Fault detection of sludge bulking using a self-organizing type-2 fuzzy-neural-network

Fault detection is important in the operation of wastewater treatment process (WWTP). In this paper, to ensure the process safety and effluent qualities, an intelligent fault detection (IFD) method, based on self-organizing type-2 fuzzy-neural-network (SOT2FNN) and intelligent identification method, was developed to detect and identify different types of sludge bulking. The main advantages of IFD are as follows. First, a data-driven framework, based on a data-driven model and an intelligent identification algorithm, was developed to facilitate the fault diagnosis. Second, a SOT2FNN, based on the intensity of information transmission algorithm and adaptive second-order algorithm, was designed to predict the sludge volume index (SVI) with high accuracy to provide necessary information for process monitoring. Third, an intelligent identification method, using the target-related identification algorithm (TRIA), was proposed to extract the correlation information to identify the types of sludge bulking. Finally, simulations and experimental examples were provided to confirm the effectiveness of the proposed IFD method.     

基于区间预测模型的污水处理厂传感器故障检测

污水处理厂配备许多传感器用于监测出水水质。传感器的正常工作与否对保证出水水质至关重要。给出了一种污水处理出水变量传感器故障检测方法。该方法根据入水和出水数据,采用径向基函数神经网络构造出水变量预测模型;使用参数线性集员辨识算法得到网络输出权值的集合描述,从而使预测模型能够给出出水变量的置信区间;以此置信区间为基础获得传感器的故障检测策略。由于置信区间描述了出水变量的存在范围,当传感器测量值超出置信区间,则可推断传感器发生故障。此外,在设计传感器故障检测策略时还考虑了污水处理过程异常的影响。实验结果证实所提方法的有效性。     

A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network

In this paper, a soft computing method, based on a recurrent self-organizing neural network (RSONN) is proposed for predicting the sludge volume index (SVI) in the wastewater treatment process (WWTP). For this soft computing method, a growing and pruning method is developed to tune the structure of RSONN by the sensitivity analysis (SA) of hidden nodes. The redundant hidden nodes will be removed and the new hidden nodes will be inserted when the SA values of hidden nodes meet the criteria. Then, the structure of RSONN is able to be self-organized to maintain the prediction accuracy. Moreover, the convergence of RSONN is discussed in both the self-organizing phase and the phase following the modification of the structure for the soft computing method. Finally, the proposed soft computing method has been tested and compared to other algorithms by applying it to the problem of predicting SVI in WWTP. Experimental results demonstrate its effectiveness of achieving considerably better predicting performance for SVI values.     

基于DPCA方法的传感器故障检测与诊断

针对多传感器的相关时序测量数据,在假设只存在传感器故障的前提下,提出了一种基于动态主成分分析（DPCA）的传感器故障检测方法。根据测量数据建立传感器的DPCA模型,在该模型基础上利用T2和SPE统计量进行传感器的故障检测。同时,将基于主成分分析（PCA）模型的传感器有效度指标SVI推广应用于DPCA模型中。通过对污水处理系统中重要传感器的故障诊断仿真实验表明：该方法能有效地检测和识别出故障传感器。     

Fault prognosis of filamentous sludge bulking using an enhanced multi-output gaussian processes regression

The activated sludge process (ASP) is widely adopted to remove pollutants in wastewater treatment plants (WWTPs). However, the occurrence of filamentous sludge bulking often compromises the stable operation of the ASP. For timely diagnosis of filamentous sludge bulking for an activated sludge process in advance, this study proposed a Multi-Output Gaussian Processes Regression (MGPR) model for multi-step prediction and presented the Vector auto-regression (VAR) to learn the MGPR modelling deviation. The resulting models and associated uncertainty levels are used to monitor the filamentous sludge bulking related parameter, sludge volume index (SVI), such that the evolution of SVI can be predicted for both one-step and multi-step ahead. This methodology was validated with SVI data collected from one full-scale WWTP. Online diagnosis and prognosis of filamentous bulking sludge with real-time SVI prediction were tested through a simulation study. The results demonstrated that the proposed methodology was capable of predicting future SVI with good accuracy, thereby providing sufficient time for filamentous sludge bulking.     

Incipient sensor fault isolation based on augmented Mahalanobis distance

Incipient sensor fault diagnosis is important to an efficient and optimal operating condition for modern industrial systems. Recently, a new fault detection index called augmented Mahalanobis distance (AMD) has been proposed in our previous work for incipient fault detection. Following detection, fault isolation is also quite desired so as to investigate root causes of the occurred fault. In the present work, the AMD statistic is first revisited and a geometric illustration of AMD is provided, which intuitively shows its superiority for incipient fault detection. Then, with available fault direction information, an incipient sensor fault isolation approach is proposed. Its fault isolability condition is analyzed theoretically and compared with that of the conventional method. For complex sensor faults whose fault direction information is unknown, a corresponding fault isolation strategy is also briefly discussed. Case studies on a high-speed train air brake system and the continuous stirred tank reactor (CSTR) process are carried out, which demonstrate the effectiveness of the AMD based fault detection and isolation methods, in comparison with conventional approaches.     

