High accuracy key feature extraction approach for the non-stationary signals measurement based on NGO-VMD noise reduction and CNN-LSTM

• Published in: Measurement Science and Technology
• Main Authors: Xu, Fujing, Jing, Ruirui, Zhang, Yan, Liu, Qiang, Wu, Yimin A
• Other Authors: Open Fundation for the State Key Laboratory of Dynamic Testing Technology Jointly Built by Province and Ministry, Shanxi Scholarship Council of China, National Natural Science Foundation of China
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: IOP Publishing 2023
• Subjects: Northern Goshawk
• Online Access: http://dx.doi.org/10.1088/1361-6501/ad031c; https://iopscience.iop.org/article/10.1088/1361-6501/ad031c; https://iopscience.iop.org/article/10.1088/1361-6501/ad031c/pdf

Abstract The effective extraction of key features in non-stationary signals measurement is crucial in numerous engineering fields, including fault diagnosis, geological exploration, and state detection. To accomplish a more accurate and efficient extraction of key feature information from non-stationary signals, we design a novel approach based on variational mode decomposition (VMD) optimization by northern goshawk optimization (NGO) algorithm, convolutional neural network (CNN), and long short-term memory network (LSTM). First, NGO is used to optimize multiple intrinsic mode functions of VMD and reconstruct the signal according to the linear correlation method. Subsequently, the features of moving root mean square, moving kurtosis, and upper envelope are calculated, thereby constructing the feature matrix. Finally, the CNN-LSTM model is established with the chosen optimal hyperparameters prior to the training phase. The experimental results demonstrate that the proposed NGO-VMD-CNN-LSTM method, with a high accuracy reaching 98.22%, can more accurately extract the key information of typical non-stationary signals.