Xilang Tang

Predicting remaining useful life (RUL) is a crucial part of prognostics and health management (PHM), which has attracted widespread attention in academia and industry over the past few decades. Effective estimation of RUL is predicated on the development of a suitable degradation model for the system. However, most of the existing models require offline learning of a priori parameters, which is not applicable in the absence of historical data. To solve this problem, a stochastic degradation model based on the Lévy process is proposed in this study. The utilization of the Kalman filter and expectation–maximization in conjunction with Rauch–Tung–Striebel (KF-EM-RTS) enables the acquisition and real-time updating of model parameters and the probability density function (pdf) for RUL. This approach also allows for the characterization of parameter uncertainty. Simultaneously, the analytical expression of the RUL pdf can be obtained by the definition based on first hitting time (FHT) and Lévy–Khinchin formula. The high-speed computer numerical control (CNC) milling machine cutters dataset from the 2010 IEEE Data Challenge and solid-state RF power amplifier (SSRFPA) degradation data are utilized to validate the proposed model. The results of the experiments demonstrate that the proposed model is capable of delivering accurate RUL estimations.

Fault diagnosis is crucial for repairing aircraft and ensuring their proper functioning. However, with the higher complexity of aircraft, some traditional diagnosis methods that rely on experience are becoming less effective. Therefore, this paper explores the construction and application of an aircraft fault knowledge graph to improve the efficiency of fault diagnosis for maintenance engineers. Firstly, this paper analyzes the knowledge elements required for aircraft fault diagnosis, and defines a schema layer of a fault knowledge graph. Secondly, with deep learning as the main method and heuristic rules as the auxiliary method, fault knowledge is extracted from structured and unstructured fault data, and a fault knowledge graph for a certain type of craft is constructed. Finally, a fault question-answering system based on a fault knowledge graph was developed, which can accurately answer questions from maintenance engineers. The practical implementation of our proposed methodology highlights how knowledge graphs provide an effective means of managing aircraft fault knowledge, ultimately assisting engineers in identifying fault roots accurately and quickly.

When an aircraft malfunctions, quickly and accurately identifying the faulty unit is essential for ensuring normal operation. Unfortunately, maintenance engineers often struggle to acquire the necessary fault-related knowledge due to poor management and utilization of aircraft fault documents. To address this issue, we introduce knowledge graph technology into the field of aircraft fault diagnosis, exploring its construction and application for effective knowledge management. Our work starts by analyzing the critical knowledge elements required for aircraft fault diagnosis and designing a schema layer for fault knowledge graphs. We then we then combine deep learning and heuristic rules to extract fault knowledge from both structured and unstructured data, enabling the construction of aircraft fault knowledge graphs. Finally, we develop a fault question-answering system based on fault knowledge graphs that can accurately give solutions to questions posed by maintenance engineers. Our practice demonstrates that knowledge graphs provide an effective means of managing aircraft fault knowledge, assisting engineers in locating fault reasons accurately and quickly.

In order to make full use of aircraft fault information of total life cycle and construct a knowledge base for intelligent fault diagnosis, this paper preliminarily explored extracting fault knowledge from massive unstructured texts by using the event extraction technology. According to the business requirements of fault diagnosis, we defined the elements of fault events, including trigger words, and fault time, argument roles such fault time, fault occasion, fault unit, signal indicate. To extract above elements, three models, including fault event identification model, trigger extraction model and Argument extraction model is developed in this paper. The results show that this method is effective.

This paper proposes a method for estimating the health status of spacecraft key components based on the belief rule base (BRB), a semi-quantitative method which uses both human judgmental information and numerical data. It not only allows experts to establish rules to provide useful conclusions, but also allows historical data to train its parameters to obtain more accurate outputs. To balance the parameter training and experts’ knowledge, the Markov Chain Monte Carlo (MCMC) technique instead of traditional optimization method is used to adjust the BRB parameter. A practical case of estimating the health condition of space application batteries is studied.