Non-linear system identification using wavelet multiresolution models

A new methodology for identifying non-linear NARMAX models, from noise corrupted data, is introduced based on semi-orthogonal wavelet multiresolution approximations. An adaptive model sequencing strategy is introduced to infer model complexity from the data while reducing computational costs. This is used in conjunction with an iterative orthogonal-forward-regression routine coupled with model validity tests to identify sparse but accurate wavelet series representations of non-linear processes. Experimental data from two real systems, a liquid level system and from a civil engineering structure are used to illustrate the effectiveness of the new identification procedure.     

Non-linear system identification using the Hammerstein model

An algorithm for the identification of non-linear systems which can be described by a Hammerstein model consisting of a single-valued non-linearity followed by a linear system is presented. Cross-correlation techniques are employed to decouple the identification of the linear dynamics from the characterization of the non-linear element. These results are extended to include the identification of the component subsystems of a feedforward process consisting of a Hammerstein model in parallel with another linear system.     

Online fault diagnosis using recursive subspace identification: Application to a dam-gallery open channel system

The paper presents an online strategy for sensor and/or actuator fault detection and isolation applied to a dam-gallery. A recursive subspace identification algorithm is used to estimate the dam-gallery model parameters. The main contribution consists in developing a specific identification scheme, insensitive to a certain type of faults. That is, the identified parameters are invariant to the faults. A fault estimation procedure is proposed to detect potential faults. The proposed approach appears to be suitable for open channel systems for which the characteristics are not easily measurable.     

Sensor deployment for fault diagnosis using a new discrete optimization algorithm

Optimal allocation of the sensor in a wireless sensor network (WSN) is required to have a satisfactory fault diagnosis within the system. In fact, the sensor nodes in the network should be located in an arrangement to maximize the failure diagnosis. In this paper, the sensor deployment optimization to diagnose the distributed failures in a wireless unmanned aerial vehicles (UAVs) network has been studied. In this way, a novel evolutionary optimization algorithm inspired by the gases Brownian and turbulent rotational motion is utilized which is called Discrete Gases Brownian Motion Optimization (DGBMO) algorithm. An integer linear programming (ILP) approach is used to formulate the sensor deployment. Then the sensor deployment optimization is solved by DGBMO as well as generic ILP solvers and Boolean satisfiability-based ILP solvers. The results show that DGBMO is suitable for sensor disposition optimization especially in large-sized UAV networks.     

Poirot: applications of a logic fault diagnosis tool

The Poirot tool isolates and diagnoses defects through fault modeling and simulation. Along with a carefully selected partitioning strategy, functional and sequential test pattern applications show success with circuits having a high degree of observability     

改进的模糊聚类在控制系统故障诊断中的应用

为了提高控制系统中传感器与执行器故障诊断的准确性,结合小波分析特征提取的优势和密度函数加权模糊C-均值聚类具有较好分类效果的特点,提出了一种新的控制系统故障诊断方法。该方法首先利用小波分析对故障信号进行特征提取,降低噪声的影响;然后对特征提取后的数据通过加权模糊C-均值聚类算法,对故障进行识别分类。实验表明,基于小波分析和加权模糊C-均值聚类相结合的方法,不仅可以识别不同部件的故障,而且可以对同一部件的不同类型的故障进行诊断。     

Agent oriented intelligent fault diagnosis system using evidence theory

Multi sensors fusion is a very important process for fault diagnosis system. Information obtained from multi sensors need to be fused because no single sensor can get all the information for fault diagnosis. Moreover, information from different sensors may be uncertainty, inaccuracy, or even conflicting. Evidence theory can be used for information fusion, which is regarded as an extension form of Bayesian reasoning, but it has a better fusion result by simple reasoning process using belief function without knowing the prior probability. All the information collected from multi sensors in the system can be described as the evidence for diagnosis so that the fault diagnosis problem can then be modeled as a problem of evidence fusion and decision. In this paper, the classical Dempster-Shafer evidence theory is discussed, and the disadvantages of the combination rule are also analyzed. The notion of support degree of focal element is suggested in order to evaluate the conflicts between multi sensors. The new combination rule is then built to allocate the conflicted information from multi sensors based on the support degree of focal element. Furthermore, the decision rules for fault diagnosis are also proposed, as well as the architecture of the agent oriented intelligent fault diagnosis system. Finally, a case study is given to illustrate the performance of the proposed model.     

