also needed to be investigated. The experimental results indicate that the temperature change influence the bolt tightening axial tension.