A concise self-adapting deep learning network for machine remaining useful life prediction. (15th May 2023)

• Record Type: Journal Article
• Authors: Xiang, Sheng; Qin, Yi; Luo, Jun; Wu, Fei; Gryllias, Konstantinos
• Is Part Of: Mechanical systems and signal processing. Volume 191(2023)
• Publication Date: 2023-05-15
• Subjects: RUL prediction -- Adaptive learning -- Involution -- Multi-input -- Aero-engine -- Wind turbine; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2023.110187
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Most remaining useful life (RUL) prediction methods learn the feature using a single fixed pattern, resulting in a lack of self-adapting learning capability and a decrease in generalization and prediction accuracy. To address this issue, we propose a concise self-adapting deep learning network (CSDLN) for RUL prediction with fewer learnable parameters per sub-module. First, a multi-branch 1D involution neural network (MINN) is proposed to adaptively extract the hidden feature from the multi-input using the involution operation, which has inverse inherence with the convolution operation. Second, an adaptive learning algorithm called the multi-head gated recurrent unit (MGRU) is proposed to learn the hidden feature. Finally, the aero-engine RUL is determined by dimension reduction of the full connection (FC) layer and linear regression of the regression layer. Additionally, rather than using the ReLU activation function, the Mish activation function is used to strengthen the self-adapting deep learning ability in CSDLN. The performance in the Commercial Modular Aero Propulsion System Simulation (C-MAPSS) dataset and the real wind turbine gearbox bearing tests demonstrate the superiority of CSDLN over the state-of-the-art RUL prediction methods. Meanwhile, dropout is adopted in the model for avoiding overfitting and achieving the uncertainty quantification of RUL prediction in the two applications.