A hybrid fault diagnosis approach using neural networks

A hybrid fault diagnosis method is proposed in this paper which is based on the parity equations and neural networks. Analytical redundancy is employed by using parity equations. Neural networks then are used to maximise the signal- to- noise ratio of the residual and to isolate different faults. Effectiveness of the method is demonstrated by applying it to fault detection and isolation for a hydraulic test rig. Real data simulation shows that the sensitivity of the residual to the faults is maximised, whilst that to the unknown input is minimised. The simulated faults are successfully isolated by a bank of neural nets.     

A novel approach for analog fault diagnosis based on neural networks and improved kernel PCA

We have developed a neural-network-based fault diagnosis approach of analog circuits using maximal class separability based kernel principal components analysis (MCSKPCA) as preprocessor. The proposed approach can detect and diagnose faulty components efficiently in the analog circuits by analyzing their time responses. First, using wavelet transform to preprocess the time responses obtains the essential and reduced candidate features of the corresponding response signals. Then, the second preprocessing by MCSKPCA further reduces the dimensionality of candidate features so as to obtain the optimal features with maximal class separability as inputs to the neural networks. This simplifies the architectures reasonably and reduces the computational burden of neural networks drastically. The simulation results show that our resulting diagnostic system can classify the faulty components of analog circuits under test effectively and achieves a competitive classification performance.     

A modular neural network approach to fault diagnosis

Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of "toy" alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation.     

A spiking neural network-based approach to bearing fault diagnosis

Fault diagnosis, with the aim of accurately identifying the presence of various faults as early as possible so at to provide effective information for maintenance planning, has been extensively concerned in advanced manufacturing systems. With the increase of the amount of condition monitoring data, fault diagnosis methods have gradually shifted from the model-based paradigm to data-driven paradigm. Intelligent fault diagnosis approaches which can automatically mine useful information from a huge amount of raw data are becoming promising ways to identify faults of manufacturing systems in the context of massive data. In this paper, the Spiking Neural Network (SNN), as the third generation neural network, is tailored as an intelligent fault diagnosis tool for bearings in rotating machinery. Compared to the perceptron and the back propagation neural network (BPNN) which are respectively the first and second generations of neural networks. SNN, which introduces the concept of time into its operating model can more closely mimic natural neural networks and possesses high bionic characteristics. In the proposed SNN-based approach to bearing fault diagnosis, features extracted from raw vibration signals through the local mean decomposition (LMD) are encoded into spikes to train an SNN with the improved tempotron learning rule. The performance of the proposed method is examined by the CWRU and MFPT datasets, and the experimental results show that the method can achieve a promising accuracy in bearing fault diagnosis.     

A Neural Network Appraoch to Fault Diagnosis in Analog Circuits

Thia paper presents a neural network based fault diagnosis approach for analog circuits,taking the tolerances of circuit elements into account.Specifically,a normalization rule of input information,a pseudo-fault domain border(PFDB)pattern selection method and a new output error function are proposed for training the backpropagation(BP) network to be a fault diagnoser.Experimental results demonstrate that the diagnoser performs as well as or better than any classical approaches in terms of accuracy,and provides at least an order-of-magnitude improvement in post-fault diagnostic speed.     

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.     

Fault diagnosis of a cstr using fuzzy neural networks

On-line process fault diagnosis using fuzzy neural networks is described in this paper. The fuzzy neural network is obtained by adding a fuzzification layer to a conventional feed forward neural network. The fuzzification layer converts increments in on-line measurements and controller outputs into three fuzzy sets: “increase”, “steady”, and “decrease”. Abnormalities in a process are represented by qualitative increments in on-line measurements and controller outputs. These are classified into various categories by the network. By representing abnormalities in qualitative form, training data can be condensed. The fuzzy approach ensures smooth transitions from one fuzzy sets to another and, hence, robustness to measurement noise is enhanced. The technique has been successfully applied to a CSTR system.     

