Bolt Loosening Research Articles

Study on the effect mechanism of vibration excitation parameters on bolt loosening based on an implicit monitoring method

This study focuses on investigating the mechanism of bolt loosening under harmonic vibration. An implicit monitoring method of bolt loosening based on strain detection is proposed. By conducting simulations on a PCB (Printed Circuit Board) structure with fastening bolts base on the implicit method, a three-stage peculiarity for bolt loosening is established under harmonic vibration. Under different tightening torques, distinct effecting trends of the two excitation parameters of excitation amplitudes and frequencies on the strain response are indicated comprehensively. The comprehensive effect mechanism of the excitation amplitudes and frequencies on bolt loosening is investigated, and the results reveal that when the tightening torque is small, the excitation amplitude has a main impact on the strain RMS, while when the tightening torque is larger, the excitation frequency plays a major role. A bolt loosening peculiarity based on strain monitoring is provided for different excitation frequencies, amplitudes accompanied with different tightening torques under harmonic vibration. In practical application, the RMS value of the strain response of the monitored point is calculated and then compared with the three-stage loosening peculiarity to determine whether the bolt is loosen or not. The simulation analysis and test verification on a four-bolt connection structure are conducted based on the implicit method under harmonic vibration, and the good consistency is shown. Valuable insights for the detection and prevention of bolt loosening are provided, which have important engineering reference significance.

Automated Structural Bolt Micro Looseness Monitoring Method Using Deep Learning.

The detection of bolt loosening in key components of aircraft engines faces problems such as complex and difficult-to-establish bolt loosening mechanism models, difficulty in identifying early loosening, and difficulty in extracting signal features with nonlinear and non-stationary characteristics. Therefore, the automated structural bolt micro looseness monitoring method using deep learning was proposed. Specifically, the addition of batch normalization methods enables the established Batch Normalized Stacked Autoencoders (BNSAEs) model to converge quickly and effectively, making the model easy to build and effective. Additionally, using characterization functions preprocess the original response signal not only simplifies the data structure but also ensures the integrity of features, which is beneficial for network training and reduces time costs. Finally, the effectiveness of the proposed method was verified by taking the bolted connection structures of two key components of aircraft engines, namely bolt connection structures and flange connection structures, as examples.

Acoustoelastic Theory and Mode Analysis of Bolted Structures Under Preload

Bolted connections are a common feature of connection in mechanical structures, employed to secure connected parts by tightening nuts and providing preload. The preload is susceptible to various factors leading to potential bolt loosening. The acoustoelastic theory is the most common measure of a bolt structure’s stress. The present study investigates the relationship between the inherent properties of a structure and its acousticelastic properties. The modal response of the bolted structure under different preload forces is studied by translating the acoustoelastic relationship of the structure into an analysis of its intrinsic properties. The modal analysis reflects the relative change in wave velocity to be determined implicitly based on the eigenfrequencies of the structure. A frequency formulation of classical bolted structures based on acoustoelastic theory is presented in this paper to conduct the intrinsic characteristic analysis of bolted structures. The COMSOL5.4 simulation results are under the acoustic elasticity coefficients for ultrasonic wave propagation in bolt structures, as predicted by the acoustic elasticity theory, and the present solutions are compared with those available in the literature to confirm their validity. A systematic parameter study for bolted structures under the varying preloads with different material parameters, Lame elastic constants, Murnaghan third-order elastic constants, and structural parameters are presented. These results may serve as a benchmark for researchers in this field.

Detecting bolt looseness of flanged joint based on multiple-mode conversion

This article detects the bolt looseness of a flanged joint based on the energy conversion between multiple wave modes. The mode conversion introduced by the flange carries abundant information referring to the bolt looseness, but it requires precise sensing technology to capture each mode. The mode transducer for cylindrical shells was first proposed based on the orthogonality of mode shapes. The distribution scheme and electrode function enable the piezo transducer array to selectively control multiple modes. Both the simulated and experimental validations were subsequently conducted to check the design scheme. We examined the shapes and mode signals of the excited wave field to ensure that the energy was concentrated in the target mode. Finally, two-stage detection was conducted for eight bolts: the first stage detected the initial looseness of a single bolt using two indicators; the second stage trained the machine learning classifiers to recognize the location of the loose bolts. The results exhibited a high sensitivity of mode conversion in detecting initial looseness, with an accuracy of [Formula: see text] in recognizing the loose location.