Set-membership identification and fault detection using a Bayesian framework

This paper deals with the problem of set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation problem can be reformulated from the Bayesian viewpoint in order to, first, determine the feasible parameter set in the identification stage and, second, check the consistency between the measurement data and the model in the fault-detection stage. The paper shows that, assuming uniform distributed measurement noise and uniform model prior probability distributions, the Bayesian approach leads to the same feasible parameter set than the well-known set-membership technique based on approximating the feasible parameter set using sets. Additionally, it can deal with models that are nonlinear in the parameters. The single-output and multiple-output cases are addressed as well. The procedure and results are illustrated by means of the application to a quadruple-tank process.     

Non-linear systems identification using radial basis functions

This paper investigates the identification of discrete-time non-linear systems using radial basis functions. A forward regression algorithm based on an orthogonal decomposition of the regression matrix is employed to select a suitable set of radial basis function centers from a large number of possible candidates and this provides, for the first time, fully automatic selection procedure for identifying parsimonious radial basis function models of structure-unknown non-linear systems. The relationship between neural networks and radial basis functions is discussed and the application of the algorithms to real data is included to demonstrate the effectiveness of this approach.     

Development of a new automatic fault diagnosis system for fine pattern transmission lines

International Journal of Control, Automation and Systems - This paper presents a new automatic fault diagnosis and detection system for fine pattern interconnects. It is verified by performance of...     

Non-linear system identification using Hammerstein and non-linear feedback models with piecewise linear static maps

We consider the identification of Hammerstein/non-linear feedback models by approximating internal non-linearities using piecewise linear static maps. The resulting method utilizes a point-slope parameterization that leads to a computationally tractable optimization problem. The computational appeal of this technique is derived from the fact that the method only requires a matrix inverse and singular value decomposition. Furthermore, the identification method simultaneously identifies the linear dynamic and static non-linear blocks without requiring prior assumptions on the form of the static non-linearity.     

贝叶斯网络在电子系统故障诊断中的应用研究

电子系统大多结构复杂,各组成模块存在错综复杂、相互影响的关系,另外测点较少且测点数据常常是不完备的。针对此类情况,以某电源系统为研究对象,提出了基于贝叶斯网络的电子系统故障诊断方法。首先依据系统的结构获得其因果图,并对各测点信号进行离散化处理;其次建立用于故障诊断的贝叶斯网络模型,并且根据历史数据完成该网络的参数学习,最后利用获得的事实来实现故障的诊断。仿真结果验证了该方法的有效性,为电子系统的故障诊断提出了一种新的思路。     

A new approach to fault diagnosis in electrical distribution networks using a genetic algorithm

In this paper, a new approach to fault diagnosis in electrical distribution network is proposed. The approach is based upon the parsimonious set covering theory and a genetic algorithm. First, based on the causality relationship among section fault, protective relay action and circuit breaker trip, the expected states of protective relays and circuit breakers are expressed in a strict mathematical manner. Secondly, the well developed parsimonious set covering theory is applied to the fault diagnosis problem. A 0–1 integer programming model is then proposed. Thirdly, a powerful genetic algorithm (GA) based method for the fault diagnosis problem is developed by using information on operations of protective relays and circuit breakers. The developed method can deal with any complicated faults, and simultaneously determine faulty sections and any hidden defects in the feeder protection systems. Test results for a sample electrical distribution network have shown that the developed mathematical model for the fault diagnosis problem is correct, and the adopted GA based method is efficient.     

Non-linear dynamic system identification: A multi-model approach

We are concerned with models which are able to describe multiple-input multiple-output (MIMO) non-linear dynamic systems. These models are represented in the form of rules and are known as Tagaki-Sugeno models. An identification algorithm for these models based on input and output data is presented. Parameter estimation is based on the calculation of model sensitivity functions with respect to their parameters. Some aspects of structure identification are also tackled, i.e. determination of local model orders and number of rules.     

