Haiyin Zhou

Data-driven diagnosing for unanticipated fault by a general process model

The improvement of unanticipated fault detection and diagnosis (UFDD) capability is a difficult point, and is also a tendency for research and application. In this paper, a general process model (GPM) for unanticipated fault diagnosis is established. And combined with the characteristics of monitoring data, the corresponding diagnosis methods are researched. The model and the methods are used for online unanticipated fault detection, isolation and recognition. The GPM for the unanticipated fault diagnosis is designed, by adopting a three-layer progressive structure, which is comprised of an inherent detection layer (IDL), an unanticipated isolation layer (UIL) and an unanticipated recognition layer (URL). Several key problems in the GPM are analyzed, including the establishment and evaluation of detection statistics, the extraction of fault feature direction, and the design of fault isolation criterion and the calculation of contribution factor. The proposed model and methods are driven by pure data and they can detect and diagnose the unanticipated fault. The proposed approach is evaluated by using an example of a satellites attitude control system, and excellent results have been obtained.

Fault diagnosis for satellite's attitude determination system based on model error prediction and EMD

As one of the main parts for a satellites attitude measure and control system, star trackers and gyros tend to be damaged in the actual on-orbit operation. That often makes the satellite fail to finish the given task. In this paper, an approach is proposed to detect, identify and diagnose the fault for gyro and star tracker in a satellites attitude determination system. The model errors, including the model uncertainty and the fault factors, are estimated by predictive filter based on a satellites attitude kinematics equation firstly. And then the estimated model error is decomposed into some intrinsic mode functions (IMF) and one trend component by the empirical mode decomposition (EMD). Furthermore, the fault model for the trend component is established by the time series analysis. And the time series models parameters and residual variance are extracted as the feature vector, which is used to construct the discriminant function for the fault diagnosis of gyro and star tracker. Finally, promising simulated experimental results demonstrate the validity and effectiveness of the proposed method. Compared with the traditional fault diagnosis method, the detection and identification precision of the approach are improved significantly.

Maximum Correntropy Unscented Kalman Filter for Ballistic Missile Navigation System based on SINS/CNS Deeply Integrated Mode

Strap-down inertial navigation system/celestial navigation system (SINS/CNS) integrated navigation is a high precision navigation technique for ballistic missiles. The traditional navigation method has a divergence in the position error. A deeply integrated mode for SINS/CNS navigation system is proposed to improve the navigation accuracy of ballistic missile. The deeply integrated navigation principle is described and the observability of the navigation system is analyzed. The nonlinearity, as well as the large outliers and the Gaussian mixture noises, often exists during the actual navigation process, leading to the divergence phenomenon of the navigation filter. The new nonlinear Kalman filter on the basis of the maximum correntropy theory and unscented transformation, named the maximum correntropy unscented Kalman filter, is deduced, and the computational complexity is analyzed. The unscented transformation is used for restricting the nonlinearity of the system equation, and the maximum correntropy theory is used to deal with the non-Gaussian noises. Finally, numerical simulation illustrates the superiority of the proposed filter compared with the traditional unscented Kalman filter. The comparison results show that the large outliers and the influence of non-Gaussian noises for SINS/CNS deeply integrated navigation is significantly reduced through the proposed filter.

Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise

As one of the most critical issues for target track, α -jerk model is an effective maneuver target track model. Non-Gaussian noises always exist in the track process, which usually lead to inconsistency and divergence of the track filter. A novel Kalman filter is derived and applied on α -jerk tracking model to handle non-Gaussian noise. The weighted least square solution is presented and the standard Kalman filter is deduced firstly. A novel Kalman filter with the weighted least square based on the maximum correntropy criterion is deduced. The robustness of the maximum correntropy criterion is also analyzed with the influence function and compared with the Huber-based filter, and, moreover, the kernel size of Gaussian kernel plays an important role in the filter algorithm. A new adaptive kernel method is proposed in this paper to adjust the parameter in real time. Finally, simulation results indicate the validity and the efficiency of the proposed filter. The comparison study shows that the proposed filter can significantly reduce the noise influence for α -jerk model.