Multi-bolt looseness monitoring using guided waves: a cross-correlation approach of the wavelet energy envelope

Abstract This paper proposes a novel approach for monitoring multi-bolt looseness using guided waves and the cross-correlation of the wavelet energy envelope. By assessing variations in the wave packet, the looseness in multi-bolt assemblies can be estimated. First, the dispersion effects of Lamb waves were theoretically analyzed using the Rayleigh-Lamb equation. Next, the wavelet energy was derived through wavelet transform, and the Lamb wave envelope was obtained as a criterion for accurately separating the wave packet. Cross-correlation analysis was employed to quantitatively evaluate the dispersion of wave packets for varying levels of bolt looseness. A looseness index, termed the normalized decorrelation coefficient of wavelet energy (NDCWE), was defined. Then, validation experiments were conducted using a joint with five M8 bolts, each tightened to a standard torque of 42 N·m. Two piezoelectric transducers were attached to the periphery of the bolt group. Three preload conditions were tested for each bolt: fully tightened, 80% of the standard torque, and 10% of the standard torque, corresponding to no looseness (NL), minor looseness (ML), and significant looseness (SL), respectively. Results showed that when significant looseness occurs, the NDCWE value exceeds 0.4, confirming the effectiveness of NDCWE in detecting substantial reductions in bolt preload. Experiments assessing the effect of temperature revealed that temperature has a negligible effect on the waveforms of the S0 and A0 mode waves. Finally, to quantitatively evaluate the efficiency of the ultrasonic transducers, the bolt-to-sensor ratio (BSR) was introduced. In this study, the BSR reached 2.5, indicating that a single piezoelectric transducer can monitor the preload of 2.5 bolts. The proposed approach shows great potential for multi-bolt looseness monitoring.

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

PurposeThe 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/approachIn 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.FindingsThe 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/valueThe 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 reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-06-2024-0216/

Entire loosening stage monitoring of bolted joints via nonlinear electro-mechanical impedance spectroscopy

This article proposes a nonlinear electro-mechanical impedance spectroscopy (NEMIS) methodology for the entire loosening stage monitoring of bolted joints. Unlike the conventional electro-mechanical impedance spectroscopy (EMIS) implemented in the frequency domain, NEMIS utilizes a temporal interrogating signal to procure the developed nonlinear impedance spectrum. It can harness the structural resonance information akin to the conventional EMIS, while concurrently capturing the contact acoustic nonlinearity (CAN) features with only one solitary Piezoelectric Wafer Active Sensor (PWAS). To elucidate the underlying principles of NEMIS, a comparative analysis juxtaposing the conventional EMIS, nonlinear ultrasonic techniques, and the proposed NEMIS was conducted. Subsequently, a reduced-order analytical model was established to simulate the rough contact interfaces of the bolted joints under various looseness states. Parametric studies were carried out to demonstrate the resonance deviation and nonlinear features resulting from the bolt looseness. Ultimately, the experimental implementation of NEMIS for the entire loosening stage monitoring of the bolted joint was performed compared with the conventional EMIS method. It was proved that NEMIS could detect both the incipient bolt loosening at an embryo stage and severe loosening of the bolted joint at the terminal period, integrating the continuous monitoring capability of conventional EMIS and the sensitivity of nonlinear ultrasonic methodology. The quantification on the severity of bolt loosening was accomplished via both linear and nonlinear damage indices, exhibiting a consistent trend with the gradations of bolt looseness. The article culminates in a summary, concluding remarks, and suggestions for future work.

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.

Vision-Based Real-Time Bolt Loosening Detection by Identifying Anti-Loosening Lines

Bolt loosening detection is crucial for ensuring the safe operation of equipment. This paper presents a vision-based real-time detection method that identifies bolt loosening by recognizing anti-loosening line markers at bolt connections. The method employs the YOLOv10-S deep learning model for high-precision, real-time bolt detection, followed by a two-step Fast-SCNN image segmentation technique. This approach effectively isolates the bolt and nut regions, enabling accurate extraction of the anti-loosening line markers. Key intersection points are calculated using ellipse and line fitting techniques, and the loosening angle is determined through spatial projection transformation. The experimental results demonstrate that, for high-resolution images of 2048 × 1024 pixels, the proposed method achieves an average angle detection error of 1.145° with a detection speed of 32 FPS. Compared to traditional methods and other vision-based approaches, this method offers non-contact measurement, real-time detection capabilities, reduced detection error, and general adaptability to various bolt types and configurations, indicating significant application potential.

