Tooth Crack Detection Research Articles

A lightweight detection algorithm for tooth cracks in optical images

ObjectivesCracked tooth syndrome (CTS) is one of the major causes of tooth loss, presents the problem of early microcrack symptoms that are difficult to distinguish. This paper aims to investigate the practicality and feasibility of an improved object detection algorithm for automatically detecting cracks in dental optical images. MethodsA total of 286 teeth were obtained from Sun Yat-sen University and Guangdong University of Technology, and simulated cracks were generated using thermal expansion and contraction. Over 3000 images of cracked teeth were collected, including 360 real clinical images. To make the model more lightweight and better suited for deployment on embedded devices, this paper improves the YOLOv8 model for detecting tooth cracks through model pruning and backbone replacement. Additionally, the impact of image enhancement modules and coordinate attention modules on optimizing our model was analyzed. ResultsThrough experimental validation, we conclude that that model pruning reduction maintains performance better than replacing a lightweight backbone network on a tooth crack detection task. This approach achieved a reduction in parameters and GFLOPs by 16.8 % and 24.3 %, respectively, with minimal impact on performance. These results affirm the effectiveness of the proposed method in identifying and labeling tooth fractures. In addition, this paper demonstrated that the impact of image enhancement modules and coordinate attention mechanisms on YOLOv8's performance in the task of tooth crack detection was minimal. ConclusionsAn improved object detection algorithm has been proposed to reduce model parameters. This lightweight model is easier to deploy and holds potential for assisting dentists in identifying cracks on tooth surfaces.

Present status and future directions: Endodontic management of traumatic injuries to permanent teeth.

The prognosis of traumatized teeth depends largely on the fate of the pulp and its treatment. This review aims to update the present status on the endodontic management of traumatic injuries to permanent teeth and to identify relevant research areas that could contribute to an improvement in diagnosis and treatment of traumatized permanent teeth. Future research should pay greater attention to (1) diagnostic methods to assess the perfusion of the pulp and enhance detection of tooth cracks and initial signs of root resorption; (2) improved materials for vital pulp treatment; (3) studies focusing on type and duration of splinting after root fractures; (4) antiresorptive intracanal medication in case of posttraumatic pulp necrosis and infection-related resorption and (5) long-term data on the apical barrier technique compared to revitalization.

A time series model-based method for gear tooth crack detection and severity assessment under random speed variation

In industry (e.g., wind power), gearboxes often operate under random speed variations. A condition monitoring system is expected to detect faults and assess their severity using vibration signals collected under different speed profiles. A few studies have been reported for condition monitoring of gearboxes under random speed variations, including a novelty diagnostic method and a support vector machine (SVM) based method. However, these methods either are based on the strict assumption that the rotating speed does not vary significantly within a rotating cycle or have the drawback of low classification accuracy. This paper presents a time series model-based method for gear tooth crack detection and severity assessment under random speed variation. Specifically, the rotating speed and phase are considered as covariates in a linear parameter varying autoregression (AR) model for representing impulsive vibration signals. We propose refined B-splines for mapping the dependency between AR coefficients and the rotating phase. The performance of the presented time series model-based method has been validated using laboratory signals. The presented method can assess 93.8% of the tooth crack severity state correctly, which is better than the novelty diagnostic method (74.4%) and SVM-based method (87.7%).

An improved singular value decomposition-based method for gear tooth crack detection and severity assessment

Gear tooth crack fault detection and severity assessment using vibration analysis rely on the extraction of fault induced periodic impulses. Singular value decomposition (SVD)-based methods have been used by researchers for periodic impulses extraction. Most such methods extract high-energy signal components (SCs) but ignore weak-energy SCs. A newly reported reweighted SVD (RSVD) method uses periodic modulation intensity (PMI) criterion to measure how influential the periodic impulses are in each SC. This method effectively selects the correct SCs regardless of their energy. However, RSVD suffers from two issues: 1) it does not consider interference from non-fault related vibration components on the PMI, which leads to a high miss and/or false alarm rate; and 2) it directly conduct reweighted summation of all SCs with PMI values that exceed the threshold for signal reconstruction, which undermines periodic impulses extraction. To overcome these issues, this paper proposes an improved SVD-based method. First, the improved method adopts an auto-regression model-based baseline removal approach. SVD is applied to decompose the residual signal rather than the raw signal. Interference from non-fault related vibration components on the PMI is therefore eliminated. Second, the improved method selects the SCs such that the PMI of the reconstructed signal is maximized. Periodic impulses extraction is therefore strengthened. An experimental study was conducted. Results show that the improved method outperforms RSVD in terms of fault detection and severity assessment without creating a considerable computational burden.

