Determining the propagation path of a disturbance in multi-rate process and electromechanical systems

This paper proposes a multi-rate method to identify the propagation path of a persistent disturbance in an enlarged system envelope which includes the process plant and its electromechanical equipment. The need to integrate process and equipment diagnosis has been highlighted by industrial commentators. However, process and electromechanical measurements often have different sampling rates. The multi-rate method proposed extends a state-of-the-art propagation path method so that it combines fast-sampled electromechanical measurements and slow-sampled process measurements. The method is based on non-linear mutual prediction, which yields the directionality in the relationship between two time series. The method was demonstrated and validated, giving the expected outcome in an experimental case study, in which the root cause and propagation path of the disturbance were known.     

Nonlinear aircraft sensor fault reconstruction in the presence of disturbances validated by real flight data

This paper proposes an approach for Inertial Measurement Unit sensor fault reconstruction by exploiting a ground speed-based kinematic model of the aircraft flying in a rotating earth reference system. Two strategies for the validation of sensor fault reconstruction are presented: closed-loop validation and open-loop validation. Both strategies use the same kinematic model and a newly-developed Adaptive Two-Stage Extended Kalman Filter to estimate the states and faults of the aircraft. Simulation results demonstrate the effectiveness of the proposed approach compared to an approach using an airspeed-based kinematic model. Furthermore, the major contribution is that the proposed approach is validated using real flight test data including the presence of external disturbances such as turbulence. Three flight scenarios are selected to test the performance of the proposed approach. It is shown that the proposed approach is robust to model uncertainties, unmodeled dynamics and disturbances such as time-varying wind and turbulence. Therefore, the proposed approach can be incorporated into aircraft Fault Detection and Isolation systems to enhance the performance of the aircraft.     

Structural analysis based sensors fault detection and isolation of cylindrical lithium-ion batteries in automotive applications

The battery sensors fault diagnosis is of great importance to guarantee the battery performance, safety and life as the operations of battery management system (BMS) mainly depend on the embedded current, voltage and temperature sensor measurements. This paper presents a systematic model-based fault diagnosis scheme to detect and isolate the current, voltage and temperature sensor fault. The proposed scheme relies on the sequential residual generation using structural analysis theory and statistical inference residual evaluation. Structural analysis handles the pre-analysis of sensor fault detectability and isolability possibilities without the accurate knowledge of battery parameters, which is useful in the early design stages of diagnostic system. It also helps to find the analytical redundancy part of the battery model, from which subsets of equations are extracted and selected to construct diagnostic tests. With the help of state observes and other advanced techniques, these tests are ensured to be efficient by taking care of the inaccurate initial State-of-Charge (SoC) and derivation of variables. The residuals generated from diagnostic tests are further evaluated by a statistical inference method to make a reliable diagnostic decision. Finally, the proposed diagnostic scheme is experimentally validated and some experimental results are presented.     

基于小波神经网络的模拟电路故障诊断

模拟电路故障诊断具有诊断特性复杂,故障字典建立耗时长等特性,用传统的方法很难得到最佳的诊断效果。本文采用小波神经网络对故障电路建模,基于该网络学习收敛快,对网络输入不太敏感的特点,实现故障诊断。     

一种新的累加自动地形生成方法*

提出了一种新的基于分形的累加自动地形生成方法,首先引入断裂形成算法作为地形高度生成器的初始模型,通过控制幅度和迭代次数,生成形态各异的地形轮廓,然后从分形理论fBM数学模型出发,使用中点位移法对初始模型进行迭代处理,生成丰富的地形细节。该方法的主要优点是自动性和可控性的结合,一方面通过设置断裂形成算法的参数可以自动生成特征模板,另一方面通过控制分形参数,生成任意层次的地形细节。实验表明,使用该方法可以生成真实感较强的地形,该方法是可行且有效的。     

动态故障树割序集分析的模块化方法

针对割序集模型较高的复杂度,提出静态子树模块化和动态子树模块化2种简化方法。利用模块化方法将动态故障树划分为多个静态子树和动态子树。对完全由静态门构成的静态子树采用二叉决策图计算其发生概率;对动态子树采用割序集模型进行分析,将其中包含的静态子树作为一个整体进行处理。通过实例阐述模块化方法的应用过程,算例分析结果表明,该方法能有效降低割序集模型的复杂度。     

Improving the yield of NoC-based systems through fault diagnosis and adaptive routing

We propose an effective and low cost method to increase the yield and the lifetime of torus NoCs. The method consists in detecting and diagnosing NoC interconnect faults using BIST structures and activating alternative paths for the faulty links. Alternative paths use the inherent redundancy of the torus topology, thus leading to minimal performance, area, and power overhead. We assume an extended interconnect fault model comprising stuck-at and pairwise shorts within a single link or between any two links in the network. Experimental results for a 3×3 NoC show that the proposed approach can correctly diagnose 93% of all possible interconnect faults and can mitigate 42% of those faults (representing 94.4% of the solvable faults) with a worst case performance penalty of 8% and 1% of area overhead. We also demonstrate the scalability of the method by presenting its application to larger NoCs.     

Application of extreme learning machine for series compensated transmission line protection

This paper proposes a new approach based on combined Wavelet Transform-Extreme Learning Machine (WT-ELM) technique for fault section identification (whether the fault is before or after the series capacitor as observed from the relay point), classification and location in a series compensated transmission line. This method uses the samples of fault currents for half cycle duration from the inception of fault. The features of fault currents are extracted by first level decomposition of the current samples using discrete wavelet transform (DWT) and the extracted features are applied as inputs to ELMs for fault section identification, classification and location. The feasibility of the proposed method has been tested on a 400 kV, 300 km series compensated transmission line for all the ten types of faults using MATLAB simulink. On testing 28,800 fault cases with varying fault resistance, fault inception angle, fault distance, load angle, percentage compensation level and source impedance, the performance of the proposed method has been found to be quite promising. The results also indicate that the proposed method is robust to wide variation in system and operating conditions.     

基于故障跟踪估计器的非线性时滞系统故障诊断

提出一种可有效检测和估计一类非线性时滞系统故障的故障跟踪估计器.根据预测控制和迭代学习控制的思想,在所选取的优化时域长度内,通过迭代算法调节故障跟踪估计器中的可调参数,使之逼近系统中实际发生的故障.与以往基于观测器的故障诊断方法不同的是,故障跟踪估计器可同时检测和估计系统中发生的故障,而且针对不同类型的故障亦有很好的适应性.仿真结果表明了所提出算法的可行性和有效性.     

一种基于容错的感知数据回归模型研究

传感器主要用于对外部环境进行监测,然而当传感器发生故障时监测结果会出现误差。为了提高传感器发生故障时系统的容错能力,提出了一种容错的感知数据回归模型。首先,对最小二乘和岭回归两种线性回归模型进行分析,并分析了线性回归模型的相关统计量;然后,分析了部分传感器发生故障时系统的相关统计量,并以此为基础分析了协变量矩阵的上下界;最后,依据协变量矩阵定义了故障指标,并将优化模型转化为同时最小化故障指标和均方误差的问题。实验表明,提出的容错回归模型与传统的最小二乘法和岭回归方法相比具有更小的预测误差,因而当传感器发生故障时所提模型具有更好的健壮性。