Dynamic Self-Learning Neural Network and Its Application for Rotating Equipment RUL Prediction

Abstract

Current Internet of Things (IoT)-based equipment management methods often struggle with the diversity of data types and dynamic operating conditions, as fixed neural network structures and parameters lack the flexibility needed for adaptive feature extraction and fine-tuning, leading to suboptimal remaining useful life (RUL) predictions (PRs). To address the gap in current approaches, an innovative dynamic self-learning neural network (DSLNN) is proposed. Inspired by the human eye’s ability to adjust focus, the network introduces an adaptive scaling convolution (ASC) that dynamically adjusts the receptive field by stretching or shrinking, allowing for flexible feature extraction. Building on ASC, a spatiotemporal feature extraction module is developed to capture comprehensive equipment degradation features across both time and space dimensions. Additionally, a regression self-regulating mechanism is incorporated to facilitate flexible RUL inference, with a novel unbalanced tanh function that aligns with practical engineering needs. These innovations are integrated into DSLNN, which through experimental validation on the C-MAPSS, gear, and wind turbine gearbox bearing datasets, achieves state-of-the-art performance in RUL PR and enhances equipment reliability in IoT applications.