Bolt Loosening Research Articles

An angle sensor based on a sector ring patch antenna for bolt loosening detection

This paper presents an angle sensor based on a sector ring patch antenna (SRPA) for loose bolt detection. The sensor is comprised of two parts, a ring substrate sandwiched between a ground plane and a sector ring radiation patch, and a sector ring substrate covered by a radiation patch. The two radiation patches are partially overlapped and the overlapped areas are in contact allowing the electric current to flow within the combined radiation patch. The resonant cavity theory is adopted to explain the relationship between the frequency shift and the overlapped angle. The Ansoft high frequency structure simulator software is used to simulate SRPA, and the dimensional parameters of the antenna are optimized to make the antenna perform better. For verification, two sets of SRPAs are fabricated and tested.

1D-CNN-based damage identification method based on piezoelectric impedance using adjustable inductive shunt circuitry for data enrichment

The electromechanical impedance (EMI)-based damage identification method is a non-destructive testing approach in the field of structural health monitoring. The frequency response function (FRF) of EMI can effectively reveal the health conditions of a structure. Typically, the health condition is identified by comparing the FRF of a structure to that of a baseline. However, baselines may exhibit unpredictable shifts in real applications. In this study, a new EMI-based health identification method is proposed without reference to baselines or handcrafted features. An adjustable inductive shunt circuit that can enrich the EMI dataset is connected to a piezoelectric transducer. Pre-set damage, including bolt looseness and mass variations, are selected to demonstrate damage identification. The FRFs are extracted using a phase-sensitive detection algorithm. The damage identification model is realized using a one-dimensional convolutional neural network. Experimental results show that the proposed method can identify the location of bolt loosening and mass variation with an overall accuracy of 99.24%. The proposed method can be applied for identifying the health conditions of a structure with strong nonlinearity.

Coda wave interferometry-based very early stage bolt looseness monitoring using a single piezoceramic transducer

The traditional monitoring methods can only give warnings for the bolts with severe looseness. However, it is essential for the safety of bolted joints to detect the looseness of bolts at the very early stage. To this end, in this paper, coda wave interferometry (CWI)-based high-resolution bolt preload monitoring using a single piezoceramic transducer is proposed. According to the CWI and acoustoelastic theories, a theoretical model is established and the linear relationship between the time shifts of coda waves and the preload variations of the bolt is derived. An experiment, in which a piezoceramic transducer simultaneously functions as the actuator and sensor, was carried out to verify the effectiveness of the proposed method. Three lead zirconium titanate transducers at different locations of a bolted specimen are tested. The experimental results show that the time shifts of coda waves increase linearly with the decrease of bolt preload and the detectable resolution of bolt preload (DRBP) is up to 0.326%. The DRBP value proves that the proposed technique can successfully monitor bolt looseness at the very early stage. In addition, a comparison study is carried out between the CWI-based method and the energy-based wavelet packet decomposition (WPD) method, and the result shows that the preload sensitivity of the CWI-based method is about six times higher than that of the WPD approach. Therefore, the CWI-based method is an effective way for the in situ monitoring of bolt looseness, especially in the embryonic stage.

Vibration-Based Loosening Detection of a Multi-Bolt Structure Using Machine Learning Algorithms.

Since artificial intelligence (AI) was introduced into engineering fields, it has made many breakthroughs. Machine learning (ML) algorithms have been very commonly used in structural health monitoring (SHM) systems in the last decade. In this study, a vibration-based early stage of bolt loosening detection and identification technique is proposed using ML algorithms, for a motor fastened with four bolts (M8 × 1.5) to a stationary support. First, several cases with fastened and loosened bolts were established, and the motor was operated in three different types of working condition (800 rpm, 1000 rpm, and 1200 rpm), in order to obtain enough vibration data. Second, for feature extraction of the dataset, the short-time Fourier transform (STFT) method was performed. Third, different types of classifier of ML were trained, and a new test dataset was applied to evaluate the performance of the classifiers. Finally, the classifier with the greatest accuracy was identified. The test results showed that the capability of the classifier was satisfactory for detecting bolt loosening and identifying which bolt or bolts started to lose their preload in each working condition. The identified classifier will be implemented for online monitoring of the early stage of bolt loosening of a multi-bolt structure in future works.

