基于模糊神经网络的连铸机故障诊断研究

针对连铸机结晶器液压振动系统故障特点,采用模糊理论与神经网络相结合的方法对其进行故障诊断,用模糊信息处理方法对输入信号进行处理,然后采用神经网络的逼近能力实现连铸机结晶器液压系统振动故障诊断,利用现场数据进行了仿真实验,仿真结果表明该系统具有很好的识别能力,可以对不确定行知识进行很好的处理,提高故障诊断的精度。     

非线性系统的故障诊断技术

文中对非线性系统的故障诊断方面问题给予了归纳总结,指出了基于数学模型方法,基于信号处理方法和基于知识的方法在实现非线性系统故障诊断的基本思想,并进一步指出了各各非线性系统故障诊断方法及可能的发展方向。     

Investigation of engine fault diagnosis using discrete wavelet transform and neural network

An investigation of a fault diagnostic technique for internal combustion engines using discrete wavelet transform (DWT) and neural network is presented in this paper. Generally, sound emission signal serves as a promising alternative to the condition monitoring and fault diagnosis in rotating machinery when the vibration signal is not available. Most of the conventional fault diagnosis techniques using sound emission and vibration signals are based on analyzing the signal amplitude in the time or frequency domain. Meanwhile, the continuous wavelet transform (CWT) technique was developed for obtaining both time-domain and frequency-domain information. Unfortunately, the CWT technique is often operated over a longer computing time. In the present study, a DWT technique which is combined with a feature selection of energy spectrum and fault classification using neural network for analyzing fault signal is proposed for improving the shortcomings without losing its original property. The features of the sound emission signal at different resolution levels are extracted by multi-resolution analysis and Parseval’s theorem [Gaing, Z. L. (2004). Wavelet-based neural network for power disturbance recognition and classification. IEEE Transactions on Power Delivery 19, 1560–1568]. The algorithm is obtained from previous work by Daubechies [Daubechies, I. (1988). Orthonormal bases of compactly supported wavelets. Communication on Pure and Applied Mathematics 41, 909–996.], the“db4”, “db8” and “db20” wavelet functions are adopted to perform the proposed DWT technique. Then, these features are used for fault recognition using a neural network. The experimental results indicated that the proposed system using the sound emission signal is effective and can be used for fault diagnosis of various engine operating conditions.     

Fault diagnosis of power electronic system based on fault gradation and neural network group

We propose a new fault diagnosis approach with fault gradation using BP (back-propagation) neural network group consisting of 3 sub BP neural networks. According to the hazard extents and the occurrence frequencies of different faults, the faults are divided into different grades. The higher the fault grade, the larger the number of the used sub neural networks is. Experimental results show that our approach makes the correctness rate of the fault diagnosis rise greatly (from less than 95.0% to 99.5%) and the performance of the whole fault diagnosis system gets much better especially for the on-line complex systems. The approach proposed in this paper also can be extended to other complex fault diagnosis systems, such as mechanical systems.     

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。     

一类基于神经网络非线性观测器的鲁棒故障检测和诊断

利用神经网络的非线性建模能力，对一类具有建模不确定项的非线性系统提出一种基于观测器的故障检测和诊断的方法。设计的观测器不仅能实现故障检测，而旦应用神经网络设计的故障估计器能在线估计系统中的故障向量。通过分析验证了该方法对系统中的建模误差和外部扰动具有良好的鲁棒性。仿真结果表明所提出的方法是有效的。     

基于改进BP网络算法的故障诊断分析

介绍BP神经网络结构和学习方法,针对误差反向传播神经网络模型学习收敛速度慢、容易陷入局部极小点等缺点,本文对BP网络模型进行了改进。对原始数据采用非线性的归一化函数,提出一种更加有效的学习率改进算法,提高了网络的收敛速度,采用了一种新的权值及阈值初始化方法,以避免训练时误差陷入局部极小解,并对改进BP算法与传统的BP算法进行比较,验证了该算法的优越性。     

一种新的神经网络故障诊断方法

基于数据融合思想，提出一种新的神经网络故障诊断方法。利用系统故障征兆的分散性和复杂性，采用多个神经网络分别对每一类故障进行诊断，网络输入为与输出故障相关联的监测信号的特征值，将各网络输出进行融合，给出最后诊断结果。将该方法应用于斜轴式无铰柱塞液压泵故障诊断，结果表明能够充分利用各种特征信息，提高诊断速度和精确度。     

Self adaptive growing neural network classifier for faults detection and diagnosis

