Spectral noise and data reduction using a long short-term memory network for nonlinear ultrasonic modulation-based fatigue crack detection

Abstract

This paper presents a spectral noise and data reduction technique based on long short-term memory (LSTM) network for nonlinear ultrasonic modulation-based fatigue crack detection. The amplitudes of the nonlinear modulation components created by a micro fatigue crack are often very small and masked by noise. In addition, the collection of large amounts of data is often undesirable owing to the limited power, data storage, and data transmission bandwidth of monitoring systems. To tackle the issues, an LSTM network was applied to ultrasonic signals to reduce the noise level and the amount of data. The proposed technique offers the following benefits: (1) spectral noise reduction using the LSTM network for ultrasonic signals and (2) data reduction without compromising the spectral density amplitude of the existing nonlinear modulation components. Finally, the performance evaluation was conducted using the data obtained from complex geometry and real structure under external noises, indicating that the proposed method can be applied to various structures.