Analysis of relaxation characteristics of multi-bolted connections under the transverse cyclic load

A modeling method under lateral cyclic loading is proposed for the complex multi-bolt connection loosening problem. Firstly, the thread stress surface is divided into n small sectors, and the self-relaxation mechanical model of multi-bolt connection is established considering the interaction between bolts. Secondly, the self-relaxation model is solved by MATLAB software and compared with Nassar model to verify the correctness of the model. Finally, considering the influence of different bolt number, hole clearance and connection spacing, the relaxation characteristics of multi-bolt connection are studied. The results show that during the loosening process, the bolt head undergoes a bending-sliding-bending cycle process, and the bolt tension shows a wave downward trend. When multiple bolts are connected, the number of loosening cycles decreases linearly with the increase of hole clearance. With the equidistant increase of bolt connection spacing, the number of bolt loosening cycles shows a decreasing trend. Compared with the number of bolts, the change of bolt hole clearance and connection spacing is more likely to affect bolt loosening.

Bolt loosening assessment using ensemble vision models for automatic localization and feature extraction with target‐free perspective adaptation

AbstractBolt loosening assessment is crucial to identify early warnings of structural degradation and prevent catastrophic events. This paper proposes an automatic bolt loosening assessment methodology. First, a novel end‐to‐end ensemble vision model, Bolt‐FP‐Net, is proposed to reason the locations of bolts and their hexagonal feature patterns concurrently. Second, an adaptive target‐free perspective correction method is proposed to correct perspective distortion and enhance assessment accuracy. Finally, an iterative bolt loosening quantification is developed to estimate and refine the bolt loosening rotation. Experimental parametric studies indicated that the proposed Bolt‐FP‐Net can achieve excellent performance under different environmental conditions. Finally, a case study was conducted on steel bolt connections, which shows the proposed methodology can achieve high accuracy and real‐time speed in bolt loosening assessment.

Sound Sensing: Generative and Discriminant Model-Based Approaches to Bolt Loosening Detection.

The detection of bolt looseness is crucial to ensure the integrity and safety of bolted connection structures. Percussion-based bolt looseness detection provides a simple and cost-effective approach. However, this method has some inherent shortcomings that limit its application. For example, it highly depends on the inspector's hearing and experience and is more easily affected by ambient noise. In this article, a whole set of signal processing procedures are proposed and a new kind of damage index vector is constructed to strengthen the reliability and robustness of this method. Firstly, a series of audio signal preprocessing algorithms including denoising, segmenting, and smooth filtering are performed in the raw audio signal. Then, the cumulative energy entropy (CEE) and mel frequency cepstrum coefficients (MFCCs) are utilized to extract damage index vectors, which are used as input vectors for generative and discriminative classifier models (Gaussian discriminant analysis and support vector machine), respectively. Finally, multiple repeated experiments are conducted to verify the effectiveness of the proposed method and its ability to detect the bolt looseness in terms of audio signal. The testing accuracy of the trained model approaches 90% and 96.7% under different combinations of torque levels, respectively.

Lorentz attractor excitation-based structural damage identification using state space curvature reconstruction- enhanced transformer

Abstract Vibration-based structural damage identification has been widely investigated. Different from previous studies that analyze vibrational responses in time and frequency domains, a new Lorentz attractor excitation-based damage identification is becoming a novel strategy with the advantage of capturing the structure’s nonlinear dynamic effects. In this study, Lorentz attractor-based chaotic signals were employed as excitation signals for the structural damage identification of a frame structure. Nonlinear responses were recorded and damages of bolt looseness at different locations were considered. The structural damages could be revealed in the state-space plot of the responses. A state space curvature reconstruction method was introduced to enhance the key features of the nonlinear responses. A small-sample damage identification is performed using a deep learning algorithm – a Transformer with an accuracy of 92.38%. The advantages of the proposed method over conventional deep learning algorithms were validated. The proposed method can be applied to health conditions identification of buildings, bridges, and trusses.

