Fault detection and identification for dead reckoning system of mobile robot based on fuzzy logic particle filter

To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot,an integrative framework of particle filter detection and fuzzy logic diagnosis was devised.Firstly,an adaptive fault space is designed for recognizing both known faults and unknown faults,in corresponding modes of modeled and model-free.Secondly,the particle filter is utilized to diagnose the modeled faults and detect model-free fault according to the low particle weight and reliability.Especially,the proposed fuzzy logic diagnosis can further analyze model-free modes and identify some soft faults in unknown fault space.The MORCS-1 experimental results show that the fuzzy diagnosis particle filter(FDPF) combinational framework improves fault detection and identification completeness.Specifically speaking,FDPF is feasible to diagnose the modeled faults in known space.Furthermore,the types of model-free soft faults can also be further identified and diagnosed in unknown fault space.

Fault Detection of Networked Control Systems Based on Optimal Robust Fault Detection Filter

有可能比一个采样时期大的随机、未知的导致网络的延期的联网的控制系统(NCS ) 的差错察觉在这份报纸被学习。首先，导致网络的延期引起的影响被转变成为错误建模组织，然后存在连续时间域基于引用，模型被扩大到分离时间领域并且适用指责联网的控制系统的察觉的柔韧的差错察觉方法。建议方法能被 Matlab LMI 工具箱容易实现，并且它的表演被一个模拟例子最后评估。

Design of H_∞ robust fault detection filter for nonlinear time-delay systems

In this paper, the robust fault detection filter (RFDF) design problems are studied for nonlinear time-delay systems with unknown inputs. First, a reference residual model is introduced to formulate the RFDF design problem as an H∞ model-matching problem. Then appropriate input/output selection matrices are introduced to extend a performance index to the time-delay systems in time domain. The reference residual model designed according to the performance index is an optimal residual generator, which takes into account the robustness against disturbances and sensitivity to faults simultaneously. Applying robust H∞ optimization control technique, the existence conditions of the RFDF for nonlinear time-delay systems with unknown inputs are presented in terms of linear matrix inequality (LMI) formulation, independently of time delay. An illustrative design example is used to demonstrate the validity and applicability of the proposed approach.

Fault detection filter design for linear discrete time-varying systems with multiplicative noise

The problem of fault detection for linear discrete timevarying systems with multiplicative noise is dealt with.By using an observer-based robust fault detection filter（FDF） as a residual generator,the design of the FDF is formulated in the framework of H ∞ filtering for a class of stochastic time-varying systems.A sufficient condition for the existence of the FDF is derived in terms of a Riccati equation.The determination of the parameter matrices of the filter is converted into a quadratic optimization problem,and an analytical solution of the parameter matrices is obtained by solving the Riccati equation.Numerical examples are given to illustrate the effectiveness of the proposed method.

A Delay System Approach to Fault Detection Filter of Networked Control Systems

In this paper,the fault detection filter(FDF) design problem for networked control systems(NCSs) with both network-induced delay and data dropout is studied.Based on a new NCSs model proposed recently,an observer-based filter is introduced to be the residual generator and formulated as an H∞-optimization problem for systems with two successive delay components.By applying Lyapunov-Krasovskii approach,a new sufficient condition on stability and H∞ performance is derived for systems with two successive delay components in the state.A solution of the optimization problem is then presented in terms of linear matrix inequality(LMI) formulation,dependently of time delay.In order to detect the fault,the residual evaluation problem is also considered.An illustrative design example is employed to demonstrate the validity of the proposed approach.

H_-/H_∞ fault detection filter design for interval time-varying delays switched systems

The problem of the robust fault detection filter design for time-varying delays switched systems is considered in the framework of mixed H-/H∞. Firstly, the weighted H∞ performance index is utilized as the robustness performance, and the H- index is used as the sensitivity performance for obtaining the robust fault detection filter. Then a novel multiple Lyapunov-Krasovskii function is proposed for deriving sufficient existence conditions of the robust fault detection filter based on the average dwell time technique. By introducing slack matrix variable, the coupling between the Lyapunov matrix and system matrix is removed, and the conservatism of results is reduced. Based on the robust fault detection filter, residual is generated and evaluated for detecting faults. In addition, the results of this paper are dependent on time delays,and represented in the form of linear matrix inequalities. Finally,the simulation example verifies the effectiveness of the proposed method.

Fast Rate Fault Detection Filter for Multirate Sampled-data Systems

This paper focuses on the fast rate fault detection filter (FDF) problem for a class of multirate sampled-data (MSD) systems. A lifting technique is used to convert such an MSD system into a linear time-invariant discrete-time one and an unknown input observer (UIO) is considered as FDF to generate residual. The design of FDF is formulated as an H∞ optimization problem and a solvable condition as well as an optimal solution are derived. The causality of the residual generator can be guaranteed so that the fast rate residual can be implemented via inverse lifting. A numerical example is included to demonstrate the feasibility of the obtained results.

