案例推理在印刷机械故障诊断中的应用

本文借助案例推理技术在知识获取、易于理解方面的优势,结合印刷机械设备的特点,将基于案例推理的方法引入到印刷设备的故障诊断领域,详细论述了案例推理在设备故障诊断系统中的实现过程,并给出了该方法在印刷机械设备故障诊断中的应用实例.     

一种基于软计算的转子故障诊断方法

提出了一种基于软计算的转子故障诊断方法。该方法充分利用软计算中的模糊集合理论,人工神经网 络,粗糙集理论和遗传算法等计算方法优势,弥补它们相互的不足,进行故障诊断。首先利用粗糙集理论对样本数据进 行初步规则获取,并计算规则的依赖度和条件覆盖度,然后根据这些规则进行网络设计,其中,网络隐层节点的数目等于 规则的数目,初始网络权重由规则的依赖度和条件覆盖度确定,最后用遗传算法对模糊神经网络参数进行优化。使用该 网络对转子类常见故障进行诊断。实验表明,和一般模糊神经网络相比,这种基于软计算的诊断方法具有训练时间短、 诊断准确率高的特点。     

使用表达式分析的通用故障诊断系统设计与实现

基于知识的故障诊断专家系统在应用时面临知识获取、知识表达和知识与诊断推理有机融合等几方面的困难。本文论述了一种使用表达式解析的故障诊断方法,并将其用于某型雷达系统的故障诊断保障系统中。该方法将知识表示为逻辑表达式,推理机使用表达式解析的方法推理知识。使用该方法,知识的获取和更新更加方便,并且推理机和表达式相互独立,具有相当的通用性。     

基于贝叶斯网络的计算机网络端到端服务故障诊断

研究了利用贝叶斯网络不确定推理技术实现端到端服务故障诊断的方法，详细描述了贝叶斯网络故障诊断模型的建立方法，设计了基于Pearl信念传播机制的故障诊断算法，并对其进行了改进，以提高诊断效果．最后，通过仿真验证了该方法的有效性，并提出了下一步的研究方向．     

小型组合式冷库设计专家系统

冷库设计中存在大量的符号推理及经验数据的选取，传统程序只能处理精确数据，而对符号推理和非精确性数据无能为力。采用专家系统模型，运用模糊逻辑方法，结合小型组合式冷库的设计实例，模拟专家进行冷库设计中符号推理及选取经验数据，得到了较好的设计结果。     

机械故障诊断的推理规律研究

机械故障诊断对于设备的安全、连续运行和预知维修至关重要。作为故障诊断成功的关键，文章阐述了诊断中的诊断知识及其结构，介绍了专家的概念知识、方法知识和面向诊断对象知识，并用一种双向、互补的有机结构来刻划这些知识在诊断过程中的综合应用。将故障熵的概念引申为广义故障熵，并结合最小互熵原理，用于阐述诊断推理过程。分别用对数模型和Sigmoid模型来刻划机械故障诊断中的认知规律。通过某炼油厂的一个实际诊断案例对这两个模型进行了分析和对比研究。结果表明，两者均能近似地刻划认知过程，但Sigmoid模型更能准确地描述机械故障诊断推理的一般规律，更符合诊断过程的实际情况。     

基于数据融合和多重智能模型的目标识别和跟踪

考虑到单传感器的系统存在着局限性,提出了基于多传感器（雷达和红外）信号融合的目标识别和跟踪系统,以利用数据的互补和冗余。特征层融合能利用各传感器提供的特征为提高目标识别能力;对于点目标和面目标分别提出了智能规则推理和神经网分类器的目标识别方法。决策层融合能提高目标跟踪的精度并提高抗干扰性;提出了可信度决策的目标跟踪方法。     

通用雷达故障诊断专家系统的设计

目的：为了提高雷达装备故障诊断的效率和可靠性。方法：通过对雷达装备综合测试和故障诊断要求和方法的分析。介绍一种适用于雷达装备的故障智能诊断专家模型，探讨了该模型的基本结构和相应的推理机制及故障诊断策略。结果：结论雷达故障诊断的特点，研究了该模型及诊断方法在雷达装备中的具体应用，证实了该方法是有效的。结论：将自动测试技术和专家系统相结合，可以提高故障诊断的效率和可靠性。     

