Xuewu Dai

From Model, Signal to Knowledge: A Data-Driven Perspective of Fault Detection and Diagnosis

This review paper is to give a full picture of fault detection and diagnosis (FDD) in complex systems from the perspective of data processing. As a matter of fact, an FDD system is a data-processing system on the basis of information redundancy, in which the data and humans understanding of the data are two fundamental elements. Humans understanding may be an explicit input-output model representing the relationship among the systems variables. It may also be represented as knowledge implicitly (e.g., the connection weights of a neural network). Therefore, FDD is done through some kind of modeling, signal processing, and intelligence computation. In this paper, a variety of FDD techniques are reviewed within the unified data-processing framework to give a full picture of FDD and achieve a new level of understanding. According to the types of data and how the data are processed, the FDD methods are classified into three categories: model-based online data-driven methods, signal-based methods, and knowledge-based history data-driven methods. An outlook to the possible evolution of FDD in industrial automation, including the hybrid FDD and the emerging networked FDD, are also presented to reveal the future development direction in this field.

Novel Parameter Identification by Using a High-Gain Observer With Application to a Gas Turbine Engine

In this study, a novel identification technique, that is high-gain observer-based identification approach, is proposed for systems with bounded process and measurement noises. For system parameters with abnormal changes, an adaptive change detection and parameter identification algorithm is next presented. The presented technique and algorithm are finally applied to the parameter identification of the gas turbine engine by using the recorded input data from the engine test-bed. The identified parameters and the response curves are desirable. The simulations have proved the effectiveness of the proposed procedure compared with the previous identification approach.

Kalman interpolation filter for channel estimation of LTE downlink in high-mobility environments

The estimation of fast-fading LTE downlink channels in high-speed applications of LTE advanced is investigated in this article. In order to adequately track the fast time-varying channel response, an adaptive channel estimation and interpolation algorithm is essential. In this article, the multi-path fast-fading channel is modelled as a tapped-delay, discrete, finite impulse response filter, and the time-correlation of the channel taps is modelled as an autoregressive (AR) process. Using this AR time-correlation, we develop an extended Kalman filter to jointly estimate the complex-valued channel frequency response and the AR parameters from the transmission of known pilot symbols. Furthermore, the channel estimates at the known pilot symbols are interpolated to the unknown data symbols by using the estimated time-correlation. This article integrates both channel estimation at pilot symbols and interpolation at data symbol into the proposed Kalman interpolation filter. The bit error rate performance of our new channel estimation scheme is demonstrated via simulation examples for LTE and fast-fading channels in high-speed applications.

Disturbance Attenuation in Fault Detection of Gas Turbine Engines: A Discrete Robust Observer Design

This study is motivated by the onboard fault detection of gas turbine engines (GTEs), where the computation resources are limited and the disturbance is assumed to be band-limited. A fast Fourier transformation (FFT)-based disturbance frequency estimation approach is proposed and performance indexes are improved by integrating such frequency information. Furthermore, in the left eigenvector assignment, both eigenvalues and free parameters are optimized. As illustrated in the application to the actuator fault detection of a GTE, significant improvements are achieved compared to the existing methods. By combining the frequency estimation and eigenvalue optimization, the main contribution of the paper is the reduction of the computation complexity and the avoidance of the local optimal solution due to fixed eigenvalues.

Wireless Communication Networks for Gas Turbine Engine Testing

A new trend in the field of Aeronautical Engine Health Monitoring is the implementation of wireless sensor networks (WSNs) for data acquisition and condition monitoring to partially replace heavy and complex wiring harnesses, which limit the versatility of the monitoring process as well as creating practical deployment issues. Augmenting wired with wireless technologies will fuel opportunities for reduced cabling, faster sensor and network deployment, increased data acquisition flexibility, and reduced cable maintenance costs. However, embedding wireless technology into an aero engine (even in the ground testing application considered here) presents some very significant challenges, for example, a harsh environment with a complex RF transmission channel, high sensor density, and high data rate. In this paper we discuss the results of the Wireless Data Acquisition in Gas Turbine Engine Testing (WIDAGATE) project, which aimed to design and simulate such a network to estimate network performance and derisk the wireless techniques before the deployment.

