Automatic Boiler Tube Leak Detection With Deep Bidirectional Lstm Neural Networks Of Acoustic Emission Signals

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.