Characterization of Fatigue Crack Growth Based on Acoustic Emission Multi-Parameter Analysis

Acoustic emission analysis during fatigue crack propagation by Bayesian statistical modeling

Identification and prediction of fatigue crack growth under different stress ratios using acoustic emission data

Rail networks are gradually getting busier with rolling stock traveling at higher speeds and carrying heavier axle loads than ever before. In order to consider the further application of acoustic emission (AE) for railway damage monitoring, the fatigue and AE characteristics during the fatigue crack growth (FCG) process in a rail steel were investigated in this study. For better recognition and prediction fatigue damage of rail steel, the relationship between the micromechanism and the characteristics of AE signals were studied. The FCG experiments were carried out, and the AE signals emitted from FCG process were studied by AE parameters and Fast Fourier Transformed (FFT) spectra analysis. The AE results were also compared with the fracture surfaces. The results show that the AE technique is capable of acquiring and identifying brittle fracture events during FCG of rail steel.

In the use of metals, due to industrial advances and the application of more dynamic loads, it is necessary to pay more attention to the fatigue issue. Structural health monitoring (SHM) system is a method of evaluating and monitoring structural health. It has been widely applied in various engineering sectors due to its ability to respond to adverse structural changes, improving structural reliability and life cycle management. Non-destructive inspection methods are used to condition and health monitoring structures at the time of production and even during the service life of parts. Among non-destructive methods, the acoustic emission method has become a standard and reliable method in recent years. The stimulated internal energy of the structure is received in this acoustic emission technique as health monitoring features. A dominating attribute of the acoustic emission technique is its application ability in its loading condition. Therefore, it provides instant damage information within a short period of time. Thus, acoustic emission monitoring tests are often performed in the operating conditions of the structure. Acoustic emissions can inform us of the changes that occur before the final failure and prevent much financial and human damage. In this project, the characteristics of acoustic emission in the fatigue crack growth of aluminum alloy 2025 for online structural monitoring have been investigated and determined. Acoustic emission tests have been performed in two parts: bending fatigue test with the aim of initiation of fatigue cracks in aluminum alloy 2025 specimens and following tensile tests with the aim of growth of fatigue cracks. The acoustic emission signals and parameters sent by the acoustic emission sensor during both tests were received and recorded by the acoustic emission software. According to the received acoustic emission information, various diagrams are plotted. Analyzing the results from online acoustic emission monitoring showed the acoustic emission method can be considered a suitable and reliable technique for detecting crack initiation and crack growth in aluminum alloy 2025.

To the instability of acoustic emission (AE) signal of rock fracture, the method about feature extraction and comprehensive recognition of those was came up with combining AE parameters, EMD and BP neural network. Through the acoustic emission experiment of different brittle rock under uniaxial compression, stress-strain curve and AE data were obtained; time-frequency characteristics of AE signal of rock samples were compared. Feature vectors, like AE parameters, and EMD energy entropy, was synthesized to BP neural network to distinguish different AE signal. The results show that evolution characteristic with stress or time of AE parameters of different rock which was under uniaxial compression exist similarities and differences. EMD and Welch spectrum can reflect the difference among spectrum, energy distribution of AE signal of different rock very well. With various characteristics of different rock acoustic emission, the neural network has good recognition effect.

In the use of metals, due to industrial advances and the application of more dynamic loads, it is necessary to pay more attention to the fatigue issue. Non-destructive inspection methods are used to condition and health monitoring of structures at the time of production and even during the service life of parts. Among non-destructive methods, the acoustic emission method has become a standard and reliable method in recent years. In this project, the characteristics of acoustic emission in the fatigue crack growth of aluminum alloy 2025 for online structural monitoring have been investigated and determined. Acoustic emission tests have been performed in two parts: bending fatigue test with the aim of initiation of fatigue cracks in aluminum alloy 2025 specimens and following tensile tests with the aim of growth of fatigue cracks. The acoustic emission signals and parameters sent by the acoustic emission sensor during both tests were received and recorded by the acoustic emission software. According to the received acoustic emission information, various diagrams are plotted. Analyzing the results from online acoustic emission monitoring showed, the acoustic emission method can be considered as a suitable and reliable technique for detecting crack initiation and crack growth in aluminum alloy 2025.

Increased traffic speeds and axle loads on modern railways enhance rail track degradation. To eliminate track failure due to rail defects, a condition monitoring system requires methods for the early detection of defects which grow in service. Acoustic emission (AE) monitoring is the only non-destructive technique which might be applied online to study the defect growth under traffic loading. However, a high level of traffic noise and a limited signal from crack growth, especially at low crack growth rates, significantly complicate the AE signal analysis. In the present work, the AE monitoring of rail steel fatigue was carried out in a ‘noisy’ laboratory environment using different methods of signal analysis. Signal parameters of AE for machine noise, sample deformation and crack growth were identified. The crack growth related AE signature was found to be dependent on fracture mode.

