Tool life prediction is critical to workpiece quality and machining costs. In this paper, a remaining useful life prediction method was proposed, which considering the dimension optimization accuracy of vibration features and the iterative speed of industrial models. Firstly, an optimization method for extracting spatial information of vibration signals was found. The spatial topology of high dimensional vibration information was preserved, when achieving dimension reduction of high dimensional feature. Secondly, a determination method of loss function was proposed to ensure the extraction effects of spatial information. Meanwhile, the k value selection method was proposed which considered to balance the time efficiency factor by using the extraction effect of spatial information. Thirdly, an optimal strategy and model for the remaining useful life prediction of the tool which is suitable for industry was determined, in order to ensure the accuracy and increase the speed. Finally, the superiority and effectiveness of the proposed method were verified by using the processing data of practical tool life cycle.

At present, pipeline inspection gauge (PIG) is the main means to ensure the reliability of oil and gas transportation pipelines. Due to the unstable friction between the PIG and the pipeline, the failure and blockage of the PIG often occurs. In this paper, the theoretical modeling and experimental measurement of fluctuating friction on PIG are studied. Firstly, considering the coupling relationship between cylindrical shell and conical shell, the contact radial force model between PIG and pipeline is derived. Based on which, the nonlinear frictional model of PIG is proposed, and the parameters are measured by several experiments. Then an experimental platform is developed to measure the fluctuating friction force of PIG in actual operation, and verify the accuracy of the proposed model. The motion characteristic analysis and dynamics research are carried out, and the PIG creeping phenomenon caused by fluctuating friction is explained. Finally, based on the proposed nonlinear friction model, the influence of parameters is analyzed. The conclusions based on the proposed model and the experiments results can provide technical supports for practical engineering application.

The traditional predictive model for remaining useful life predictions cannot achieve adaptiveness, which is one of the main problems of said predictions. This paper proposes a LightGBM-based Remaining useful life (RUL) prediction method which considers the process and machining state. Firstly, a multi-information fusion strategy that can effectively reduce the model error and improve the generalization ability of the model is proposed. Secondly, a preprocessing method for improving the time precision and small-time granularity of feature extraction while avoiding dimensional explosion is proposed. Thirdly, an importance coefficient and a custom loss function related to the process and machining state are proposed. Finally, using the processing data of actual tool life cycle, through five evaluation indexes and 25 sets of contrast experiments, the superiority and effectiveness of the proposed method are verified.

With the advancement of condition monitoring technology, deep learning has gained widespread application in prognostics and health management (PHM). However, data-driven predictive methods often heavily rely on raw data leading to compromised accuracy under variable operating conditions and suffer from poor model transparency. Moreover, data-driven approaches may lead to inferences that do not align with physical laws. Poor generalization due to neglect of inherent degradation physics. To address these issues, this paper proposes a physical knowledge integrated Transformer network for RUL prediction. The proposed method employs a channel attention mechanism based on advanced signal processing techniques to learn the degradation process of the target unit from the frequency domain perspective, with the reconstructed features containing rich physical information. In addition, the irreversibility of the degradation process and variations in the degradation rate are viewed as prior knowledge, and a novel loss function is introduced to align the model’s predictions with actual degradation patterns. Experiments on bearing degradation datasets demonstrate the effectiveness and superiority of the proposed method.