一种感应电机转子断条早期故障诊断方法

针对感应电机转子发生早期断条故障时,定子电流故障特征频率容易被基波频率淹没,导致电机转子断条故障发现不及时的问题,采用多回路方程建立感应电机发生转子断条故障的模型,推导了dq0坐标系下感应电机发生早期断条故障的模型。在该故障模型的基础上,利用参数辨识和滑窗技术得到电机等效参数变化曲线。该方法得到的电机等效参数曲线对转子早期断条故障具有明显的故障特征信号,通过辨识曲线特征可诊断电机转子断条故障,同时给出了转子发生不同程度故障时的差异指标。最后试验验证了该方法的可行性。     

一种变频电机参数离线辨识方法的研究

传统电机参数辨识采用固定脉冲宽度调制(PWM)波占空比。该方法导致电流大小不可控,而辨识电流大小又直接影响辨识准确性。为此,提出新的参数离线辨识方法,采用恒电流辨识原理并检测电机相电流,控制辨识电流在给定值附近波动,解决了电机辨识电流不合适问题。设计了续流二极管和IGBT电压模型,d轴和q轴电感辨识都采用在270°而非0°进行辨识,以此提高了辨识精度。最后,在变频冰箱上进行验证并与原来数据进行对比。该方法很好地解决了冰箱电机带背压条件下,电机d、q轴电感辨识不准确、辨识时间长等问题,试验结果证明了该方法的可行性和有效性。     

结合统计的模型辨识在发动机故障诊断中应用

以一个双轴分排涡扇发动机为例,研究了数理统计和模型辨识组合方法诊断发动机双故障的可行性和效果.模型辨识法基于发动机性能仿真模型,在辨识的过程中利用了发动机平衡技术,考虑了模型的非线性.运用数理统计方法对辨识法得到的结果进行再处理,弥补了单纯以拟合程度为标准进行故障隔离的不足,定义和选择合适的故障指标,可以在多重故障模型下得到良好的故障隔离效果.      

基于LMI的一类非线性离散动态系统的鲁棒故障诊断

 针对一类非线性不确定离散动态系统 ,提出了一种新型鲁棒故障诊断方法。该方法不但能够对被诊断系统进行故障检测 ,而且同时能够实现故障的分离和辨识。它首先通过构造一个辅助系统 ,将故障的辨识问题转化为min max问题 ,并且通过巧妙设计辅助系统的输出增益矩阵 ,使得辅助系统与被诊断系统的状态差值方程和输出差值方程稳定 ,然后将min max问题转化成LMI问题 ,最后通过求解LMI问题来实现故障诊断。分析了故障诊断方法的鲁棒性、灵敏度和故障的辨识误差。仿真结果验证了该方法的有效性。     

基于扩展卡尔曼滤波的动量轮故障检测方法

 动量轮作为卫星姿态控制系统的关键执行部件,对其故障检测对维持卫星的正常运行具有重要意义。首先对动量轮系统的故障进行分析,在建立动量轮线性离散状态空间模型的基础上,把动量轮的故障检测作为时变参数系统的跟踪来处理,将动量轮的模型参数作为扩展的状态空间中的状态量,使得动量轮物理模型参数与状态空间中的状态量有对应关系,通过在扩展了的状态空间上采用扩展的卡尔曼滤波,完成时变参数的跟踪。然后,将离散空间的状态量变换回连续空间中,利用物理参数与状态量的对应关系,实现对动量轮物理参数的跟踪。此方法物理意义明确,为系统的物理参数提供了定量的估计值,为进一步诊断故障原因提供了良好的基础。数值仿真表明,此方法能够通过同时检测多个故障参量,实现故障的检测并满足卫星实时性要求。     

舵面损伤的气动模型及故障检测研究

针对飞机舵面损伤故障检测和诊断的气动模型和飞行状态限制，提出了在线故障模式预测方法，并使用某机的故障及正常风洞数据建立了舵面损伤非线性气动模型。最后，仿真验证了该方案的气动模型独立性、飞行状态及操纵的适应性。结果表明，该方法可提高自修复飞控系统中舵面损伤故障检测算法的适用性和工程应用能力。     