Experiment and detection method for loose bolts in sensor installation

Experiment and detection method for loose bolts in sensor installation

Audio feature augmentation for bolt looseness classification in data-deficient scenarios using machine learning

ABSTRACT Existing machine learning (ML) models have limitations in classifying bolt looseness in data-deficient scenarios. In this article, a novel audio-feature augmentation was established to detect percussion-based bolt looseness in data-deficient scenarios using ML approaches. An n-time augmentation was performed on the important features extracted from the percussion audio signals using correlation and eigenvalue analyses. The accuracy and generalisation ability of bolt looseness prediction of (i) k-nearest neighbour, (ii) multi-layer perceptron, (iii) multinomial logistic regression, (iv) random forest, and (v) hybrid (ML) methods were demonstrated using statistical metrics. One of the key observations from the present study is that the application of eight-fold cross-validation on the hybrid model trained with 20-time feature augmentation demonstrated the best overall scenario in enhancing bolt looseness prediction. It exhibited a maximum increase in accuracy of 18.0% in identifying bolt looseness levels in data-deficient scenarios compared to without feature augmentation. Further, the eight-fold cross-validation analysis indicated an accuracy fluctuation drop from 33.4% to 5.6%, indicating consistent prediction when trained with 20-time feature augmentation as compared to without feature augmentation. Overall, the present investigations manifested the verifiable potential of the audio feature augmentation approach to enhance bolt looseness prediction in data-deficient scenarios.

Temperature compensation method for impedance signals of bolt loosening under temperature varying conditions using TransUnet

Abstract Piezoelectric transducer based electro-mechanical impedance technology is an effective and reliable detection method for bolt structure loosening under external load. However, temperature change usually causes some non-loosening factors to change the impedance spectrum of bolt structure. In order to reduce the misjudgment of loosening damage caused by temperature change, it is necessary to construct a temperature compensation model for impedance spectrum. In this paper, the convolutional structure in U-net and Transformer are effectively combined to form a TransUnet deep neural network structure suitable for input and output of one-dimensional data. Using impedance data between 10 °C and 50 °C and temperature as input to the network. After convolution operation, the convolutional block attention module is embedded in the U-net to optimize the encoder transmission characteristics and enhance the performance of the skip connection in the traditional U-shaped structure. The temperature compensation rate (TCR) is defined to measure the effect of temperature compensation. Then the lightweight convolutional neural network structure is used to recognize the bolt loosening damage of the compensated impedance signal. The generalization ability of the TransUnet was tested using impedance data that is not in the training dataset. The results show that the TransUnet proposed in the paper can realize the temperature compensation of multi-peak impedance signals. The TCR and recognition accuracy of bolt loose damage reaches reach 0.003 and 95.7%, respectively, which is 11% higher than that without temperature compensation. At 60 °C, the TCR and the identification accuracy of loosening damage can still reach 0.0055 and 90.2%, respectively, which show that the TransUnet has strong generalization ability.

A unified nonlinear dynamic model for bolted flange joint disk-drum structures under different interface states: Theory and experiment

Bolted flange joint disk-drum structures (BFJDDSs) are key components in aero-engines, however, bolt looseness inevitably occurs due to extreme service conditions. This can cause the bolted joint interface to exhibit complex contact behaviors, which significantly complicate the vibration characteristics of BFJDDSs. Existing dynamic models for BFJDDSs have not considered the effect of bolt looseness (slip and separation), so their vibration behaviors are not well understood. In this study, a unified nonlinear dynamic model for BFJDDSs considering different interface states (stick, slip, and separation) is proposed, which is effective under both bolt looseness and non-looseness (stick) conditions. The bi-linear hysteretic model combined with the piecewise linear model is used to simultaneously consider different interface states. The Kirchhoff plate theory, the Sanders’ shell theory, and the Euler-Bernoulli beam theory are used to derive the energy functions of the disk, drum, and flange, respectively. Then, the Lagrange equations are employed to derive the governing equations of BFJDDSs. Modal and nonlinear forced vibration experiments are carried out on a BFJDDS to prove the correctness of the proposed mathematical model. Research results show that the proposed mathematical model is able to achieve good predictions of nonlinear dynamic properties of BFJDDSs under both bolt looseness and non-looseness conditions. This model is also able to well predict the jumping phenomenon observed in the experiment.

