The use of multi-sensor information fusion techniques is essential for condition monitoring and prediction in large complex systems. In this paper, a new distributed model fusion method is proposed to predict the remaining useful life (RUL) of a nonlinear Wiener process. First, the state–space model of the nonlinear Wiener process is established, based on multi-sensor monitoring, and the distributed Kalman filtering algorithm is used to filter and fuse the measurement data received from multiple sensors. Next, the parameters and degradation states of the state–space model are estimated and updated online in real time using the expectation maximum and smoothing filter algorithms. Moreover, the distribution of the system’s RUL is obtained according to the estimated state–space model considering the random failure threshold factor. Finally, numerical experiments are conducted to elucidate the accuracy of the adopted distributed fusion method, and the adaptability and effectiveness of the proposed method are verified using the FD001 data of the C-MPASS dataset as an example.

Efficient mining machinery operation is essential for coal mining enterprises to reduce production costs, improve production efficiency, and maximize profits. As a key consumable part of mining machinery, the reliability of the pick directly determines the operational performance and service life of the machinery. During the mining process, the pick can be affected by natural wear and tear caused by the coal and rock and load shocks caused by the gangue and faults—that is, the result of the competing influence of soft failure caused by natural wear and tear and hard failure caused by random load shocks. At the same time, because the gangue and faults have a certain volume and hardness, a random load shock may last for a certain period of time, producing different acceleration effects on the pick wear under different hardness conditions. In this article, the reliability modeling of the pick competing failure process under random load shocks was studied by considering the influence of continuous shocks and the accelerated degradation at changing rates for different shock durations on the pick wear. First, the pick degradation model under random load shocks was established by considering the natural wear degradation, instantaneous shock degradation, and accelerated degradation at changing rates for different shock durations. Second, the pick reliability model under the competing failure mode was established on this basis. Finally, a numerical experiment and an effectiveness analysis of the pick reliability model were conducted based on the engineering data. The results showed that the competing failure reliability model of the pick considering continuous shocks and a changing degradation rate was more in line with engineering practice, which is of great significance for pick design optimization and improved reliability.

In dynamic operating environment, equipment degradation exhibits significant complexity and variability, which can adversely impact the accuracy of predicting the remaining useful life (RUL). A time-varying non-parametric kernel density estimation (KDE) strategy, integrated with an adaptive kernel auxiliary particle filter (AKAPF) is presented, which is used for monitoring the degraded status of systems and enhance RUL prediction. Firstly, an exponential weighted time-varying RUL prediction model is developed by employing non-parametric KDE in combination with a forgetting factor. Subsequently, the relative density between samples is computed using the k-nearest neighbor (KNN) approach, which facilitates the determination of sparsity and density, enabling the adaptive selection of kernel bandwidth. Next, AKAPF is employed to update the degradation state of the dynamic system and determine the forgetting factor ω, thereby achieving optimal adaptive time-varying weights. Through iterative updates and loops, this real-time prediction system effectively identifies the degradation state and assesses the RUL. Finally, numerical experiments, along with data from the NASA dataset and a high-temperature furnace case study are conducted to validate the robustness and practicality of the proposed model.

The health status assessment of industrial equipment is critical to fault prediction and health management technologies. To fully reflect system degradation and accurately predict the remaining useful life (RUL) of a system, the construction of multiple performance indicators is important. Thus, a new state-space model and RUL prediction method are proposed herein based on multiple indicators, considering the three correlation relationships among features and degradation indicators. First, according to the extracted and selected feature signals, various relationships such as the correlations among multiple features, multiple features and indicators, and multiple indicators are analyzed. Then an intuitive multi-indicator state-space model is established considering the three correlations and the identified degradation patterns. On this basis, three failure modes of the system are defined according to the multi-indicator complex correlation conditions, and the corresponding probability density function (pdf) of the system RUL is calculated. Finally, the PHM2012 Challenge and XJTU-SY datasets are used as examples for experimental verification. Compared with other prediction methods, the proposed method can intuitively reflect the correlation between features and indicators, to solve the problem caused by the hidden random correlation.