基于模糊多模型匹配与辨识的伪逆重构控制

将多模型匹配及在线模糊辨识的思想与伪逆重构控制方法相结合 ,提出了一种新的智能型重构控制策略。该方法不仅可以适应多种故障模式 ,而且能克服各种不确定性因素和扰动对系统重构后动、静态性能的影响。理论分析和针对歼击机在机动飞行过程中各种舵面故障的仿真研究均表明本文方法的有效性     

N4SID辨识方法在航空发动机中的应用

针对在现代航空发动机的自适应控制和故障监控领域建立系统动态模型的需要 ,本文研究了基于状态空间模型的数字子空间状态空间系统辨识 (N4SID)方法 ,并在某型双转子涡喷发动机非线性气动热力学模型的“小偏离”状态上进行了仿真实验 ,结论表明 :该方法可以较准确地辨识发动机“小偏离”状态时的系统模型     

采样时间序列的故障数据检测与滑动容错滤波

在导出采样时间序列数据滑动LS滤波的基础上，本文建立了了组简洁的故障数据检测与修复算法，地故障数据修复与滤波估计相结合，构造了对突发性故障数据有良好容错能力的滑动容错滤波算法。仿计算证实，该方法有效、可靠。     

多维平稳过程的容错辨识与突变检测

本文将参数容错辨识与过程突变检测相结合,构造了多维平稳过程的协方差-位置参数(∑,μ)的一组具有良好容错能力的辨识算法;基于该算法,建立了多维平稳过程突发性故障的检测与诊断算法;简要指出了上述方法在航天测控工程中的应用前景。最后,通过仿真计算证实了容错辨识与突变检测算法的有效性。     

Fault diagnosis and isolation of the component and sensor for aircraft engine

Aircraft engine component and sensor fault detection and isolation approach was proposed,which included fault type detection module and component-sensor simultaneous fault isolation module. The approach can not only distinguish among sensor fault, component fault and component-sensor simultaneous fault, but also isolate and locate sensor fault and the type of engine component fault when the engine component fault and the sensor faults occur simultaneously. The double-threshold mechanism has been proposed, in which the fault diagnostic threshold changed with the sensor type and the engine condition, and it greatly improved the accuracy and robustness of sensor fault diagnosis system. Simulation results show that the approach proposed can diagnose and isolate the sensor and engine component fault with improved accuracy. It effectively improves the fault diagnosis ability of aircraft engine.      

基于模型的推进系统故障检测与诊断

针对泵压式供应系统液体火箭发动机的健康监控问题，提出了故障检测与诊断的基本框架，并讨论了基于发动机系统非线性数学模型，推广的卡尔曼滤波的故障检测方法的基于低阶线性模型的故障诊断方法。     

Integrated active fault-tolerant control using IMM approach

An integrated fault detection, diagnosis, and reconfigurable control scheme based on interacting multiple model (IMM) approach is proposed. Fault detection and diagnosis (FDD) is carried out using an IMM estimator. An eigenstructure assignment (EA) technique is used for reconfigurable feedback control law design. To achieve steady-state tracking, reconfigurable feedforward controllers are also synthesized using input weighting approach. The developed scheme can deal with not only actuator and sensor faults, but also failures in, system components. To achieve fast and reliable fault detection, diagnosis, and controller reconfiguration, new fault diagnosis and controller reconfiguration mechanisms have been developed by a suitable combination of the information provided by the mode probabilities from the IMM algorithm and an index related to the closed-loop system performance. The proposed approach is evaluated using an aircraft example, and excellent results have been obtained     

对特定传感器故障敏感的最优奇偶向量检测与隔离方法

 提出了一种利用对特定传感器故障敏感的最优奇偶向量进行故障检测与隔离的方法。主要思想是设计一个性能指标函数，使得最优奇偶向量对被检测的传感器故障最敏感，而对其余传感器故障和未知输入不敏感。与常用的广义似然比方法相比，该方法明显地提高了故障的检测能力，此外，对故障的隔离效果也更好。     

