Remaining useful life prediction is one of the essential processes for machine system prognostics and health management. Although there are many new approaches based on deep learning for remaining useful life prediction emerging in recent years, these methods still have the following weaknesses: (1) The correlation between the information collected by each sensor and the remaining useful life of the machinery is not sufficiently considered. (2) The accuracy of deep learning algorithms for remaining useful life prediction is low due to the high noise, over-dimensionality, and non-linear signals generated during the operation of complex systems. To overcome the above weaknesses, a general deep long short memory network-based approach for mechanical remaining useful life prediction is proposed in this paper. Firstly, a two-step maximum information coefficient method was built to calculate the correlation between the sensor data and the remaining useful life. Secondly, the kernel principal component analysis with a simple moving average method was designed to eliminate noise, reduce dimensionality, and extract nonlinear features. Finally, a deep long short memory network-based deep learning method is presented to predict remaining useful life. The efficiency of the proposed method for remaining useful life prediction of a nonlinear degradation process is demonstrated by a test case of NASA’s commercial modular aero-propulsion system simulation data. The experimental results also show that the proposed method has better prediction accuracy than other state-of-the-art methods.

Reliable online condition monitoring is essential for maintaining process stability and guaranteeing machining quality in precision grinding. Against this background, this study proposes an electrostatic induction-based measurement strategy and further performs systematic comparisons with conventional force-based monitoring methods. First, an electrostatic sensor model is established to quantitatively characterize charge transfer behaviors during grinding interactions. A three-axis experimental grinding platform is deployed to correlate electrostatic responses and mechanical force signals with critical grinding variables, including grinding speed, depth of cut, feed rate, and wheel wear severity. To achieve quantitative and objective evaluation, a unit-free Dynamic Sensitivity Change Degree (DSCD) index is introduced. Comparative results based on the DSCD index reveal that electrostatic signals exhibit better performance than traditional force-based signals in tracking grinding wheel speed variations and progressive wear evolution under the tested conditions. Meanwhile, the DSCD fluctuation in electrostatic signals remains within 1% under varying cutting depths, indicating favorable linear stability within the scope of the present experiments. Furthermore, a Generalized Conditional Variational Auto-Encoder (G-CVAE) model is developed to augment insufficient wheel wear datasets. The generated synthetic signals exhibit high fidelity and enable accurate and robust classification of grinding wheel wear states. This study verifies the existence of explicit quantitative correlations between electrostatic induction signals and key grinding parameters as well as wheel wear conditions. The proposed monitoring method can provide high-quality, reliable data support for subsequent grinding condition assessment and intelligent process decision-making.