A combined RF–GRNN algorithm for monitoring complex damage of bolted joints with high-level robustness

Bolted joints in aerospace, rail transportation, civil engineering, and other applications are prone to various forms of damage, such as bolt loosening and crack, due to the harsh service environments. The difficulties arising from crosstalk and nonlinear evaluation between composite damage and single damage at bolted joints in practical monitoring environments are particularly pronounced. In response to this challenge, we propose an integrated algorithm, namely the random forest-generalized regression neural network (RF–GRNN) algorithm, characterized by high-level robustness. The proposed algorithm adheres to the core concept of “specificity” and forms a multi-task prediction framework of “classification before regression.” This approach enables efficient damage assessment and mitigates the nonlinear complexity of the prediction model. To evaluate the effectiveness of the RF–GRNN algorithm, its generalization, robustness, and prediction accuracy based on the piezoelectric ultrasonic guided wave principle under 0%–10% noise (the corresponding minimum signal-to-noise ratio was 10.4 dB) interference were investigated. Additionally, we clarify the impact of feature selection and its combination method on the algorithm’s prediction performance, deciphering the intrinsic link between prediction performance and feature distribution. The results demonstrate that the presented algorithm achieves the accurate classification and quantification of multiple bolted structural states, including no damage, crack, loosening, and crack-loosening composite damage. Therefore, the RF–GRNN algorithm is an important attempt to solve the bottleneck present in existing damage monitoring techniques which fail to balance the identification and quantification of multiple damage models.

Data-Driven Multi-Fault Detection in Pipelines Utilizing Frequency Response Function and Artificial Neural Networks

This research presents a data-driven structural health monitoring (SHM) approach for pipeline systems that leverages frequency response function (FRF) signals and artificial neural network (ANN) algorithms to accurately identify and classify diverse pipeline fault conditions. The study focuses on three specific faults: bolt looseness, scale deposits, and crack occurrence at pipeline supports, which were replicated on a pipeline segment located at the Sound and Vibration Research Group (SVRG) at University Putra Malaysia (UPM). The FRF signals were captured using accelerometers to monitor the structural health of the pipeline. The data acquisition stage involved collecting FRF signals from the accelerometers to capture vibrations and responses related to the identified faults using a Siemens LMS SCADAS data acquisition unit. The data underwent preprocessing, including the application of principal component analysis (PCA) for feature selection. The subsequent data processing stage involved the application of an artificial neural network (ANN) algorithm for pattern recognition to analyze and classify the acquired data, identifying patterns associated with the replicated fault conditions. The proposed methodology demonstrated exceptional performance, with the ANN model achieving consistently high overall accuracy (above 99.7%) and remarkably low mean squared error (in the range of 0.0088×10−3to0.3062×10−3) across multiple iterations and sensor datasets. The detailed class-specific metrics, including accuracy, precision, sensitivity, and F1-score, further substantiated the model's effectiveness in identifying the individual fault types with near-perfect or perfect results for the majority of the fault scenarios. The location-invariant performance of the ANN model across different sensor placements demonstrates the robustness of the proposed data-driven SHM methodology. This research highlights the transformative potential of integrating state-of-the-art data-driven techniques to revolutionize the monitoring and assessment of critical pipeline infrastructure, ultimately enhancing the safety, reliability, and longevity of these vital systems.

A tailless FBG smart bolt with good performance

A novel type of tailless FBG smart bolt with good performance is developed. Owe to the innovative design of an integrated FBG sensor comprising a strain measured FBG and a temperature-compensated FBG, the tailless FBG smart bolt serves the purpose of torque monitoring or bolt looseness monitoring. We use a theoretical approach to establish the linear correlation between the applied torque and the central wavelength of the tailless FBG smart bolt. Additionally, a newly designed M24 bolt with a nominal strength has been integrated as a crucial component of the tailless FBG smart bolt. To enable the experimental study, the tailless FBG smart bolt is newly designed, fabricated and tested. The experimental results show a strong linear correlation between the applied torque and the central wavelength of the tailless FBG smart bolt, showing good agreement with the theory.