飞行器导航传感器故障诊断的应用研究

研究导航传感器故障诊断问题,由于飞行器导航传感器所处环境十分复杂,导航系统由多种部件组成,故障存在许多随机性、模糊性和不确定性因素,难以建立确定数学模型。传统线性模型故障诊断准确率低。为了提高飞行器导航传感器故障诊断准确率,提出一种神经网络的导航传感器故障诊断方法。飞行器导航传感器发生故障时信号中会产生突变成分,利用小波包对原始故障信号进行分解,提取信号特征向量,然后将特征向量输入神经网络训练,实现飞行器导航传感器故障智能化诊断。在Matlab平台实现传感器故障诊断的仿真,结果表明,神经网络提高了飞行器导航传感器故障诊断的准确率,是一种在线、行之有效的导航传感器故障方法。     

基于分形和FNN的水轮机组振动故障在线诊断*

水轮发电机组的故障诊断具有模糊性和耦合性,提出一种基于模糊神经网络FNN的水轮发电机组振动故障在线诊断方法。首先,对反映转子振动状态的轴心轨迹用分形维数提取其结构特征,实现图形量化,以便FNN在线识别;接着,以6种典型振动故障为研究对象,在总结了包括轴心轨迹在内4类共14种故障征兆的基础上,分析各故障征兆的模糊属性,给出它们的模糊处理;然后,建立一种六层的前向FNN映射征兆到故障间的模糊推理,并给出学习算法修正网络参数;FNN通过自学习可保证良好的在线诊断精度。实例分析结果验证了其可行性。     

水电数字仿真在线融合故障诊断专家系统

为了解决丰满水电数字仿真系统的在线故障诊断问题，基于信息与方法融合的思想，提出了分布式集成神经网络建模方法、模糊神经网络专家系统(FNNES)在线故障诊断方法。将模糊神经网络(FNN)嵌入专家系统(ES)中，FNN负责知识获取和逻辑推理，ES负责系统信息的输入和输出、符号推理，并对FNN的结论进行解释。系统的运行验证了方法的有效性和实际应用价值。为现场诊断系统的开发提供了有益的方法和经验。     

基于UKF的水下航行器IMU故障检测与诊断方法研究

惯性测量单元（IMU）作为水下航行器导航系统关键传感器,其可靠性直接影响航行器的导航性能。为了提高IMU的容错能力,本文提出一种基于无迹卡尔曼滤波（UKF）算法的IMU故障诊断技术。首先根据水下航行器的动力学方程和导航系统特点,建立描述IMU故障与导航状态量关系的解析模型;接着基于UKF非线性滤波的特点,进行导航滤波解算,基于此,提出了解耦矩阵法以实现IMU的故障检测;并且根据无迹卡尔曼滤波器新息正交原理,提出了实时估计IMU故障的方法,从而完成水下航行器IMU故障的在线检测与诊断。最后,通过实际航行数据验证了所提出算法的有效性。     

An integral approach to inferential quality control with self-validating soft-sensors

This paper presents an integral technique for designing an inferential quality control applicable to multivariate processes. The technique includes a self-validating soft-sensor and a multivariate quality control index that depends on the specifications. Based on a partial least squares (PLS) decomposition of the online process measurements, a fault detection and diagnosis technique is used to develop an improved self-validation strategy that is able to confirm, correct or reject the soft-sensor predictions. Model extrapolations, disturbances or sensor faults are first detected through a combined statistic (that considers the calibration region); then, a diagnosis is made by combining statistics pattern recognition, contribution analysis, and disturbance isolation based on historical fault patterns. An off-spec alarm is produced when the proposed index detects that an operating point lies outside the integral design space driven by the specifications. The effectiveness of the proposed technique is evaluated by means of two numerical examples. First, a synthetic example is used to interpret the fundamentals of the method. Then, the technique is applied to the industrial Styrene-Butadiene rubber process, which is emulated through an available numerical simulator.     

Robust detection of intermittent multiplicative sensor fault

In this paper, the detection problem of intermittent multiplicative sensor fault is investigated for stochastic uncertain systems. A robust optimal filter is designed according to the criterion of minimum estimation error covariance. Then, based on this, a residual generator is constructed, and the quantitative effect of the fault on it is discussed in detail. After that we design the evaluation function and detection threshold to achieve intermittent fault detection. Our proposed strategy has a recursive form and only includes simple arithmetic operations, thus it is suitable for real‐time online applications. Finally, a simulation example is given to demonstrate the effectiveness of the proposed strategy.     