Fuzzy DIFACONN-miner: A novel approach for fuzzy rule extraction from neural networks

Artificial neural networks (ANNs) are mathematical models inspired from the biological nervous system. They have the ability of predicting, learning from experiences and generalizing from previous examples. An important drawback of ANNs is their very limited explanation capability, mainly due to the fact that knowledge embedded within ANNs is distributed over the activations and the connection weights. Therefore, one of the main challenges in the recent decades is to extract classification rules from ANNs. This paper presents a novel approach to extract fuzzy classification rules (FCR) from ANNs because of the fact that fuzzy rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules. A soft computing based algorithm is developed to generate fuzzy rules based on a data mining tool (DIFACONN-miner), which was recently developed by the authors. Fuzzy DIFACONN-miner algorithm can extract fuzzy classification rules from datasets containing both categorical and continuous attributes. Experimental research on the benchmark datasets and comparisons with other fuzzy rule based classification (FRBC) algorithms has shown that the proposed algorithm yields high classification accuracies and comprehensible rule sets.     

A novel approach to fault diagnosis for time-delay systems

In this paper, a novel fault detection and identification (FDI) scheme for time-delay systems is presented. Different from the existing FDI design methods, the proposed approach utilizes fault tracking approximator (FTA) and iterative learning algorithm to obtain estimates of the fault functions. Performance of the FTA is rigorously analyzed by investigating its stability and fault tracking sensitivity properties in the presence of slowly developing or abrupt faults for state delayed dynamic systems. A novel feature of the FTA is that it can simultaneously detect and identify the shape and magnitude of the faults. Additionally, an extension to a class of nonlinear time-delay systems is made by using nonlinear control theories. Finally, the applicability and effectiveness of the proposed FDI scheme is illustrated by a practical industrial process.     

基于多级模糊神经网络的故障诊断在化工生产过程中的应用

化工生产过程一般都非常复杂，如柠檬酸蒸发。由于控制回路与测控参数很多，生产过程的故障检测与诊断问题非常困难，难以做到实时检查，得到其故障信息。所以本文提出一种基于神经网络的多级故障诊断系统。采用三级递阶模糊神经网络，降解整个系统故障诊断问题的复杂性，同时采用所有子神经网络全局并行的推理方式，具有快速处理能力，适合系统实时在线故障诊断。     

Dynamic fuzzy neural networks-a novel approach to functionapproximation

In this paper, an architecture of dynamic fuzzy neural networks (D-FNN) implementing Takagi-Sugeno-Kang (TSK) fuzzy systems based on extended radial basis function (RBF) neural networks is proposed. A novel learning algorithm based on D-FNN is also presented. The salient characteristics of the algorithm are: 1) hierarchical on-line self-organizing learning is used; 2) neurons can be recruited or deleted dynamically according to their significance to the system's performance; and 3) fast learning speed can be achieved. Simulation studies and comprehensive comparisons with some other learning algorithms demonstrate that a more compact structure with higher performance can be achieved by the proposed approach.     

A stock selection strategy using fuzzy neural networks

This paper describes, from a general system-design perspective, an artificial neural network (ANN) approach to a stock selection strategy. The paper suggests a concept of neural gates which are similar to the processing elements in ANN, but generalized into handling various types of information such as fuzzy logic, probabilistic and Boolean information together. Forecasting of stock market returns, assessing of country risk and rating of stocks based on fuzzy rules, probabilistic and Boolean data can be done using the proposed neural gates. Fuzzy logic is known to be useful for decision-making where there is a great deal of uncertainty as well as vague phenomena, but lacks the learning capability; on the other hand, neural networks are useful in constructing an adaptive system which can learn from historical data, but are not able to process ambiguous rules and probabilistic data sets. This paper describes how these problems can be solved using the proposed neural gates.     

A novel dynamic fault restoration mechanism using cluster allocation approach in WDM mesh networks

This work describes a novel distributed algorithm, called the Cluster Allocation Algorithm (CAA), for the restoration scheme in a WDM mesh network. The nodes search for neighboring nodes and establish the relationship between them to build numerous logical clusters. Each cluster has a unique manager, called Cluster Head (CH) that searches for routing path, wavelength assignment (RWA) and restoration paths upon receiving requests from its cluster members. Some clusters might comprise only one CH, and cluster members in each cluster can be directly connected to CH. The communication among clusters is also negotiated through the manager. The selected restoration path is pre-computed from the CH to the destination node with the minimum cost function. Therefore, restoration paths can be sought with quick assignment of wavelength routings when the link, node or channel failure occurs based on the status of traffic load, number of nodes and transmission time. The primary aim of this work is to use clusters near faults to share the restoration load throughout the mesh network. The system performance of the CAA is compared with p-cycle, double cycle and DMRA methodologies in terms of restoration time and non-restoration ratio.     