基于贝叶斯网络的计算机网络设备级故障诊断

贝叶斯网络能够用图形化的方式表示对象间的依赖关系,并支持不确定推理。研究基于贝叶斯网络的计算机网络设备级故障诊断方法,描述故障诊断模型的构造方法,设计故障诊断算法,并通过仿真验证该方法的有效性。     

多层抽象混合推理的智能诊断模型与应用

针对设备故障智能诊断问题，提出了一种多层抽象混合推理的智能诊断模型。该模型把设备故障智能诊断问题分成四个静态层和三个动态层，对诊断知识进行了比较合理的分层与分类，并把领域“深知识”、经验“浅知识”和算法知识有机地结合起来加以运用，文中给出了该模型在设备故障诊断知识表示和推理的具体应用方法。     

Condition‐based Maintenance Optimization Using Neural Network‐based Health Condition Prediction

Artificial neural network (ANN)‐based methods have been extensively investigated for equipment health condition prediction. However, effective condition‐based maintenance (CBM) optimization methods utilizing ANN prediction information are currently not available due to two key challenges: (i) ANN prediction models typically only give a single remaining life prediction value, and it is hard to quantify the uncertainty associated with the predicted value; (ii) simulation methods are generally used for evaluating the cost of the CBM policies, while more accurate and efficient numerical methods are not available, which is critical for performing CBM optimization. In this paper, we propose a CBM optimization approach based on ANN remaining life prediction information, in which the above‐mentioned key challenges are addressed. The CBM policy is defined by a failure probability threshold value. The remaining life prediction uncertainty is estimated based on ANN lifetime prediction errors on the test set during the ANN training and testing processes. A numerical method is developed to evaluate the cost of the proposed CBM policy more accurately and efficiently. Optimization can be performed to find the optimal failure probability threshold value corresponding to the lowest maintenance cost. The effectiveness of the proposed CBM approach is demonstrated using two simulated degradation data sets and a real‐world condition monitoring data set collected from pump bearings. The proposed approach is also compared with benchmark maintenance policies and is found to outperform the benchmark policies. The proposed CBM approach can also be adapted to utilize information obtained using other prognostics methods. Copyright © 2012 John Wiley & Sons, Ltd.     

Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks

This paper presents the work carried out towards developing a diagnostic system for the identification of accident scenarios in 220 MWe Indian PHWRs. The objective of this study is to develop a methodology based on artificial neural networks (ANNs), which assists in identifying a transient quickly and suggests the operator to initiate the corrective actions during abnormal operations of the reactor. An operator support system, known as symptom-based diagnostic system (SBDS), has been developed using ANN that diagnoses the transients based on reactor process parameters, and continuously displays the status of the reactor. As a pilot study, the large break loss of coolant accident (LOCA) with and without the emergency core cooling system (ECCS) in reactor headers has been considered. Several break scenarios of large break LOCA have been analyzed. The time-dependent transient data have been generated using the RELAP5 thermal hydraulic code assuming an equilibrium core, which conforms to a realistic estimation. The diagnostic results obtained from the ANN study are satisfactory. These results have been incorporated in the SBDS software for operator assistance. A few important outputs of the SBDS have been discussed in this paper.     

A new artificial neural network-based response surface method for structural reliability analysis

This paper presents a new artificial neural network-(ANN)based response surface method in conjunction with the uniform design method for predicting failure probability of structures. The method involves the selection of training datasets for establishing an ANN model by the uniform design method, approximation of the limit state function by the trained ANN model and estimation of the failure probability using first-order reliability method (FORM). In the proposed method, the use of the uniform design method can improve the quality of the selected training datasets, leading to a better performance of the ANN model. As a result, the ANN dramatically reduces the number of required trained datasets, and shows a good ability to approximate the limit state function and then provides a less rigorous formulation in the context of FORM. Results of three numerical examples involving both structural and non-structural problems indicate that the proposed method provides accurate and computationally efficient estimates of the probability of failure. Compared with the conventional ANN-based response surface method, the proposed method is much more economical to achieve reasonable accuracy when dealing with problems where closed-form failure functions are not available or the estimated failure probability is extremely small. Finally, several important parameters in the proposed method are discussed.     