High-Gain Observer-Based Estimation of Parameter Variations With Delay Alignment

This technical note analyzes the estimation delay in a high gain observer, where the state estimates may lag behind the actual states due to the observers non-zero phase response. The technical note proves that, for a slowly time-varying system subject to bounded noises, the estimation delay depends on the observer gain, but is independent of the variations of system parameters. Rather than estimating the delay, a novel method is proposed to calculate the delay from the observers phase response. In terms of system identification, the delay is compensated by aligning other measurements with the lagged estimate so that they have the same lag. The simulation results of an aero engine model show significant improvements in estimation. On one hand, the proposed approach improves the estimation accuracy, and on the other hand, it removes the assumption of zero delay and gives a new insight into the high-gain observer design.

Zero assignment for robust H2/H∞ fault detection filter design

In practical engineering, it is inevitable that a system is perturbed by noise signals. Unfortunately, H infin /H infin filtering may fail to detect some faults when the noise distribution matrix are the same as the fault distribution matrix. In this paper, it is shown that the dynamic feedback gain of a dynamic filter introduces additional zeros to the filter, and both the filter poles and the additional zeros can be assigned arbitrarily. In order to attenuate band-limited noises, the zero assignment technique is used, and an optimal dynamic fault detection filtering approach is proposed by locating the zeros to the noise frequencies and optimizing the poles. Compared to other dynamic filter design approaches, the zero assignment technique gives a better tradeoff between more design freedom and computation costs. As shown in the simulation, a better noise attenuation and fault detection performance have been obtained. The zero assignment in multivariable fault detection filter design would be the main contribution of this paper.

Discrete-time Robust Fault Detection Observer design: A genetic algorithm approach

With the fast development of digital computers, more industrial processes are controlled by digital processors and there is an increasing demand for improving the system reliabilities. The robustness in model-based fault detection has received a lot of attention during the last two decades, and RFDO (robust fault detection observer) forms an important branch of fault detection. However, most of current research focuses on continuous-time domain and needs relative more computation. Further studies on DRFDO (discrete-time robust fault detection observer) are required. In this paper, a frequency weighted robustness index is proposed and a left-eigenvector assignment based DRFDO design method is presented. A genetic algorithm is applied to optimize such an observer. As illustrated in the simulation, a better disturbance attenuation and faster fault detection are achieved. The main contribution of this paper is that the disturbance is attenuated further by combining frequency dependent performance indices and genetic algorithms.

Zero Assignment for Robust Fault Detection Filter Design

In practical engineering, it is inevitable that a system is perturbed by noise signals. Unfortunately, H infin /H infin filtering may fail to detect some faults when the noise distribution matrix are the same as the fault distribution matrix. In this paper, it is shown that the dynamic feedback gain of a dynamic filter introduces additional zeros to the filter, and both the filter poles and the additional zeros can be assigned arbitrarily. In order to attenuate band-limited noises, the zero assignment technique is used, and an optimal dynamic fault detection filtering approach is proposed by locating the zeros to the noise frequencies and optimizing the poles. Compared to other dynamic filter design approaches, the zero assignment technique gives a better tradeoff between more design freedom and computation costs. As shown in the simulation, a better noise attenuation and fault detection performance have been obtained. The zero assignment in multivariable fault detection filter design would be the main contribution of this paper.

Dynamic modelling and Robust Fault Detection of a gas turbine engine

Dynamic modelling and fault detection play an important role in the condition monitoring of gas turbine engines (GTEs). Although system identification and robust fault detection observer (RFDO) have been studied intensively, on-board fault detection raises challenges. A fast identification and discrete observer design is required because of the limited computation ability. In this paper, an output error model is identified first and a discrete observer is designed to avoid the discrete-continuous conversion. With the aid of disturbance frequency estimation, an improved performance index and a fast left-eigenvector based robust observer design method are proposed. As illustrated in the application results, a better disturbance attenuation and fault detection performance have been achieved.