Model-based fault diagnosis using fuzzy matching

A new fuzzy-model-based approach to fault detection and diagnosis is proposed. The scheme uses a set of fuzzy reference models which describe faulty and fault-free operation, and a classifier based on fuzzy matching for fault diagnosis. The reference models are obtained off-line from simulation data. A fuzzy model which describes the actual behavior of the plant is identified online from normal operating data and compared to each of the reference models. A degree of similarity is evaluated every time the online fuzzy model is identified. Dempster's rule of combination is used to combine new evidence with that already collected. The method of diagnosis accounts for any ambiguity (which may result from fault-free and faulty operation, or different faults, having similar symptoms at a given operating point) by comparing the fuzzy reference models with each other. Results are presented which demonstrate the effectiveness of the scheme when it is used to detect and identify faults in the cooling coil subsystem of the air-handling unit of both simulated and experimental air-conditioning plant     

A novel approach of multisensory fusion to collaborative fault diagnosis in maintenance

Collaborative fault diagnosis can be facilitated by multisensory fusion technologies, as these can give more reliable results with a more complete data set. Although deep learning approaches have been developed to overcome the problem of relying on subjective experience in conventional fault diagnosis, there are two remaining obstacles to collaborative efficiency: integration of multisensory data and fusion of maintenance strategies. To overcome these obstacles, we propose a novel two-part approach: a stacked wavelet auto-encoder structure with a Morlet wavelet function for multisensory data fusion and a flexible weighted assignment of fusion strategies. Taking a planetary gearbox as an example, we use noisy vibration signals from multisensors to test the diagnosis performance of the proposed approach. The results demonstrate that it can provide more accurate and reliable fault diagnosis results than other approaches.     

Object-oriented software fault prediction using neural networks

This paper introduces two neural network based software fault prediction models using Object-Oriented metrics. They are empirically validated using a data set collected from the software modules developed by the graduate students of our academic institution. The results are compared with two statistical models using five quality attributes and found that neural networks do better. Among the two neural networks, Probabilistic Neural Networks outperform in predicting the fault proneness of the Object-Oriented modules developed.     

A fast approach for automatic generation of fuzzy rules bygeneralized dynamic fuzzy neural networks

A fast approach for automatically generating fuzzy rules from sample patterns using generalized dynamic fuzzy neural networks (GD-FNNs) is presented. The GD-FNN is built based on ellipsoidal basis functions and functionally is equivalent to a Takagi-Sugeno-Kang fuzzy system. The salient characteristics of the GD-FNN are: (1) structure identification and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori; (2) fuzzy rules can be recruited or deleted dynamically; (3) fuzzy rules can be generated quickly without resorting to the backpropagation (BP) iteration learning, a common approach adopted by many existing methods. The GD-FNN is employed in a wide range of applications ranging from static function approximation and nonlinear system identification to time-varying drug delivery system and multilink robot control. Simulation results demonstrate that a compact and high-performance fuzzy rule-base can be constructed. Comprehensive comparisons with other latest approaches show that the proposed approach is superior in terms of learning efficiency and performance     

On fuzzy modeling using fuzzy neural networks with theback-propagation algorithm

A fuzzy modeling method using fuzzy neural networks with the backpropagation algorithm is presented. The method can identify the fuzzy model of a nonlinear system automatically. The feasibility of the method is examined using simple numerical data.     

构造型神经网络在故障诊断中的应用研究

提出一种新的故障诊断方法,以便更加有效地解决具有先验知识的故障分类问题。以先验样本点为中心,利用内积判断样本数据的相似度,从而进行聚类分析,在特征空间里作超平面与球面相交,得到一个球面覆盖领域,从而将神经网络训练问题转化为点集的覆盖问题。该算法以构造型神经网络为基础,其特点是直接对故障样本数据进行处理,由于覆盖中心确定,该算法构造出的是隐层元最少的网络结构,有效地克服了传统神经网络训练时间长、学习复杂的问题。计算机仿真实验结果证实了该算法的有效性。