Prediction of fracture parameters of concrete by Artificial Neural Networks

Modelling of material behaviour generally involves the development of a mathematical model derived from observations and experimental data. An alternative way discussed in this paper is Artificial Neural Network (ANN)-based modelling which is a subfield of artificial intelligence. The main benefit in using an ANN approach is that the network is built directly from experimental data using the self-organising capabilities of the ANN. In this paper the Two-Parameter Model (TPM) in the fracture of cementitious materials is modelled with a back-propagation ANN. The results of an ANN-based TPM look viable and very promising.     

Application of artificial neural networks to nuclear power plant transient diagnosis

A study on various artificial neural network (ANN) algorithms for selecting a best suitable algorithm for diagnosing the transients of a typical nuclear power plant (NPP) is presented. NPP experiences a number of transients during its operations. These transients may be due to equipment failure, malfunctioning of process systems, etc. In case of any undesired plant condition generally known as initiating event (IE), the operator has to carry out diagnostic and corrective actions. The objective of this study is to develop a neural network based framework that will assist the operator to identify such initiating events quickly and to take corrective actions. Optimization study on several neural network algorithms has been carried out. These algorithms have been trained and tested for several initiating events of a typical nuclear power plant. The study shows that the resilient-back propagation algorithm is best suitable for this application. This algorithm has been adopted in the development of operator support system. The performance of ANN for several IEs is also presented.     

Neural and fuzzy model performance evaluation of a dynamic production system

Although mathematical modelling techniques are very well developed, some production processes are difficult to be modelled by these modelling techniques or their math-models are too complex to be used for real-time control due to uncertain, imprecise and vague parameters’ relations. Spray dryers are complex, dynamic and ill-defined production processes. Their product (powder) must have a controllable size distribution consisting of spherical shapes and free-flowing characteristic of particles, which is required for an ideal pressing operation to overcome the product sticking in the dies. The relations of production process' parameters are highly non-linear. In this study, these non-linear parameters were studied and three different soft-computing intelligent models were developed and used to predict uncertain parameter relations. The first is the fuzzy model of the production process; the others are the artificial neural network (ANN) architectures; the back-propagation multilayer perceptron (BPMLP) algorithm and the radial basis function network (RBF). To deal with uncertainty and vagueness of the production system, a method (methodology) based on a fuzzy hierarchical analytic process modelling approach and two ANN approaches was applied. The performance of the BPMLP algorithm was found most vigorous than the RBF and fuzzy modelling approach.     

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers

In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.     

Artificial neural networks for pattern recognition

This tutorial article deals with the basics of artificial neural networks (ANN) and their applications in pattern recognition. ANN can be viewed as computing models inspired by the structure and function of the biological neural network. These models are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between pattern and data to emphasize the need for developing pattern processing systems to address pattern recognition tasks. After introducing the basic principles of ANN, some fundamental networks are examined in detail for their ability to solve simple pattern recognition tasks. These fundamental networks together with the principles of ANN will lead to the development of architectures for complex pattern recognition tasks. A few popular architectures are described to illustrate the need to develop an architecture specific to a given pattern recognition problem. Finally several issues that still need to be addressed to solve practical problems using ANN approach are discussed. This paper is mostly a consolidation of work reported by several researchers in the literature, some of which is cited in the references. The author has borrowed several ideas and illustrations from the references quoted in this paper.     

Using fuzzy self-organising maps for safety critical systems

This paper defines a type of constrained artificial neural network (ANN) that enables analytical certification arguments whilst retaining valuable performance characteristics. Previous work has defined a safety lifecycle for ANNs without detailing a specific neural model. Building on this previous work, the underpinning of the devised model is based upon an existing neuro-fuzzy system called the fuzzy self-organising map (FSOM). The FSOM is type of ‘hybrid’ ANN which allows behaviour to be described qualitatively and quantitatively using meaningful expressions. Safety of the FSOM is argued through adherence to safety requirements—derived from hazard analysis and expressed using safety constraints. The approach enables the construction of compelling (product-based) arguments for mitigation of potential failure modes associated with the FSOM. The constrained FSOM has been termed a ‘safety critical artificial neural network’ (SCANN). The SCANN can be used for non-linear function approximation and allows certified learning and generalisation for high criticality roles. A discussion of benefits for real-world applications is also presented.