Fault detection and diagnosis have gained widespread industrial interest in machine monitoring due to their potential advantage that results from reducing maintenance costs, improving productivity and increasing machine availability. This article develops an adaptive intelligent technique based on artificial neural networks combined with advanced signal processing methods for systematic detection and diagnosis of faults in industrial systems based on a classification method. It uses discrete wavelet transform and training techniques based on locating and adjusting the Gaussian neurons in activation zones of training data. The learning (1) provides minimization in the number of neurons depending on cost error function and other stopping criterions; (2) offers rapid training and testing processes; (3) provides accuracy in classification as confirmed by the results on real signals. The method is applied to classify mechanical faults of rotary elements and to detect and isolate disturbances for a chemical process. Obtained results are analyzed, explained and compared with various methods that have been widely investigated for fault diagnosis.     

基于神经网络的装载机故障诊断模型

首先分析了故障诊断的常用方法及其优缺点,设计了装载机故障诊断的流程,并阐述了流程中一些重要环节的设计和功能。然后在分析装载机信号的基础上提取了装载机信号的故障特征,相继建立了用于装载机故障诊断的BP神经网络和组合神经网络模型,并比较两者的优缺,选择更适合装载机故障诊断的模型。     

A neural networks-based negative selection algorithm in fault diagnosis

Inspired by the self/nonself discrimination theory of the natural immune system, the negative selection algorithm (NSA) is an emerging computational intelligence method. Generally, detectors in the original NSA are first generated in a random manner. However, those detectors matching the self samples are eliminated thereafter. The remaining detectors can therefore be employed to detect any anomaly. Unfortunately, conventional NSA detectors are not adaptive for dealing with time-varying circumstances. In the present paper, a novel neural networks-based NSA is proposed. The principle and structure of this NSA are discussed, and its training algorithm is derived. Taking advantage of efficient neural networks training, it has the distinguishing capability of adaptation, which is well suited for handling dynamical problems. A fault diagnosis scheme using the new NSA is also introduced. Two illustrative simulation examples of anomaly detection in chaotic time series and inner raceway fault diagnosis of motor bearings demonstrate the efficiency of the proposed neural networks-based NSA.     

Diverse neural net solutions to a fault diagnosis problem

The development of a neural net system for fault diagnosis in a marine diesel engine is described. Nets were trained to classify combustion quality on the basis of simulated data. Three different types of data were used: pressure, temperature and combined pressure and temperature. Subsequent to training, three nets were selected and combined by means of a majority voter to form a system which achieved 100% generalisation to the test set. This performance is attributable to a reliance on the software engineering concept of diversity. Following experimental evaluation of methods of creating diverse neural nets solutions, it was concluded that the best results should be obtained when data is taken from two different sensors (e.g. a pressure and a temperature sensor), or where this is not possible, when new data sets are created by subjecting a set of inputs to non-linear transformations. These conclusions have far reaching implications for other neural net applications.     

一种基于输入训练神经网络的非线性PCA 故障诊断方法

简要讨论了线性PCA故障诊断方法存在的问题，提出一种基于输入训练神经网络的非线性PCA故障诊断方法。该方法首先利用输入训练神经网络和BP网络双网络机制，实现非线性主元的识别，并采用统计方法进行故障检测与故障分离。对CSTR的仿真研究结果表明，该方法能够克服线性PCA方法在提取过程变量的非线性特征方面存在的不足，并能够准确地进行故障检测和分离。     

Development of an expert system for fault diagnosis in scooter engine platform using fuzzy-logic inference

In the present study, a fault diagnosis system using acoustic emission with an adaptive order tracking technique and fuzzy-logic interference for a scooter platform is described. Order tracking of acoustic or vibration signal is a well-known technique that can be used for fault diagnosis of rotating machinery. Unfortunately, most of the conventional order-tracking methods are primarily based on Fourier analysis with the revolution of the machinery. Thus, the frequency smearing effect often arises in some critical conditions. In the present study, the order tracking problem is treated as the tracking of frequency-varying bandpass signals and the order amplitudes can be calculated with high resolution. The order amplitude figures are then used for creating the data bank in the proposed intelligent fault diagnosis system. A fuzzy-logic inference is proposed to develop the diagnostic rules of the data base in the present fault diagnosis system. The experimental works are carried to evaluate the effect of the proposed system for fault diagnosis in a scooter platform under various operation conditions. The experimental results indicated that the proposed expert system is effective for increasing accuracy in fault diagnosis of scooters.     