Preload measurement of steel-to-timber bolted joint using piezoceramic-based electromechanical impedance method

Steel-to-timber bolted connections play an essential role in enabling glulam column and beam to work together. However, these connections are prone to bolt looseness due to shrinkage and creep effects of timber material during a long service life, which may cause consequent connection failures and structural safety hazards. Recently, detection and evaluation of bolt looseness have gained considerable attention, whereas the existing vision-based method is challenging to inspect early bolt looseness, and the percussion-enabled approach is susceptible to being affected by harsh environmental noises. Thus, in this study, the authors proposed a pre-tightening torque measurement method for steel-to-timber bolted connection based on variations of electromechanical impedance (EMI) of designed Lead Zirconate Titanate (PZT) sensors. A theoretical formula is derived first based on the piezoelectric equation and boundary conditions to illustrate the feasibility of using EMI variations to reflect looseness levels. Moreover, a sensor that can be easily integrated into the steel-to-timber connection and work as a washer is designed and fabricated. Consequently, the environmental performance against temperature and humidity of designed sensors is investigated by preliminary tests. Based on preliminary tests results, a frequency range that showed lower environmental sensitivity is selected as the excitation signal in the following bolt looseness measuring experimental studies. Experimental results well validate the effectiveness of EMI method in the monitoring of pre-tightening torque of steel-to-timber bolted connections, showing a great potential to inspect preload looseness of glulam structures in field applications.

へたりがボルト締結体の軸力低下に与える影響評価

Loose bolts and the loss of clamp force can lead to serious accidents such as broken or missing parts. Loosening of bolts is classified into two types: rotational loosening and non-rotational loosening. The former is caused by external load that rotates the bolt and nut. The latter is caused by material deformation due to temperature change or load. The basic principle of non-rotational loosening has not yet been clarified in many cases. That reason is that the changes are minute and long-lasting, and the factors that affect them are diverse. In this study, the phenomenon known as "initial loosening" among non-rotational loosening was examined. This is due to the flattening of small irregularities on the material surface by the surface pressure of tightening, which decreases the thickness of the fastener, thereby reducing the axial force of bolt. Therefore, experiments were conducted in this paper to elucidate the basic principles of initial loosening. Specifically, the axial force, surface roughness, number of plates, and thickness of the fastener were changed without applying external force, and the changes in axial force were measured by strain gauges to evaluate their effects.

On-Line Fault Diagnosis for Structural Health of Wind Power Dedicated Busbar Trunking

The key objective in structural health monitoring of wind power dedicated busbar trunking (WPDBT) is to detect abnormal events in structural behavior by monitoring of severity of fixing bolt looseness in the linear connection unit of WPDBT. However, it is difficult to directly monitor the looseness of the fixing bolts online. The tightening torque of the fixing bolt directly reflects the looseness of the bolt. A novel quantitative indirect measurement method of tightening torque employing temperature rise, operation current, structure and material parameters as direct measurement is proposed in this paper. Based on the proposed physical model of the connection between the tightening torque, the temperature rise and the operation current of the linear connection unit, a magnetic-electric-thermal multi-physics coupling model of WPDBT is established. The bolt tightening torque can be directly calculated from the temperature rise and the operation current by the physical model presented in the paper. To further demonstrate the accuracy of the method and achieve online monitoring of structural health, an online monitoring system for fault diagnosis of the linear connection unit of WPDBT is designed based on this physical model. The online monitoring system was applied to an experimental platform for WPDBT. The effectiveness of the method proposed in this paper is further verified. It is proved that the temperature rise and operation current of the linear connection unit can indirectly measure the looseness of the fixing bolts. Finally, a method of structural health assessment for WPDBT is given. The new approach opens the way for real-time monitoring the structural health for WPDBT.

Damage detection and localization in loose bolted joints

In this paper damage detection and damage localization of imperfect bolted joints is studied numerically using three-dimensional explicit finite element simulations. The study is focused on using contact acoustic nonlinearity (CAN) due to interaction of asymmetric guide waves (A0) with imperfect joint surfaces for detecting and locating imperfect joints. Numerically obtained data are processed and it is shown that the CAN can detect and locate defects. The finite element model is used to study several case studies. It is demonstrated that finite element can predict propagation of guided waves in bolted joints. CAN is used for locating the loose bolt. It is demonstrated that FE model followed by signal processing approach used in this study can successfully and accurately detect and locate the imperfect joints. Overall, the findings of this study provide improved physical insights into using CAN for detecting and locating imperfect joints, which can be used to further advance damage detection techniques using nonlinear guided waves

Diagnosis of bolt loosening using transmitted ultrasonic pulse with mode conversion

Diagnosis of bolt loosening using transmitted ultrasonic pulse with mode conversion

A Novel Multisensor Orthogonal Attention Fusion Network for Multibolt Looseness State Recognition Under Small Sample