Detection of Bearing Faults Using a Novel Adaptive Morphological Update Lifting Wavelet

The current morphological wavelet technologies utilize a fixed filter or a linear decomposition algorithm, which cannot cope with the sudden changes, such as impulses or edges in a signal effectively. This paper pre- sents a novel signal processing scheme, adaptive morpho- logical update lifting wavelet （AMULW）, for rolling element bearing fault detection. In contrast with the widely used morphological wavelet, the filters in AMULW are no longer fixed. Instead, the AMULW adaptively uses a morphological dilation-erosion filter or an average filter as the update lifting filter to modify the approximation signal. Moreover, the nonlinear morphological filter is utilized to substitute the traditional linear filter in AMULW. The effectiveness of the proposed AMULW is evaluated using a simulated vibration signal and experimental vibration sig- nals collected from a bearing test rig. Results show that the proposed method has a superior performance in extracting fault features of defective roiling element bearings.

Frequency Loss and Recovery in Rolling Bearing Fault Detection

Rolling element bearings are key components of mechanical equipment. The bearing fault characteristics are a ected by the interaction in the vibration signals. The low harmonics of the bearing characteristic frequencies cannot be usually observed in the Fourier spectrum. The frequency loss in the bearing vibration signal is presented through two independent experiments in this paper. The existence of frequency loss phenomenon in the low frequencies, side band frequencies and resonant frequencies and revealed. It is demonstrated that the lost frequencies are actually suppressed by the internal action in the bearing fault signal rather than the external interference. The amplitude and distribution of the spectrum are changed due to the interaction of the bearing fault signal. The interaction mechanism of bearing fault signal is revealed through theoretical and practical analysis. Based on mathematical morphology, a new method is provided to recover the lost frequencies. The multi-resonant response signal of the defective bearing are decomposed into low frequency and high frequency response, and the lost frequencies are recovered by the combination morphological filter(CMF). The e ectiveness of the proposed method is validated on simulated and experimental data.

Fault tolerant navigation method for satellite based on information fusion and unscented Kalman filter

An effective autonomous navigation system for the integration of star sensor,infrared horizon sensor,magnetometer,radar altimeter and ultraviolet sensor is developed.The requirements of the integrated navigation system manager make optimum use of the various navigation sensors and allow rapid fault detection,isolation and recovery.The normal full fusion feedback method of federated unscented Kalman filter（UKF） cannot meet the needs of it.So a no-reset feedback federated Kalman filter architecture is developed and used in the autonomous navigation system.The minimal skew sigma points are chosen to improve the calculation speed.Simulation results are presented to demonstrate the advantages of the algorithm.These advantages include improved failure detection and correction,improved computational efficiency,and reliability.Additionally,its＇ accuracy is higher than that of the full fusion feedback method.

Design of parametric fault detection systems: An H-infinity optimization approach

Problems related to the design of observer-based parametric fault detection(PFD) systems are studied. The core of our study is to first describe the faults occurring in systemactuators, sensors and components in the form of additive parameter deviations, then to transformthe PFD problems into a similar additive fault setup, based on which an optimal observer-basedoptimization fault detection approach is proposed. A constructive solution optimal in the sense ofminimizing a certain performance index is developed. The main results consist of defining parametricfault detectability, formulating a PFD optimization problem and its solution. A numerical exampleto demonstrate the effectiveness of the proposed approach is provided.

Research on fault detection method for heat pump air conditioning system under cold weather

Building energy consumption accounts for nearly 40% of global energy consumption, HVAC （Heating, Ventilating, and Air Conditioning） systems are the major building energy consumers, and as one type of HVAC systems, the heat pump air conditioning system, which is more energy-efficient compared to the traditional air conditioning system, is being more widely used to save energy. However, in northern China, extreme climatic conditions increase the cooling and heating load of the heat pump air conditioning system and accelerate the aging of the equipment, and the sensor may detect drifted parameters owing to climate change. This non-linear drifted parameter increases the false alarm rate of the fault detection and the need for unnecessary troubleshooting. In order to overcome the impact of the device aging and the drifted parameter, a Kalman filter and SPC （statistical process control） fault detection method are introduced in this paper. In this method, the model parameter and its standard variance can he estimated by Kalman filter based on the gray model and the real-time data of the air conditioning system. Further, by using SPC to construct the dynamic control limits, false alarm rate is reduced. And this paper mainly focuses on the cold machine failure in the component failure and its soft fault detection. This approach has been tested on a simulation model of the ＂Sino-German Energy Conservation Demonstration Center＂ building heat pump air-conditioning system in Shenyang, China, and the results show that the Kalman filter and SPC fault detection method is simple and highly efficient with a low false alarm rate, and it can deal with the difficulties caused by the extreme environment and the non-linear influence of the parameters, and what＇s more, it provides a good foundation for dynamic fault diagnosis and fault prediction analysis.

