EARLY DETECTION OF GEAR TOOTH CRACKING USING THE RESONANCE DEMODULATION TECHNIQUE

Rotating shafts operating under complex loading conditions are susceptible to develop fatigue cracks at the localized stress concentration points. Fatigue cracks are the prime sources for catastrophic failures. Therefore, it is vital and challenging for the early fatigue crack detection to prevent costly machinery breakdowns. A rotor test rig with a facility of introducing stochastic loads on to the rotating shaft is designed and developed to induce fatigue crack on the shaft is utilized for this study. Accelerated fatigue tests are performed, along by introducing stochastic loads, on a shaft specimen seeded with circumferential V-notch configuration. The methodology for early crack detection is based on analyzing the steady-state vibration data acquired, till the shaft specimen develops fatigue crack, from various locations of the test rig using different sensors like miniature accelerometers, wireless telemetry strain gauge, and laser vibrometer. In the present study, signal processing tools like continuous wavelet transforms (CWT), Hilbert–Huang transform (HHT), and short-time Fourier transform (STFT) are employed for the early fatigue crack detection. Wavelet packet (WP) analysis is used to decompose the experimental vibration response data into narrow frequency band signals before the processing of CWT, HHT, and STFT analyses. In addition, these techniques are compared to evaluate their detection performance when employed directly on raw data and preprocessed data using WP analysis. Finally, this paper provides a detailed description and comprehensive selection of time-frequency techniques for the early fatigue crack detection.

A critical review and comparative study on image segmentation-based techniques for pavement crack detection

Environmental-assisted cracks in pipeline steels usually undergo the following three sequential stages prior to the failure: • Stage 1 – crack initiation and early stage crack growth, in which cracks initiate at imperfections but grow slowly depth-wise with time. Crack length may be seen to increase either because of merging with new small cracks in the vicinity of an existing crack or faster crack growth at the crack tip. Some cracks pose little threat to pipeline steel integrity if they remain dormant. • Stage 2 – Increased crack growth rate where crack growth can be dictated by mechanical driving forces and crack growth rate increases with time. • Stage 3 – The final stage of crack growth where crack growth rate is very high. Typical crack management programs mitigate cracks prior to entering Stage III. It is of great importance that pipeline steels with Stage II cracks are detected, monitored, and managed to ensure operational pipeline integrity. Although a range of crack in-line inspection and detection techniques with varied detection limits are available, it is not clear how their detection limits match the threshold geometrical dimensions of Stage 2-cracks. This investigation is aimed to define critical geometrical dimensions of cracks that are considered to be Stage 2 cracks. The determination of critical geometrical dimensions of Stage 2 cracks was made with a consideration of a wide range of situations including pipeline operating conditions, susceptible environments for crack growth, metallurgical, fabrication and construction conditions of pipeline steels. A comparison of the threshold geometrical dimensions of Stage 2 cracks with the crack detection limits of modern crack inspection and detection techniques are made at the end of the paper.

Ultrasonic techniques have the potential to provide early detection of fatigue induced cracks originating from fastener holes with the long term goal of structural health monitoring. This paper considers energy‐based algorithms for analyzing ultrasonic waveforms obtained using fixed angle beam transducers in a through‐transmission configuration. The transducers were located on either side of the fastener hole with the sound beam oriented along the loading direction, and waveforms were obtained as a function of applied load. Previous work has shown that the ratio of the received ultrasonic energy at under tensile loading to the energy with the sample under no load is a robust method for determining the presence of cracks. The reason for the effectiveness of this metric is that the energy reduction from the crack opening is accentuated by the applied load. For structural health monitoring applications, the earliest possible detection of cracking is desired, and the total energy ratio method does not utilize...

With modern health monitoring systems on-board, helicopters are still experiencing undetected faults in super critical mechanical components. Two fatal crashes of the Airbus Super Puma helicopter were caused by faults in a planetary gear-bearing component. The accident investigations showed that the fault (i.e. the bore crack) initiated from fatigue spalls on the planet bearing outer raceway, which is integral with the bore of the planet gear. A recent study proposed a method based on planet-gear synchronous signal averaging to detect a planet gear bore crack. Applying deep-learning networks to gear fault diagnosis has becomes a popular topic in machine health monitoring, with the majority of studies focused on fault classification problems. In this paper, we propose a method that relies mostly on the data without detailed knowledge of the system physics. We employ a deep machine learning method, i.e. the Long Short-Term Memory based Recurrent Neural Network (LSTM-RNN), as a signal processing tool to analyse vibration data generated in a gear rig test with faulty gears. We focus on detecting anomalies or changes induced by the gear faults in the LSTM prediction error signal, or LSTM-Residual. We compare the fault detectability of LSTM-Residual with that of some of the commonly used residual signals in gear fault diagnostics. We also provide a detailed study on how to apply the LSTM to monitoring gear tooth crack growth at various stages of the fault development, and what the potential pitfalls are in applying the LSTM network—especially the problem of false detection.

Early detection of cracks in beams, machines, and other structural elements with applications in the maritime industry, oil and gas sector, mining sector, and other systems or facilities have become a major concern in building and construction and other engineering fields. Cracks frequently appear first on concrete surfaces and structures under load conditions and serve as an indicator of further deterioration. Fatigue can have a momentous impact on crack. It is, therefore, significant to detect cracks at an early stage in order to avoid catastrophic consequences. As a result, researchers have devised a variety of methods to achieve this goal. This study considers the use of a simple cantilever beam to detect the presence of a crack from measured vibration data. For this experiment, a beam with cracks in various locations and another beam without cracks were considered. Some of the variables considered at different crack locations include the acceleration responses, crack locations, mode shapes, and natural frequencies. The experimental framework was built around the data by using an ANN feed-forward backpropagation algorithm with a sigmoid function of layers 2–10-4–4, divided into 70% training, 15% validation, and 15% testing sets using a data portioning approach. The statistical indices showed a mean squared error (MSE) value of 0.0000312, root mean squared error (RMSE) value of 0.000111, and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) value of 1.0, and regression coefficient (R) value of 1.0. The result shows a very low MAE and RMSE. It was concluded from the result of the model that the presence of crack on the test sample is minimal. ANN proved to be a satisfactory technique for the analysis of stochastic datasets.