Influence of micro structure on acoustic emission behavior during stage 2 fatigue crack growth in solution annealed, thermally aged and weld specimens of AISI type 316 stainless steel

Characterization of piezoelectric wafer active sensor for acoustic emission sensing

Acoustic Emission (AE) is a passive form of non-destructive testing that relies on the detection and analysis of stress waves released during crack propagation. AE techniques are successfully employed number of industries there remains some scepticism in aerospace engineering. The reported investigation details a single four point bend test specimen undergoing fatigue loading. This test is part of a much larger programme designed to demonstrate a technology readiness level (TRL) of five of the use of AE to detect crack initiation and growth in landing gear structures. The completed test required that crack growth had to be monitored to allow a comparison with the detected and located AE signals. The method of crack monitoring had to be non-contact so as not to produce frictional sources of AE in the crack region, preventing the use of crack mouth opening displacement gauges. Furthermore adhesives on the specimen surface had to be avoided to eliminate the possibility that the detected AE was from adhesive cracking, thus the use of strain gauges or foil crack gauges was not possible. A method using Digital Image Correlation (DIC) to monitor crack growth was investigated. The test was stopped during fatigue loading at 1000 cycle intervals and a DIC image captured at peak load. The displacement due to crack growth was observed throughout the investigation and the results compared with the detected AE signals. Results showed a clear correlation between AE and crack growth and added further evidence of TRL5 for detecting fractures in landing gears using AE.

Investigation of the relationship between internal fluid leakage through a valve and the acoustic emission generated from the leakage

This study was carried out to investigate the relationship between the strain and acoustic emission (AE) signals, thus, to confirm the capability of AE technique to monitor the fatigue failure mechanism of a steel component. To achieve this goal, strain and AE signals were captured on the steel specimen during the cyclic fatigue test. Both signals were collected using specific data acquisition system by attaching the strain gauge and AE piezoelectric transducer simultaneously at the specimen during the test. The stress loading used for the test was set at 600 MPa, and the specimens were fabricated using the SAE 1045 carbon steel. The related parameters for both signals were determined at every 2000 seconds until the specimen failed. It was found that a meaningful correlation of all parameters, i.e. amplitude, kurtosis and energy, was established. Finally, all AE parameters are correlated with the damage values, which have been estimated using the Coffin-Manson model. Hence, it was suggested that the AE technique can be used as a monitoring tool for fatigue failure mechanism in a steel component.

The acoustic emission (AE) monitoring technique is widely used in mechanical and materials research for detection of plastic deformation, fracture initiation and crack growth. However, the quantitative dependences of the AE signal parameters on material fracture parametersare not completely understood. This paper presents recent research results on AE monitoring of the fracture behavior of X80 line pipe steel, a critically important material for the oil and gas transportation industry.Fracture of this steel was studied using tensile testing of small scale specimens coupled with AE monitoring and high speed video camera. The dependence of fracture behavior and AE parameters on loading conditions (strain rate and presence or absence of a notch) was investigated. The AE parameters were analyzed using the “Average Hit” features and “Wave Form and Power Spectrum” methodologies. The fracture surface was characterized using scanning electron microscopy and a dependence of the AE parameters on the average void size has been obtained.

Taking acoustic emission (AE) parameters as the cut-in point, AE experiments were carried out for three-point bending beams with notch, the basic features of AE parameters were studied for the fracture process of concrete and reinforced concrete with different strength grades and different reinforcement ratios, through ringing counts rate and time curve, accumulative ringing counts and time curve, energy and time curve, accumulate energy and time curve, et al. The research result shows that the initial loads and the maximum loads of concrete three-point bending beams can be determined more accurately, by the reciprocal combination of ringing counts and energy curves. The AE parameters of concrete and reinforced concrete three-point bending beams are influenced by the initial design strength. As the reinforcement ratio changes, the fracture processes of reinforced concrete are different, and the corresponding AE signals are also different. The results of this work will provide the experimental basis to further study the fracture process of concrete.

Acoustic emission (AE) technique has been proved as a powerful technique for structural health monitoring. AE technique can also be efficiently used for evaluation of corrosion activity in concrete. Despite its advantages, an effective analysis of data recorded by AE technique still founds to be a challenging task demanding an appropriate damage assessment methodology. For quantification of damages, various methods for analysis of AE data have been proposed but not a single method has found to be standardized for specific application. In this paper, a procedure for analysis of AE signals is proposed using modified dimensional analysis method. Many times, it becomes difficult to choose the appropriate AE parameter which can be effectively co-related to the physical feature for development of accurate prediction model. Hence, in the present research work an attempt has been made to develop a model by incorporating primary characteristic AE waveform parameters. A corrosion rate prediction model using modified dimensional analysis of AE signals is developed and compared with the model developed using non-linear regression analysis. The performance of two models is further assessed using different statistical parameters. The study demonstrated that the methodology of modified dimensional analysis indicated improvement in the corrosion rate predictions. Thus, modified dimensional analysis can be implemented as a promising method for analysis of complex AE signal data as well as for development of statistical modelling of corrosion phenomenon in reinforced concrete based on recorded AE parameters.