On Optimal Fault Detection for Discrete-time Markovian Jump Linear Systems

这份报纸为分离时间的 Markovian 处理差错察觉的问题跳线性系统(MJLS ) 。把一个基于观察员的差错察觉过滤器(FDF ) 用作一个剩余发电机， FDF 的设计为最大化随机的 H/H 或 H/H 表演索引作为一个优化问题被提出。在一个操作符优化方法的帮助下，一个统一最佳的解决方案能被解决 a coupled Riccati 方程导出，这被显示出。数字例子被给显示出建议方法的有效性。

Optimal fault detection for a class of discrete-time switched linear systems

This paper deals with the problem of optimal fault detection filter （FDF） design for a class of discrete-time switched linear systems under arbitrary switching. By using an observer-based FDF as a residual generator, the design of the FDF is formulated into an optimization problem through maximizing the H_/H∞ or H∞/H∞ performance index. With the aid of an operator optimization method, it is shown that a mode-dependent unified optimal solution can be derived by solving a coupled Riccati equation. A numerical example is given to show the effectiveness of the proposed method.

A New UKF Based Fault Detection Method in Non-linear Systems

To detect the bias fault in stochastic non-linear dynamic systems, a new Unscented Kalman Filtering（UKF） based real-time recursion detection method is brought forward with the consideration of the flaws of traditional Extended Kalman Filtering（ EKF）. It uses the UKF as the residual generation method and the Weighted-Sum Squared Residual （WSSR） as the fault detection strategy. The simulation results are provided which demonstrate better effectiveness and a higher detection ratio of the developed methods.

Robust fault detection for discrete-time Markovian jump systems with mode-dependent time-delays

This paper investigates a fault detection problem for a class of discrete-time Markovian jump systems with norm-bounded uncertainties and mode-dependent time-delays. Attention is focused on constructing the residual generator based on the filter of which its parameters matrices are dependent on the system mode, that is, the fault detection filter is a Markovian jump system as well. The design of fault detection filter is reduced to H-infinity filtering problem by using H-infinity control theory, which can guarantee the difference between the residual and the fault （or, more generally weighted fault） as small as possible in the context of enhancing the robustness of residual to modeling errors, control inputs and unknown inputs. Sufficient condition for the existence of the above filters is established by means of linear matrix inequalities, which can be readily solved by using standard numerical software. A numerical example is given to illustrate the feasibility of the proposed method.

Design of Heading Fault-Tolerant System for Underwater Vehicles Based on Double-Criterion Fault Detection Method

This paper proposes a heading fault tolerance scheme for operation-level underwater robots subject to external interference.The scheme is based on a double-criterion fault detection method using a redundant structure of a dual electronic compass.First,two subexpansion Kalman filters are set up to fuse data with an inertial attitude measurement system.Then,fault detection can effectively identify the fault sensor and fault source.Finally,a fault-tolerant algorithm is used to isolate and alarm the faulty sensor.The program can effectively detect the constant magnetic field interference,change the magnetic field interference and small transient magnetic field interference,and conduct fault tolerance control in time to ensure the heading accuracy of the system.Test verification shows that the system is practical and effective.

Model-Based Fault Detection of a Battery System in a Hybrid Electric Vehicle

Recently, a new type of IMM （interacting multiple model） method was introduced based on the relatively new SVSF （smooth variable structure filter）, and is referred to as the IMM-SVSF. The SVSF is a type of sliding mode estimator that is formulated in a predictor-corrector fashion. This strategy keeps the estimated state bounded within a region of the true state trajectory, thus creating a stable and robust estimation process. The IMM method may be utilized for fault detection and diagnosis, and is classified as a model-based method. In this paper, for the purposes of fault detection, the IMM-SVSF is applied through simulation on a simple battery system which is modeled from a hybrid electric vehicle.

固有成分滤波器的旋转机械故障诊断方法

针对噪声环境下旋转机械微弱复合故障诊断问题,提出了一种强噪声干扰下基于固有成分滤波器(intrinsic component filtering,简称ICF)的旋转机械故障检测和分离方法。ICF通过最小化样本间特征的L1/2范数和样本内特征的L3/2范数来实现样本之间特征的一致性和样本内部特征的稀疏性,并训练出最优滤波器组,是一种无监督多维盲解卷积算法。首先,构建输入信号的Hankel训练矩阵,通过权值矩阵与Hankel矩阵的乘积模拟卷积过程,再利用固有属性滤波器实现特征学习;其次,通过峭度信息选择最优滤波器;最后,根据滤波后的时域波形和包络谱实现故障诊断。仿真和试验信号验证了提出方法的故障诊断性能,研究结果表明,提出的方法无需任何先验经验,可以实现强噪声环境下的微弱故障的分离,同时具备很好的鲁棒性。

基于声发射技术的长距离天然气管道泄漏点定位方法

为了准确定位泄漏点位置,及时消除安全隐患,提出基于声发射技术的长距离天然气管道泄漏点定位方法。通过声发射技术采集天然气管道泄漏信号,提取泄漏声发射信号的时域特征,根据天然气管道泄漏信号时域幅值变化,判断管道泄漏情况,利用自适应时延估计定位法确定天然气管道泄漏点,通过自适应滤波器最小均方差调节含有噪声的泄漏信号,当自适应滤波器参数达到稳定状态后,泄漏信号传递到传感器的误差越小,得到更准确时延估计值,增加定位该管道泄漏点的准确度。实验结果表明:所提方法能够准确地定位到长距离天然气管道泄漏点所在位置,传感器间距离越小,定位精度越高。