Efficient agreement using fault diagnosis

Summary We give an extremely simple Byzantine agreement protocol that usesO(t ²) processors, min(f+2,t+1) rounds of communication,O(n·t·f·log|V|) total message bits, andO(log|V|) maximum message size, wheren is the total number of processors that actually participate in the protocol,t is an upper bound on the number of faulty processors,f is the number of processors that actually fail in a given execution, andV is the set of possible inputs. This protocol uses roughly the same resources as a more complex protocol due to Dolev, Reischuk, and Strong. By adding explicit fault diagnosis to our first protocol, we produce a some-what more complicated protocol that usesO(t ^1.5) processors, min(f+2,t+1) rounds,O(n·t ² ·f·log|V|) total message bits, andO(t·log|V|) maximum message size. Brian A. Coan received the B.S.E. degree in electrical engineering and computer science from Princeton University in 1977, the M.S. degree in computer engineering from Stanford University in 1979, and the Ph.D. degree in computer science from the Massachusetts Institute of Technology in 1987. He has worked for Amdahl Corporation and AT&T Bell Laboratories. Since 1987 he has been a member of the technical staff in the Network Systems Research Department at Bellcore. His main research interests are in distributed systems, fault tolerance, and platforms to support distributed multimedia systems.A preliminary version of this paper appeared in the Proceedings of the 26th Annual Allerton Conference on Communication, Control, and Computing, pp 663–672, 1988     

PLC硬件结构的水电站故障监测系统及故障识别

针对现有水电站故障监测系统识别效率低、工作难度大等问题,提出了一种基于巡检机器人视觉识别的故障监测方法,并将其应用于水轮机调节系统故障诊断问题;通过可编程逻辑控制器保证巡检机器人的稳定工作运行,通过非线性输出频率响应函数分析故障参数的特性;利用方向梯度直方图作为模板,采用可变形组件模型算法实现HTGS的故障识别;试验表明,该研究方法处理2 GB故障数据所耗时间为40 s。     

Non-linear system identification using closed-loop data with no external excitation: The case of a lean combustion chamber

This paper deals with the analysis of a set of measurements collected on a lean premixed combustion process operating in a limit cycle. Due to the fact that the data are collected in closed-loop and the system has no external excitation, the identification task is particularly challenging. This work mainly focuses on the issue of the feasibility of the identification task. It will be shown that, despite the paucity of information available, a grey-box non-linear model can be estimated. The model provides an explanation both of the limit-cycle fundamental oscillation and of a non-harmonic high-frequency signal affecting the pressure of the combustion chamber.     

Fault diagnosis of an industrial gas turbine prototype using a system identification approach

In this work, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine simulated process is presented. The main point of the paper consists of exploiting an identification scheme in connection with dynamic observer or filter design procedures for diagnostic purposes. Thus, black-box modelling and output estimation approaches to fault diagnosis are in particular advantageous in terms of solution complexity and performance achieved. Moreover, the suggested scheme is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. In order to experimentally verify the robustness of the solution obtained, the proposed FDI strategy has been applied to the simulation data of a single-shaft industrial gas turbine plant in the presence of measurement and modelling errors. Hence, extensive simulations of the test-bed process and Monte Carlo analysis are the tools for assessing experimentally the capabilities of the developed FDI scheme, when compared also with different data-driven diagnosis methods.     

A new image-based immersive tool for dementia diagnosis using pairwise ranking and learning

Dementia disease is globally acknowledged as one of the most severe non-communicable diseases nowadays. Identifying different stages of dementia disease is significant in its later treatment for delaying the onset and progression of the disease. Among diverse types of tools utilized in dementia disease diagnosis, brain scanning is generally accepted as an effective and affordable way at present. There are several kinds of medical images incorporated in contemporary dementia studies, and magnetic resonance images receives vast popularity. In this study, arterial spin labeling, an emerging perfusion functional-magnetic resonance imaging technique, is adopted in a newly proposed image-based immersive tool for dementia disease diagnosis. Novel pairwise ranking and learning techniques based on a new continuous and differentiable surrogated Kendall-Tau rank correlation coefficient is proposed to realize the immersive tool. Extensive experiments based on a database composed of images acquired from 350 demented patients are carried out with several popular pattern recognition diagnosis tools being compared. Their results undergo rigorous and comprehensive statistical analysis, and the superiority of the newly proposed image-based immersive tool in dementia disease diagnosis has been demonstrated.