Vibration analysis for tooth crack detection in a spur gear system with clearance nonlinearity

This paper concentrates on the gear tooth crack detection by investigating the effects of tooth crack on the vibration behaviors of a nonlinear spur gear system. Firstly, an analytical tooth crack model is used to calculate the gear mesh stiffness for different crack sizes. Then, a 3 DOF spur gear pair model is developed with consideration of tooth backlash and bearing clearance nonlinearity. The nonlinear frequency responses based on several statistical indicators are obtained to assess the vibration behaviors and fault conditions for the gear system. In addition, the vibration responses in time and frequency domains are also analyzed. Results show that peak and crest factor are the optimal indicators for tooth crack detection. Whereas, the kurtosis hardly works in most cases due to the presence of clearance nonlinearity. Besides, the vibration response characteristics such as time histories, phase diagrams, Poincare maps, FFT spectrums and sidebands, could present indications reflecting the tooth crack propagation to some extent. Therefore, the developed model is capable of predicting the mesh stiffness and vibration behaviors of a cracked gear system, and the corresponding vibration analysis could provide references aiding the tooth crack detection for researchers and engineers.

Model‐Based Analysis of Spur Gears’ Dynamic Behavior in the Presence of Multiple Cracks

Early detection of tooth cracks is crucial for effective condition‐based monitoring and decision making. The scope of this work was to bring more insight into the vibration behavior of spur gears in the presence of single and multiple simultaneous tooth cracks. The investigation was conducted in both time and frequency domains. A finite element analysis was performed to determine the variation in stiffness with respect to the angular position for different combinations of crack lengths. A simplified nonlinear lumped parameter model of a one‐stage gearbox with six degrees of freedom was then developed to simulate the vibration response of faulty external spur gears. Four different multiple‐crack scenarios were proposed and studied. The performances of various statistical fault detection indicators were considered and investigated. The simulation results obtained via MATLAB indicated that, as the severity of a single crack increases, the values of the time domain statistical indicators increase also, but at different rates. Moreover, the number of cracks was found to have a negative effect on the values of all the performance indicators, except for the RMS. The number and amplitude of the sidebands in the frequency spectrum were also considered, while assessing the severity of the faults in each scenario. It was observed that, in the case of consecutive tooth cracks, the number of spectrum peaks and the number of cracks were consistent in the frequency range of 4‐5 kHz. The main finding of this study was that the peak spectral amplitude was the most sensitive indicator of the number and severity of cracks.

Tooth cracks detection and gingival sulcus depth measurement using optical coherence tomography.

The aims of this study were to develop an automatic detection technique for tooth cracks and to suggest quantitative methods for measuring gingival sulcus depth using swept-source optical coherence tomography (SS-OCT). We evaluated SS-OCT with wavelength centered at 1310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks and gingival sulcus depth. The reliability of the SS-OCT images was verified by imaging the crack in extracted human teeth and gingival sulcus of porcine sample. The SS-OCT could automatically detect the position of various cracks and visualize the deep periodontal pockets. Therefore, the detection capability of SS-OCT images could be useful diagnostic tool for dental cracks and periodontal pockets.

Automatic detection of tooth cracks in optical coherence tomography images.