D34MB - a FDI toolbox with new features

A toolbox is developed in the MATLAB/GUI environment for FDI (Fault Detection and Isolation) which is an important field of automatic control theory and engineering. It provides an interactive interface for FDI. In this GUI (Graphical User Interface), a variety of methods are available for the FDI tasks, e.g., the traditional data-driven and model-based methods. a new feature of it is D34MB (Data Driven Design for Model Based) fault diagnosis. Some other new methods, e.g., robust, adaptive and local approach are also implemented. The sufficient benchmarks toolbox and the GUI feature ensure user-friendliness for operating this toolbox. Among all the methods, D34MB is emphasized, which is why the whole toolbox is called D34MB FDI toolbox. The new feature for static and dynamic model, the design, and the application of the toolbox is illustrated in this paper.

An Improved Detection Statistic for Systems with Unsteady Trend

Abstract The object of this paper is to address data-driven fault detection design for systems with unsteady trend, which shows cyclicity, monotonicity and non-zero mean. Firstly, mean theorem and covariance theorem are proposed and proved. The former is the mean property of projection matrix, and the latter is the recursive formula for covariance matrix of regression residual. Secondly, an improved fault detection statistic, called Least Square T 2 (LST 2 ), is proposed. It can partly solve the detection problem for systems with unsteady trend. The improvement can also partly cope with the limitations of the traditional multivariate detection methods, such as Principal Component Analysis (PCA). Thirdly, based on the two theorems, a recursive algorithm and a moving window algorithm of LST 2 are given, thus both time and space complexity are greatly reduced for online detection. The effectiveness of the presented detection statistic is evaluated with an application of monitoring satellite attitude control system. The case study result shows that the false alarm rate of LST 2 is much lower than that of T 2 based on PCA, while LST 2 is more sensitive to fault.

Robust diagnosis framework based on sliding least squares estimate and directional discrepancy

Proposed in this paper is a diagnosis framework based on SLSE (Sliding Least Squares Estimate) and directional discrepancy. This framework can be used when linear dynamic equation and beforehand baseline data are not available. It is efficient, for SLSE works by recursive computation, removing the oldest datum while accepting the newest. Its computational complexity is low and asks for little memory space. Detection statistic is given by the prediction residual and isolation statistic by directional discrepancy. Both detection and isolation criterion are based on hypothesis-testing procedure. Another feature of the framework is that it can diagnosis unanticipated fault, which has never happened before, without wrongly assigning it as some anticipated. The proposed method is especially fit for real-time diagnosis of stochastic system without accurate dynamic equation or complete fault patterns. The application to fault diagnosis of satellite control system demonstrates its validity.

A new method for isolating faults in the nonstationary and nonlinear processes

The objective of this paper is to address a new method based on trend extraction for isolating faults in the nonstationary and nonlinear processes. Firstly, a concise review of the traditional methods for fault isolation based on Hotelling statistic are introduced, a rigorous analysis of their weaknesses, especially the smearing (coupling) phenomena, is provided, and the possible handling strategies are given. Secondly, a new contribution index for isolation is proposed based on the improved detection statistic, iT2, and its properties are analyzed. Finally, the effectiveness of the new contribution method is validated by a nonstationary and nonlinear numerical case. Also it is used for monitoring the satellite attitude control system. The results show that the new contribution method can cope with the smearing phenomena of the traditional contribution indexes.

Research on prediction model and method for equipment state and fault in test range

The state and the fault prediction for the measure & control equipment in the test range plays an important role in the test and evaluation for the large-scale weapon system. It is also a research development in the state or fault process region. The objective of this paper is to address a state or fault prediction method for the equipment in the test range, which is combined with the physical network relationship for the predicted equipment and the mathematical analysis for the monitored state. According to the life rules of the electronic parts and the components of the predicted equipment, a distribution function for the abnormality state is constructed, and then the state prediction method for the discrete variables is especially researched. The effectiveness of the proposed prediction model and method is validated by a large optical system. The state or fault prediction model can realize the long-distance manage & control for the test ranges equipment, and can also provide the technical support for enhancing the equipment reliability during the test task.

Autonomous navigation algorithms based on improved CKF filters

Considering the great effects the measurement system errors of sensors have on satellite autonomous navigation, we study the filter algorithm for the navigation system with constant system error in this paper to improve the navigation precision. this paper improves conventional CKF algorithm and proposes an adaptive CKF filters based on model compensation, the state and bias estimation is performed separately using the CKF filters, and then the state variables are updated using the estimated bias. Finally, we verify the feasibility of the improved algorithm by simulation experiment, the simulation results show that the algorithm can improve the navigation accuracy.