Ningyun Lu

Incremental locally linear embedding-based fault detection for satellite attitude control systems

Abstract This paper presents a novel fault detection method based on incremental locally linear embedding (I-LLE) to improve the accuracy of fault detection for a satellite with high-dimensional telemetry data. Firstly, the I-LLE algorithm is introduced, followed by an application on Satellite “TX-1” telemetry to extract the low-dimensional features, which can be used to perform fault detection with statistical indexes. A rapid semi-physical platform for satellite attitude control systems based on PC104 and AD7011-EVA is designed to perform fault simulation, because limited telemetry contains fewer fault patterns, which renders fault simulation for in-orbit satellites difficult. The I-LLE-based fault detection scheme is then employed to detect anomalies in simulation data. Simulation results presented in this paper validate the fault detection scheme.

ToMFIR-based incipient fault detection and estimation for high-speed rail vehicle suspension system ☆

Abstract High requirements on safety and reliability of the high-speed railway vehicle suspension (HRVS) system demand accurate detection and estimation of various incipient faults as early as possible under closed-loop control configurations. Firstly, a dynamic model of the suspension system for a three-car CRH2 multiple unit is set up, under which the external disturbances (i.e., track irregularities) and the actuator faults are further modeled. Then, an improved total measurable fault information residual (ToMFIR) based fault detection and estimation method is proposed, in which the restriction on fault type in the original method is removed. Finally, the obtained theoretical results are applied to a CRH2 HRVS simulation system. Results show that the proposed method can detect and estimate several frequent incipient faults effectively, outperforming the conventional observer-based or output residual-based fault detection and estimation methods.

Data-Driven Incipient Sensor Fault Estimation with Application in Inverter of High-Speed Railway

Incipient faults in high-speed railway have been rarely considered before developing into faults or failures. In this paper, a new data-driven incipient fault estimate (FE) methodology is proposed under multivariate statistics frame, which incorporates with Kullback-Leibler divergence (KLD) in information domain and neural network approximation in machine learning. By defining one sensitive fault indicator (SFI), the incipient fault amplitude can be precisely estimated. According to the experimental platform of China Railway High-speed 2 (CRH2), the proposed incipient FE algorithm is examined, and the more sensitivity and accuracy to tiny abnormality are demonstrated. Followed by the incipient FE results, several factors on FE performance are further analyzed.

Bayesian Network Based Fault Prognosis via Bond Graph Modeling of High-Speed Railway Traction Device

Reliability of the traction system is of critical importance to the safety of CRH (China Railway High-speed) high-speed train. To investigate fault propagation mechanism and predict the probabilities of component-level faults accurately for a high-speed railway traction system, a fault prognosis approach via Bayesian network and bond graph modeling techniques is proposed. The inherent structure of a railway traction system is represented by bond graph model, based on which a multilayer Bayesian network is developed for fault propagation analysis and fault prediction. For complete and incomplete data sets, two different parameter learning algorithms such as Bayesian estimation and expectation maximization (EM) algorithm are adopted to determine the conditional probability table of the Bayesian network. The proposed prognosis approach using Pearl’s polytree propagation algorithm for joint probability reasoning can predict the failure probabilities of leaf nodes based on the current status of root nodes. Verification results in a high-speed railway traction simulation system can demonstrate the effectiveness of the proposed approach.

Data mining-based flatness pattern prediction for cold rolling process with varying operating condition

Data-rich environments in modern rolling processes provide a great opportunity for more effective process control and more total quality improvement. Flatness is a key geometrical feature of strip products in a cold rolling process. In order to achieve good flatness, it is necessary to reveal the factors that often influence the flatness quality, to develop a general flatness pattern prediction model that can handle the varying operating condition during the rolling of products with different specifications and to realize an effective flatness feedback control strategy. This paper develops a practical data mining-based flatness pattern prediction method for cold rolling process with varying operating condition. Firstly, the high-dimensional process measurements are projected onto a low-dimensional space (i.e., the latent variable space) using locality preserving projection method; at the same time, the Legendre orthogonal polynomials are used to extract the basic flatness patterns by projecting the high-dimensional flatness measurements into several flatness characteristic coefficients. Secondly, a mixture probabilistic linear regression model is adopted to describe the relationships between the latent variables and the flatness characteristic coefficients. Case study is conducted on a real steel rolling process. Results show that the developed method has not only the satisfactory prediction performance, but good potentials to improve process understanding and strip flatness quality.

Multi-mode kernel principal component analysis–based incipient fault detection for pulse width modulated inverter of China Railway High-speed 5:

This article deals with incipient fault of insulated-gate bipolar transistors to improve the safety of traction systems of China Railway High-speed 5. Combining with the pulse width modulated strategy which makes signals variate periodically, the multi-mode kernel principal component analysis algorithm is proposed. It can effectively not only capture the tiny changes caused by incipient faults but also detect short-faults of insulated-gate bipolar transistors in electrical systems. In feature space of every mode, different thresholds will be formed corresponding to defined modes. The proposed scheme is tested in experimental setup of traction system of China Railway High-speed 5 with incipient fault and short-circuit fault, and experimental results show that the multi-mode kernel principal component analysis has superior monitoring performance compared to other five methods.

Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices☆

This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach.

Fault prognosis for process industry based on information synchronization

Abstract Information delay problem connected with the nature of flowing information in process industry is studied. An information synchronization technique is proposed using time-delayed mutual information, to determine the informations directionality and time delays between process variables. Signed Digraph model is derived to represent the informations directionality and lags. A principle component analysis (PCA) based fault prognosis method is finally proposed to achieve early detection of plant-wide process abnormalities. The proposed method was applied to an air separation unit and achieved satisfying results in predicting the frequently occurred nitrogen-block faults.

A FDD method by combining transfer entropy and signed digraph and its application to air separation unit

A fault detection and diagnosis (FDD) method is proposed by combining transfer entropy (TE) and signed digraph (SDG). Given process historical data, transfer entropy is used to construct a SDG model to represent causal relationship between process variables. A fault severity evaluation method is then proposed based on the modified SDG model, where the nodes can take values of (0), (±1), (±3) and (±6). Then, an index named DoF is developed to measure fault severity. The application results can verify the effectiveness and feasibility of the proposed method.

The residual life prediction of the satellite attitude control system based on Petri net

Residual life is widely recognized as the time period that the system can work normally from the current operating conditions until broken. As an important means of system health evaluation, the residual life prediction has become a research hotspot at home and abroad. The residual life of the satellite attitude control system is affected by a variety of factors. In this paper, the concept of degraded state or partial failure is introduced to analyze the influence caused by different failures of the system thoroughly. Five states are defined in this paper numbered from 1 to 5 in consideration of the severity of the failure. There are transitions between states, whose conditional probability can be estimated through Kaplan-Meier estimator and then fitted as parametric Weibull distribution via the Maximum Likelihood Estimation (MLE) approach. The states and the transitions constitute the Petri net model of the satellite attitude control system, on which the residual life can be predicted through a large number of simulations. In the end of the paper, some simulations of specific satellite attitude control system are made given the state of the system, from which we can predict the residual life of the system and the possible state the system may transition to in advance. In this paper, we explore the residual life prediction method in terms of the satellite attitude control system, which will be helpful for making effective mission plans for satellites on orbit.