Ultrasonic Phased Array imaging is a key method for fast and reliable nondestructive testing of structures, especially when only one side of the part is accessible. Full matrix capturing (FMC) in combination with the total focusing method (TFM) provides a strong tool for ultrasonic imaging of structures with complex flaw patterns. However, still, operator needs to go through the generated images and manually check for the possible defects. One important task is to separate true and false indications, as some of them are noises or artifacts. Inspecting large structures with TFM Phased Array Imaging generates a huge amount of data which takes a significant time to go through them manually. In this work, we evaluate the possibility of using the neural network as an artificial intelligent toolbox to identify the defects. Using finite element method and an in-house developed TFM code, the phased array images are produced as the input to the neural network. The output of the neural network, target, is defined as the probability of defect existence. After generating TFM final images with different flaw patterns, the network was trained and evaluated based on the stochastic genetic algorithm learning method. This made the training feasible with limited provided data. Results indicate the great potential of machine learning for automatic or assisted defect recognition. The main challenge to pursuing a comprehensive and reliable machine learning toolbox, is to train the system with a satisfactory number of examples in different situations to ensure the final product is able to cover all possible conditions. It is concluded the proposed neural network model is capable of image pattern recognition with limited provided training data.
KEYWORDS: Sensors, Signal detection, Neural networks, Acoustic emission, Algorithm development, Acoustics, Machine learning, Data modeling, Data analysis, Signal generators
Boiler tubes in power plants develop defects including creep and thermal fatigue damage that can lead to fluid leakage over the operation period. Such leakage is the main cause of outages and power generation losses in thermal power plants. Therefore, early detection of leaks in boiler tubes is necessary to avoid more than 60% of boiler outages. To monitor and detect tube leaks in real-time, Acoustic Emission (AE) technique is widely used in power plants. A boiler tube leak could be detected using Average Signal Level (ASL) of the acquired AE signal using a network of sensors attached to the body of the boiler. Changes in ASL are proportional to the tube leakage; however, background signals generated by operating soot blowers bury the features which represent the tube leaks in the boiler and makes it nearly impossible to detect them automatically with established threshold methods. Soot blowers are used to remove the soot that is deposited on the tubes to maintain the efficacy and continuous operation of boilers. In this study, a bidirectional long short-term memory (LSTM) recurrent neural network (RNN) is developed to automatically detect tube leaks in power plant boilers. This detection method aims at identifying abnormal acoustic signals which differ from the reference/normal data that the system was trained with. The neural networks are trained on a sample boiler and the evaluation was done on the same boiler on the intervals with leak presence. Once the developed machine learning algorithm was tested with AE signals acquired from boiler tubes, the results show that this novel approach can detect anomalies in the signal levels as an indication of tube defects with an acceptable accuracy.
Quality assurance and structural integrity evaluation are the crucial parts of the successful design and service of additively manufactured (AM) components. Discontinuities and flaws in AM parts can affect the mechanical properties of a component during manufacturing and service. It is very important to identify the discontinuities in AM parts in terms of location, size, and geometrical properties using nondestructive testing (NDT) techniques. Existing research in both mechanical testing and nondestructive evaluation involves developing methods for characterizing and inspecting AM components as the use of such materials continues to rise. Although there exist relatively mature ultrasonic inspection techniques for defect detection, AM polymer components face the challenge of considerable internal inhomogeneities caused by the design and printing strategies. It has been shown that the ultrasonic signals are very sensitive to the material inhomogeneities, consequently the reflection/diffractions from the defects will be significantly influenced and defect detection will be very challenging. This work aims to present the potentials and challenges in ultrasonic detection of defects in polymer AM parts. Air-coupled ultrasonic tests to be demonstrated and followed by results and discussions. The role of porosity on detectability in the ultrasonic NDT tests is described and a possible way for attenuation assessment is demonstrated. Finally, the effect of AM part inhomogeneities on detection probability of seeded defects with different sizes and locations in AM parts is presented.
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