A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems With Application to the Satellite's Attitude Control Subsystem

This paper presents a robust fault detection and isolation (FDI) scheme for a general class of nonlinear systems using a neural-network-based observer strategy. Both actuator and sensor faults are considered. The nonlinear system considered is subject to both state and sensor uncertainties and disturbances. Two recurrent neural networks are employed to identify general unknown actuator and sensor faults, respectively. The neural network weights are updated according to a modified backpropagation scheme. Unlike many previous methods developed in the literature, our proposed FDI scheme does not rely on availability of full state measurements. The stability of the overall FDI scheme in presence of unknown sensor and actuator faults as well as plant and sensor noise and uncertainties is shown by using the Lyapunov's direct method. The stability analysis developed requires no restrictive assumptions on the system and/or the FDI algorithm. Magnetorquer-type actuators and magnetometer-type sensors that are commonly employed in the attitude control subsystem (ACS) of low-Earth orbit (LEO) satellites for attitude determination and control are considered in our case studies. The effectiveness and capabilities of our proposed fault diagnosis strategy are demonstrated and validated through extensive simulation studies.     

基于稀疏分解残差的氢气传感器故障探测与辨识方法

针对传感器故障探测和诊断,提出了一种基于稀疏分解残差的氢气传感器故障探测和辨识方法.基于信号稀疏分解理论,对采集的传感器正常信号数据集,利用K奇异值分解(K-SVD)学习算法得到一超完备字典D;在字典上对非正常(故障)信号进行分解,根据稀疏分解的残差大小和范围完成对传感器故障的探测及辨识.实验结果表明:对氢气传感器的故障探测率和总辨识率分别达到98.75%和97.25%,可以有效地解决氢气传感器的故障探测和辨识.     

一类带有传感器故障的混合系统的容错控制

A model-based fault tolerant control approach for hybrid linear dynamic systems is proposed in this paper. The proposed method, taking advantage of reliable control, can maintain the performance of the faulty system during the time delay of fault detection and diagnosis (FDD) and fault accommodation (FA), which can be regarded as the first line of defence against sensor faults. Simulation results of a three-tank system with sensor fault are given to show the efficiency of the method.     

一类带有传染器故障的混合系统的容错控制

A model-based fault tolerant control approach for hybrid linear dynamic systems is proposed in this paper. The proposed method, taking advantage of reliable control, can maintain the performance of the faulty system during the time delay of fault detection and diagnosis (FDD) and fault accommodation (FA), which can be regarded as the first line of defence against sensor faults.Simulation results of a three-tank system with sensor fault are given to show the efficiency of the method.     

Application of agent technology concepts to the design of a fault-tolerant control system

This work concerns the applicability of agent technology concepts to the design of a plant fault-tolerant control system. The operation of the fault-tolerant system is enhanced by decomposing it into autonomous subsystems and by turning them into agents. The detailed development of one of the agents, the feed line of the process, is presented and its performance is tested by simulation. The proposed framework meets the control objectives and features a significant level of fault tolerance to sensor and actuator failures. This is achieved using an observer based fault detection and diagnosis (FDD) unit. Moreover, this work shows how the control strategy may be altered to tackle a case of severely impacted control capability.     

基于神经网络观测器的飞控传感器在线故障诊断

为提高无人机飞行安全可靠性,针对飞行控制系统中常出现的传感器故障以及非线性气动力模型参数难以确定的问题,提出了基于BP神经网络观测器估计的故障诊断方法;引用LM改进算法对网络参数进行调整,构造了神经网络观测器模型逼近非线性系统,并运用于飞行控制系统进行在线数字仿真,对垂直陀螺输出卡死故障、恒偏差故障和恒增益故障分别进行仿真分析;仿真结果表明,所设计神经网络观测器可以有效估计系统输出,在线诊断传感器故障。     

Optimal State Estimation and Fault Diagnosis for a Class of Nonlinear Systems

This study proposes a scheme for state estimation and,consequently,fault diagnosis in nonlinear systems.Initially,an optimal nonlinear observer is designed for nonlinear systems subject to an actuator or plant fault.By utilizing Lyapunov's direct method,the observer is proved to be optimal with respect to a performance function,including the magnitude of the observer gain and the convergence time.The observer gain is obtained by using approximation of Hamilton-Jacobi-Bellman(HJB)equation.The approximation is determined via an online trained neural network(NN).Next a class of affine nonlinear systems is considered which is subject to unknown disturbances in addition to fault signals.In this case,for each fault the original system is transformed to a new form in which the proposed optimal observer can be applied for state estimation and fault detection and isolation(FDI).Simulation results of a singlelink flexible joint robot(SLFJR)electric drive system show the effectiveness of the proposed methodology.