PurposeThe aims of the present study were to compare the image quality and visibility of tooth cracks between conventional methods and swept-source optical coherence tomography (SS-OCT) and to develop an automatic detection technique for tooth cracks by SS-OCT imaging.MethodsWe evaluated SS-OCT with a near-infrared wavelength centered at 1,310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks. The reliability of the SS-OCT images was verified by comparing the crack lines with those detected using conventional methods. After performing preprocessing of the obtained SS-OCT images to emphasize cracks, an algorithm was developed and verified to detect tooth cracks automatically.ResultsThe detection capability of SS-OCT was superior or comparable to that of trans-illumination, which did not discriminate among the cracks according to depth. Other conventional methods for the detection of tooth cracks did not sense initial cracks with a width of less than 100 μm. However, SS-OCT detected cracks of all sizes, ranging from craze lines to split teeth, and the crack lines were automatically detected in images using the Hough transform.ConclusionsWe were able to distinguish structural cracks, craze lines, and split lines in tooth cracks using SS-OCT images, and to automatically detect the position of various cracks in the OCT images. Therefore, the detection capability of SS-OCT images provides a useful diagnostic tool for cracked tooth syndrome.

Dynamic response and time-frequency analysis for gear tooth crack detection

Vibration health monitoring is a non-destructive technique which can be applied to detect cracks propagating in gear teeth. This paper studies gear tooth crack detection by investigating the natural frequencies and by performing time-frequency analysis of a 6 DOF dynamic gear model. The gear mesh stiffness used in the model was calculated analytically for different cases of crack sizes. The frequency response functions (FRFs) of the model were derived for healthy and faulty cases and dynamic simulation was performed to obtain the time signal responses. A new approach involving a short-time Fourier transform (STFT) was applied where a fast Fourier transform (FFT) was calculated for successive blocks with different sizes corresponding to the time segments of the varying gear mesh stiffness. The relationship between the different crack sizes and the mesh-stiffness-dependent eigenfrequencies was studied in order to detect the tooth crack and to estimate its size.

Vibration signal modeling of a planetary gear set for tooth crack detection

In a planetary gearbox, there are multiple vibration sources, and the transmission path of vibration signals changes due to the rotation of the carrier. This study aims to model the vibration signals of a planetary gearbox and investigate the vibration properties in the healthy condition and in the cracked tooth condition. A dynamic model is developed to simulate the vibration source signals. A modified Hamming function is proposed to represent the effect of the transmission path. By incorporating the effect of multiple vibration sources and the effect of transmission path, resultant vibration signals of a planetary gearbox are obtained. Through analyzing the resultant vibration signals, some vibration properties of a planetary gearbox are revealed and the fault symptoms of sun gear tooth crack are identified and located. Finally, the proposed approach is experimentally verified.

Dynamic modelling of a one-stage spur gear system and vibration-based tooth crack detection analysis

For the purpose of simulation and vibration-based condition monitoring of a geared system, it is important to model the system with an appropriate number of degrees of freedom (DOF). In earlier papers several models were suggested and it is therefore of interest to evaluate their limitations. In the present study a 12 DOF gear dynamic model including a gyroscopic effect was developed and the equations of motions were derived. A one-stage reduction gear was modelled using three different dynamic models (with 6, 8 and 8 reduced to 6 DOF), as well as the developed model (with 12 DOF), which is referred as the fourth model in this paper. The time-varying mesh stiffness was calculated, and dynamic simulation was then performed for different crack sizes. Time domain scalar indicators (the RMS, kurtosis and the crest factor) were applied for fault detection analysis. The results of the first model show a clearly visible difference from those of the other studied models, which were made more realistic by including two more DOF to describe the motor and load. Both the symmetric and the asymmetric disc cases were studied using the fourth model. In the case of disc symmetry, the results of the obtained response are close to those obtained from both the second and third models. Furthermore, the second model showed a slight influence from inter-tooth friction, and therefore the third model is adequate for simulating the pinion׳s y-displacement in the case of the symmetric disc. In the case of the asymmetric disc, the results deviate from those obtained in the symmetric case. Therefore, for simulating the pinion׳s y-displacement, the fourth model can be considered for more accurate modelling in the case of the asymmetric disc.

Gear tooth crack detection using dynamic response analysis

Efficient non-destructive fault detection and severity assessment can be performed using vibration analysis. This paper studies gear tooth crack detection through investigating the natural frequenc ...