Bolts are common and effective fasteners for connecting steel structures, and bolt looseness detection is essential to ensure structural reliability and equipment safety. However, due to the complex spatial structure and multi-bolt of pipeline flange bolt connections, it is difficult for existing methods to implement fast and precise detection with single-sensor information and scarce data. To overcome these challenges, a novel multi-sensor orthogonal attention fusion network (MsOAFN) is proposed for multi-bolt looseness state recognition under a small sample in this paper. MsOAFN consists of feature extraction, feature fusion and state classification. A sensor-to-channel (STC) multi-scale convolution is used to extract features of different sensors to avoid data conflicts among sensors. In addition, a feature self-calibration attention module is built to expand the receptive field, alleviate information loss and enrich depth features during the down-sampling process. Then, a lightweight orthogonal attention fusion module is constructed, which fuses the shallow and depth features to further supplement and enhance feature information. Finally, the fusion features are input into the classification module to finish bolt loosening state recognition. Two case studies were conducted on a pair of typical flanged multi-bolt connection to verify the effectiveness of the proposed method, with an average accuracy of 99.77% and 98.84%, respectively. The superiority of this method in loosening detection of flanged bolt connection structure is compared with other algorithms.

Research on factors affecting competitive failure between loosening and fatigue of bolt under combined excitation

At present, research on loosening and fatigue of bolt is relatively mature. But insufficient research on the competitive failure between loosening and fatigue of bolt under combined excitation, as well as its influencing factors still exits. Therefore, the competitive failure between loosening and fatigue of bolts was confirmed by conducting a competitive failure test under combined excitation and its influencing factors were analyzed based on this test. The analysis results show that the displacement-load ratio, load amplitude, phase difference and preload have a significant influence on the competitive failure between loosening and fatigue. The failure type of bolts is only related to the ratio of transverse displacement amplitude and maximum axial load (R). Meanwhile, there is a critical value of R that can determine that the bolt will occur loosening failure or fatigue failure. In this case, life of the bolts is mainly affected by the load amplitude, phase difference, and preload. The combined excitation amplitude and transverse load amplitude can accelerate bolt failure, while the axial load has different effects depending on its size. Finally, based on the analysis results, guidance and suggestions are provided for reasonably selecting various influencing parameters. According to the competitive failure relationship between the loosening and fatigue and the influence law of its parameters, it is convenient to predict the failure type and failure life of bolts under combined excitation, which provides a reference for engineering applications.

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.

Competitive Failure of Bolt Loosening and Fatigue under Different Preloads

Existing research on the competitive failure relationship, failure mechanism, and influencing factors of bolt loosening and fatigue under different preloads is insufficient. This study analyzes the competitive failure relationship between bolt loosening and fatigue under composite excitation through competitive failure tests of bolt loosening and fatigue under different preloads. The results indicated that the failure mode of the bolt is only related to the load ratio (R) and is unrelated to the initial preload and excitation amplitude, which only determine the failure life of the bolt. The small axial loads of composite excitation can restrain bolt failure, and the significant degree of this restraining effect is different for different preloads. Subsequently, a fracture analysis of the bolt was performed to verify the competitive failure relationship of the bolt from a microscopic perspective, and the competitive failure mechanism of the bolt was determined. Based on the findings, we propose a calculation equation for the optimal preload of 8.8 grade high-strength bolts that can serve as a reference for engineering applications.

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.

A novel autonomous strategy for multi-bolt looseness detection using smart glove and Siamese double-path CapsNet

Recently, the issue of bolt looseness has attracted more attention due to its severe consequences. Among different methods for bolt looseness detection, the active sensing method that is based on stress wave signals is preferred since it is low cost and high robust. However, current active sensing method depends on permanent contact sensors, which may be impractical. Moreover, the investigation of multi-bolt looseness detection via the active sensing is very limited so far. With the above deficiency in mind, we propose a new robotic-assisted active sensing method based on our newly designed PZT-enabled smart gloves (SGs) and position-based visual servoing (PBVS) technique. Particularly, another main contribution is that we develop a new Siamese CapsNet to classify stress wave signals under different cases for multi-bolt looseness detection. Compared to machine learning (ML) and traditional deep learning techniques such as Convolutional Neural Networks (CNN), the proposed Siamese CapsNet model can achieve better performance and realize the recognition of signals that is never used during the training, which is impossible for common classification methods. Finally, an experiment is conducted to verify the effectiveness of the proposed method and Siamese CapsNet, which can guide future research significantly.

