A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions

While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case.

Experimental verification of lithium-ion battery prognostics based on an interacting multiple model particle filter

A new data-driven prognostic method based on an interacting multiple model particle filter (IMMPF) is proposed for use in the determination of the remaining useful life (RUL) of lithium-ion (Li-ion) batteries and the probability distribution function (PDF) of the uncertainty associated with the RUL. An IMMPF is applied to different state equations. The battery capacity degradation model is very important in the prediction of the RUL of Li-ion batteries. The IMMPF method is applied to the estimation of the RUL of Li-ion batteries using the three improved models. Three case studies are provided to validate the proposed method. The experimental results show that the one-dimensional state equation particle filter (PF) is more suitable for estimating the trend of battery capacity in the long term. The proposed method involving interacting multiple models demonstrated a stable and high prediction accuracy, as well as the capability to narrow the uncertainty in the PDF of the RUL prediction for Li-ion batteries.

Preparation of Electrosprayed, Microporous Particle Filled Layers

Polytetrafluoroethylene (PTFE) is a synthetic fluoropolymer known for its excellent hydrophobic properties. In this work, samples from PTFE dispersions with different combinations of water and carbon microparticles were prepared using an electrospraying method. The morphologies and sizes of carbon particles were investigated and the properties of layers including roughness, hydrophobicity and electrical resistivity were investigated. The non-conductive carbon microparticles were selected as a model particle to check the compatibility and electrospraying ability, and it had no effect on the hydrophobic and electrical properties. Carbon microparticles in polymer solution increased the degree of ionization and was found to be beneficial for the shape control of materials. The results showed that PTFE dispersion with the composition of water and carbon microparticles produced fine sphere particles and the layer fabricated with increased roughness. It was also found that the electrical resistivity and hydrophobicity of all the layers comparatively increased. The fabricated microporous layers can be used in various applications like interlining layer in multilayer textile sandwiches.