Early detection and analysis of concrete cracks allow preventive measures to be taken to prevent damage and possible failure of civil structures. However, previous methods of crack analysis require manual labor or costly equipment. Smartphones nowadays have high-quality cameras at an attainable price. This study is about developing an automated crack detection and measurement system with a mobile application for image acquisition. A smartphone camera is used to capture crack images, Mask R-CNN for concrete surface crack detection, and image processing to measure crack width and length. A total of one-hundred and fifty (150) images were tested. The system can successfully perform crack detection, crack measurement, and suggest crack condition if it is safe or unsafe. The system achieved 85% crack detection precision, ±0.15mm error on width measurement, and less than 10% relative error on length measurement.

Fast and Non-invasive Surface Crack Detection of Press Panels Using Image Processing

The present work reports an efficient way of capturing real-time crack propagation in concrete structures. The modified spectral analysis based algorithm and finite element modeling (FEM) were utilised for crack detection and quantitative analysis of crack propagation. Crack propagation was captured in cement-based composite (CBC) containing saw dust and M20 grade concrete under compressive loading using a simple and inexpensive 8-megapixel mobile phone camera. The randomly selected images showing crack initiation and propagation in CBCs demonstrated the crack capturing capability of developed algorithm. A measure of oriented energy was provided at crack edges to develop a similarity spatial relationship among the pairwise pixels. FE modelling was used for distress anticipation, by analysing stresses during the compressive test in constituents of CBCs. FE modeling jointly with the developed algorithm, can provide real-time inputs from the crack-prone areas and useful in early crack detection of concrete structures for preventive support and management.

A novel approach of surface crack detection using super pixel segmentation

A new fibreoptic reflectometric technique for the automatic detection and measurement of surface cracks is proposed, described and analysed. A relationship between the system output image and the surface crack distribution is obtained from theory and a crack measurement principle is derived. Analysis of the experimental data obtained using a first laboratory prototype has validated the proposed measurement principle. The results of a second generation prototype confirm that the technique promises to be a valuable tool in practical inspection tasks.

The detection and prevention of concrete cracks and spalls is crucial to ensure the structural integrity and longevity of civil infrastructure. In this research paper, we propose a novel method for concrete crack and spall detection based on YOLOv8 with ByteTrack and supervision. The proposed method exploits the advantages of the YOLOv8 object detection framework, which provides real-time and accurate detection, tracking and counting of various objects. By integrating ByteTrack, a state-of-the-art tracking algorithm, we increase system performance and robustness in tracking cracks and spalls over time. Furthermore, supervision is employed to improve detection accuracy through iterative training and fine-tuning. To train the model, an extensive dataset of concrete crack and spall images is collected, annotated, and enhanced. The dataset includes a variety of scenarios, lighting conditions, and crack/spill sizes, which ensures the model's ability to generalize to real-world situations. Transfer learning is used to support the YOLOv8 backbone with pre-trained weights, to accelerate the convergence of the training process. Experimental evaluation is conducted on a benchmark dataset, and the proposed method outperforms existing techniques in terms of accuracy, precision, and recall. YOLOv8 with ByteTrack and Supervision achieves an overall accuracy of 94% detection rate for concrete cracks and spalls, even under challenging conditions. Real-time deployment is achieved on a high-performance computing platform, allowing efficient and timely monitoring of concrete structures. The proposed method demonstrates its potential as a valuable tool for infrastructure management and maintenance, enabling early detection of concrete cracks and spalls. By promptly identifying such defects, necessary repair and reinforcement measures can be implemented, preventing further deterioration and ensuring the safety and longevity of civil infrastructure. The high detection performance of the proposed approach, achieving excellent precision, recall, and F1 score for both crack and spall detection. Our system's overall average precision of 93% indicates its accuracy in detecting concrete cracks and spalls. Additionally, the system exhibits fast real-time processing with an inference speed of 50fps and is highly robust in handling diverse concrete crack and spall scenarios. Future work may focus on expanding the dataset, exploring the integration of additional detection algorithms, and evaluating system performance in large-scale applications.

This study focuses on developing and implementing Mamdani hybrid fuzzy logic inference system (FIS) for transverse crack detection and fault diagnosis in a woven fiber laminated glass/epoxy composite beam using different vibration modes of natural frequencies. The shifting of vibration is attributed to the implication of cracks. These vibration signatures are fuzzified through hybrid fuzzy sets (triangular, trapezoidal, Gaussian) and scaled to crack location and depth using the fuzzy rules and defuzzification process. The vibration signatures are recorded using ABAQUS finite element (FE) simulation software for a fixed beam and are fed as input parameters to the developed FIS for computing the desired outputs. The realization for crack depth and position is experimentally verified through a Fast Fourier Transform (FFT) analyzer. The experimental results with simulated data show that fuzzy logic application detects crack positions and depth accurately at different levels. It is concluded that the hybrid FIS bears a close resemblance to the experimental analysis and also stands out as an effective method for crack detection in LCB over other standalone methods. The current method can be used as a cost-effective non-destructive technique for health monitoring and fault diagnosis of composite beam structures in any practical field.

Damage analysis in composite materials using a hybrid artificial immune system

Dynamic simulation of spur gear with tooth root crack propagating along tooth width and crack depth