Optimal parity vector sensitive to designated sensor fault

Based on the parity space construction and the orthogonal constraint condition of parity vector, the approach of fault detection and isolation using optimal parity vector is presented. Its main idea is to design a performance criterion similar to the criterion presented by Zhang and Patton (1993), but most sensitive to the designated sensor's fault and least sensitive to other sensor's fault and unknown inputs such as noise. Through the Monte-Carlo simulation, it is shown that the proposed approach of choosing optimal parity vector greatly increases the ability of fault detection and the effectiveness of fault isolation is better than the generalized likelihood test (GLT) approach     

Robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels

The observer-based robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels and norm bounded modeling uncertainties are addressed. The network of unmanned vehicles is modeled as a discrete-time uncertain Markovian jump system. Based on the model, a residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of linear matrix inequality. Furthermore, a time domain optimization approach is proposed to improve the performance of the fault detection system. The problem of detecting small faults can be formulated as an optimization problem and its solution is given. For preventing false alarms, a new adaptive threshold function is established. The combined fault detection and optimization algorithm and the adaptive threshold are then applied to a network of highly maneuverable technology vehicles to illustrate the effective- ness of the orooosed aooroach.     

基于核主元分析方法的燃气涡轮发动机燃烧系统实时监测与诊断

针对燃气涡轮发动机燃烧室状态监测方法不足,故障定位难和故障早期发现难的问题,以涡轮排气温度场周向数据为分析依据,通过研究燃气在涡轮通流部分的偏转规律,利用核主元分析(KPCA)方法对经过有效性处理后的温度场数据进行分析,并结合两台发动机的故障数据,分别对燃烧系统自身故障和热电偶传感器故障进行检测与识别,验证了排气温度场燃气偏转规律与核主元分析相结合的方法对燃烧系统故障和传感器故障进行诊断的有效性.结果表明:该方法能够将安装了环管式分布火焰筒的燃气涡轮发动机燃烧室的故障诊断定位层次从目前的燃烧室这个大部件提高到火焰筒级别的小部件.      

A data driven approach for detection and isolation of anomalies in a group of UAVs

The use of groups of unmanned aerial vehicles(UAVs) has greatly expanded UAV’s capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar(dynamic) model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters.Simulation results have demonstrated the efficiency and flexibility of the proposed approach.     

Fault Detection and Diagnosis of Aircraft Actuators using Fuzzy-Tuning IMM Filter

This paper proposes a new interacting multiple model (IMM) filter for actuator fault detection. Since each individual filter of the IMM filter uses the combined information of the estimation values from all the operating filters, it can effectively estimate system parameter variations, thereby it can diagnose the actuator damage with an unknown magnitude. In this study, to diagnose the actuator failure fast and accurately, fuzzy logic is used to tune a transition probability among multiple models. This makes the fault detection process smooth and reduces the possibility of false fault detection. Also, a discrete fault tolerant command tracker is derived to cope with actuator damages. To validate the performance of the proposed fault detection and diagnosis (FDD) algorithm, numerical simulations are performed for a high performance aircraft system.     

Sensor/actuator fault diagnosis based on statistical analysis of innovation sequence and Robust Kalman Filtering

In this paper, an approach to detect and isolate the aircraft sensor/actuator faults affecting the mean of the Kalman filter innovation sequence is presented. The effects of the sensor and actuator faults in the innovation process of the channels are investigated, and a decision approach to isolate the sensor and actuator faults is proposed. When a Kalman filter is used, the decision statistics change regardless of whether the fault is in the sensors or in the actuators, whilst when a Robust Kalman Filter (RKF) is used, it is easy to distinguish the sensor and actuator faults. A novel feature of this diagnostic method is that the innovation sequence based fault isolation algorithm has been presented and hence, the sensor/actuator fault detection and isolation problem has been solved. The categories (or classes) of the likely faults are not demanded. The statistical characteristics of the system are not required to be known after the fault has occurred. In the simulations, the longitudinal dynamics of an aircraft control system are considered, and the detection and isolation of pitch rate gyro faults and actuator faults affecting the mean of the innovation sequence are examined.