Automated structural bolt looseness detection using deep learning‐based prediction model

As one of the most common coupling elements in infrastructures, bolted joints play an important part in ensuring the integrity and safety of the whole system, whose failure may cause disastrous consequences. In recent years, precise detection and evaluation of bolt looseness have attracted numerous researchers' interest. However, the reliability of existing methods cannot be well guaranteed in long-term field detection, and real-time feedback is rather costly. This paper proposes a novel bolt looseness detection method based on audio recognition and deep learning. Firstly, a percussion experiment was designed to collect audio signals of bolts at different torque levels. Then, the time-domain bolt percussion signals were converted into Mel-frequency spectrograms, and the convolutional neural network (CNN) was adopted to mining deep information from the images for classification. To further verify the effect of different initial prestress levels on the vibration frequency of the bolted joint, a numerical study was conducted with the consideration of three different prestress levels. The results reveal that the proposed method has a high recognition accuracy in identifying bolt looseness conditions. Additionally, an iOS APP of acoustic vibration was established for real application. The prerecorded and untrained percussion audio was used to simulate the real-time bolt looseness detection, which shows its potential in real future applications.

Effect of bolt tightening force on the transmission tower modal parameters and a method for looseness identification

The transmission tower is important structure equipment in the power system. The bolts which connect the angle steel together may loose under the cycle loading. As a further effect, the dynamic behaviors of the tower may change and lead to failure. In this paper, the finite element model of a typical 500 kV transmission tower which considers the concerned region actual connections of bolts was established. And modal analysis was conducted on two different models to obtain the influence of the bolt looseness on the natural frequencies. After that, a method based on the modal analysis and the radial basis function (RBF) neutral network was used to identify the location and the severity of bolt looseness in a lap plate model. The results show that the bolt looseness of concerned region has obvious influence on the high order mode frequencies and the identification method can be applied in the looseness detection of transmission tower in further work.

Testing and Analysis of Fasteners

Abstract: By considering the tightening process, the experimental testing will be conducted to explore the mechanism of bolt selfloosening under biaxial loading. The most common mode of failure is overloading: Operating forces of the application produce loads that exceed the clamp load, causing the joint to loosen over time or fail catastrophically. Over torque might cause failure by damaging the threads and deforming the fastener, though this can happen over a very long time. Also, the bolts may fail under fatigue. The components used in the system are bolts, pneumatic cylinder and flow control valve. The pneumatic cylinder is actuated with the help of compressor. The flow of air in the cylinder will be controlled with the help of pneumatic cylinder which will be acted on the bolts in two directions that is from downward & upward direction. This means the load will be tensile and shearing load. The bolts are attached to the plates. Because of actuation of the pneumatic cylinder the bolts will become loose. These bolts will be tested by using biaxial loading. The result & conclusion was drawn after the experimental testing. Keywords: Bi-axial Loading, Fasteners, Bolt Loosening, Residual Torque, Fastener Overloading

Experimental Study on Impedance-Based Damage Detection Considering Temperature Effect

The impedance-based damage detection technique has the potential for health monitoring of different types of structures. However, it is necessary to consider the temperature effect on the impedance signal in applying this technique to actual structures. In this study, an effective impedance-based damage detection method that compensates for the temperature effect was developed. Experimental tests on a steel frame structure connected with high tensile bolts were performed. Moreover, the temperature effect on the impedance damage index was compensated for detecting damage caused by bolt looseness; that is, the relationship between the impedance damage index and the temperature was established through long-term measurements. Based on this relationship, damage detection was performed by compensating for the temperature effect. Because the damage index after the bolt loosening reflects the effects of temperature and damage, it is difficult to evaluate the damage by monitoring only the damage index. However, after compensating for the temperature effect, it was observed that the damage could be estimated precisely. The damage was effectively monitored after measuring the impedance signal and temperature over a specific period for the initial healthy structural state, analyzing the correlation between the impedance damage index and temperature, and setting an appropriate warning criterion based on the correlation.

A deep‐learning approach for health monitoring of a steel frame structure with bolted connections

This study is motivated by the need to develop a data-driven deep-learning approach for vibration-based structural health monitoring of a steel frame structure with bolted connections. A convolutional-neural-network-based deep-learning architecture is designed and trained to extract discriminative features from the vibration-based time-frequency scalogram images and use those to distinguish the undamaged and damaged cases of the targeted frame structure. Different damage and undamaged classes corresponding to the loosening of bolts are categorized as fully loose, hand tight, and fully tight (undamaged) conditions. The average training and validation accuracy were found to be 100% and 98.1%, respectively. In order to check the performance and robustness of the technique, testing is carried out for an unseen dataset corresponding to the training classes as well as some additional cases close to the training classes. The proposed deep-learning approach can successfully classify the damage classes with high testing accuracy that demonstrates its efficacy as an automation tool for health monitoring of connections of plane frame structures.