Online fault detection and isolation of nonlinear systems based on neurofuzzy networks

An online fault detection and isolation (FDI) technique for nonlinear systems based on neurofuzzy networks (NFN) is proposed in this paper. Two NFNs are used. The first one trained by data obtained under normal operating condition models the system and the second one trained online models the residuals. Fuzzy rules that are activated under fault free and faulty conditions are extracted from the second NFN and stored in the symptom vectors using a binary code. A fault database is then formed from these symptom vectors. When applying the proposed FDI technique, the NFN that models the residuals is updated recursively online, from which the symptom vector is obtained. By comparing this symptom vector with those in the fault database, faults are isolated. Further, the fuzzy rules obtained from the symptom vector can also provide linguistic information to experienced operators for identifying the faults. The implementation and performance of the proposed FDI technique is illustrated by simulation examples involving a two-tank water level control system under faulty conditions.     

A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting

We describe in this paper the application of a modular neural network architecture to the problem of simulating and predicting the dynamic behavior of complex economic time series. We use several neural network models and training algorithms to compare the results and decide at the end, which one is best for this application. We also compare the simulation results with the traditional approach of using a statistical model. In this case, we use real time series of prices of consumer goods to test our models. Real prices of tomato in the U.S. show complex fluctuations in time and are very complicated to predict with traditional statistical approaches. For this reason, we have chosen a neural network approach to simulate and predict the evolution of these prices in the U.S. market.     

故障诊断的信息融合方法

对基于信息融合的故障诊断方法进行综述．首先简要阐述信息融合的基本概念以及信息融合与故障诊断的关系;然后介绍贝叶斯定理融合故障诊断、模糊融合故障诊断、证据理论融合故障诊断、神经网络融合故障诊断和集成信息融合故障诊断方法的诊断原理与步骤。并分析其特点和局限性;最后给出了信息融合故障诊断研究的若干发展方向．     

Knowledge distilling based model compression and feature learning in fault diagnosis

Recently, there has been interest in developing diagnosis methods that combine model-based and data-driven diagnosis. In both approaches, selecting the relevant measurements or extracting important features from historical data is a key determiner of the success of the algorithm. Recently, deep learning methods have been effective in automating the feature selection process. Autoencoders have been shown to be an effective neural network configuration for extracting features from complex data, however, they may also learn irrelevant features. In addition, end-to-end classification neural networks have also been used for diagnosis, but like autoencoders, this method may also learn unimportant features thus making the diagnostic inference scheme inefficient. To rapidly extract significant fault features, this paper employs end-to-end networks and develops a new feature extraction method based on importance analysis and knowledge distilling. First, a set of cumbersome neural network models are trained to predict faults and some of their internal values are defined as features. Then an occlusion-based importance analysis method is developed to select the most relevant input variables and learned features. Finally, a simple student neural network model is designed based on the previous analysis results and an improved knowledge distilling method is proposed to train the student model. Because of the way the cumbersome networks are trained, only fault features are learned, with the importance analysis further pruning the relevant feature set. These features can be rapidly generated by the student model. We discuss the algorithms, and then apply our method to two typical dynamic systems, a communication system and a 10-tank system employed to demonstrate the proposed approach.     

Fault diagnosis on bottle filling plant using genetic-based neural network

Timely detection of the pneumatic system problems is important in industry. Many techniques have been employed to solve this problem. In this paper, Genetic Algorithm (GA) based optimal configuration of neural networks is proposed for fault diagnostic of bottle filling systems. Back-propagation is used for neural networks algorithm. The back-propagation algorithm had six inputs and one output. A fitness function was designed to the minimize execution time of ANN model by keeping the number of hidden layer(s) and nodes as low as possible while the mean square error of estimated output error is minimized. The designed GA–ANN combination and the graphical user interface (GUI) eliminate the trial and error process for selection of the fastest and most accurate configuration. The performance of the proposed system was evaluated by using experimental data collected at a pneumatic work cell which attach caps to the bottles. The sensory data was collected at normal operating conditions and a series of faults were imposed to the system such as missing bottle, attaching nonworking bottle caps at two different cylinders, two air pressure problems (insufficient and low air), and not filling water. The study demonstrated the convenience, accuracy and speed of the proposed GA–NN environment. It may also be used for training for selection of ANN configurations at various applications.     

A neural network model for fault detection in conjunction with a programmable logic controller

This paper discusses the feasibility of using neural networks as a tool in the fault detection process. A neural network is integrated with a state language programmable logic controller, an important device in an automatic control system. Time series data related to time spent in a state is gathered and used as input into a neural network, for the purpose of identifying when a fault has occurred. A feedforward neural network is used to identify which (if any) of three types of faults may have occurred. Experimental results related to sensitivity and accuracy measures are presented. A brief review of related applications and research is also presented.