基于LMD-PE与神经网络的刀具故障诊断方法

针对刀具故障诊断信号信噪比低、诊断结果不准确等问题,采用局域均值分解(LMD)结合排列熵(PE)来处理采集到的刀具加工时的振动信号,然后将提取到的特征向量输入到训练好的长短期记忆神经网络(LSTM)中得到诊断结果,为了提高LSTM的诊断效率,结合卷积神经网络(CNN)对LSTM进行了改造;试验表明,文章提出的方法诊断准确率比BP神经网络提高了将近12%,改进LSTM网络比传统LSTM的诊断时间缩短了50%。     

基于神经网络专家系统的钻井事故诊断

结合石油钻井工程的实际情况,依据钻井过程的监测参数,设计了利用神经网络进行知识获取、专家系统进行事故诊断的钻井工程事故智能诊断系统。通过神经网络对钻井复杂问题实例的不断学习训练,获得用于智能诊断的知识,完成对事故发生可能性的初步诊断。经过专家系统的进一步启发式反向推理验证事故是否存在,给出最后确诊,以此监控钻井参数,指导钻井参数调整的实施。应用实例结果表明,该智能诊断系统应用于钻井事故诊断是有效的,对减少钻井事故的发生与发展具有重大的实际应用价值。     

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。     

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.     

模糊神经网络变幅液压系统故障诊断

针对变幅液压系统复杂性、不确定性、模糊性的特点,提出基于故障树的模糊神经网络作为变幅液压系统故障诊断的方法。该方法利用故障树知识提取变幅液压系统故障诊断的输入变量和输出变量,引入模糊逻辑的概念,采用模糊隶属函数来描述这些故障的程度,利用Levenberg-Marquardt优化算法对神经网络进行训练,系统推理速度快,容错能力强,并通过实例分析验证了变幅液压系统模糊神经网络故障诊断的有效性。     

基于小波神经网络的齿轮箱故障诊断研究

论述了小波神经网络的系统结构及算法,并根据齿轮振动信号的频域变化特征,提取特征向量作为输入,利用小波神经网络建立特征向量与故障模式之间的映射关系,建立了基于该算法的齿轮故障诊断模型。仿真结果表明：与传统的BP神经网络相比,该模型显著缩短了训练时间。该小波神经网络进行机械故障诊断是有效的。     

模糊神经网络抓斗纠偏系统故障诊断

针对抓斗纠偏系统复杂性、不确定性、模糊性的特点,提出基于故障树的模糊神经网络作为抓斗纠偏系统故障诊断的方法。该方法利用故障树知识提取抓斗纠偏系统故障诊断的输入变量和输出变量,引入模糊逻辑的概念,采用模糊隶属函数来描述故障的程度,利用Levenberg-Marquardt优化算法对神经网络进行训练,系统推理速度快、容错能力强,并通过实例分析验证了抓斗纠偏系统模糊神经网络故障诊断的有效性。     

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.     

BP-LCD方法及其在滚动轴承故障诊断中的应用

针对局部特征尺度分解（Local Characteristic-scale Decomposition,LCD）方法中严重的端点效应,将BP神经网络应用到信号的延拓中,提出了一种提出基于BP神经网络延拓局部特征尺度分解（BP neural network endpoint extension Local Characteristic-scale Decomposition,BP-LCD）方法。该方法首先利用BP神经网络将待分解信号进行两端预测延拓,然后对延拓后的曲线进行LCD分解。通过仿真信号的分析,验证了该方法可以有效地抑制LCD方法中的端点效应;将该方法应用到实际滚动轴承的故障诊断中,结果表明了该方法的有效性。     

小波神经网络在滚动轴承故障诊断中的应用

结合小波变换和神经网络的优势给出小波神经网络的结构模型,研究了小波神经网络的学习算法;针对传统算法收敛速度慢等问题,从学习率和引入动量项两个方面对算法进行改进。应用小波网络对滚动轴承的典型故障进行实例诊断。以7216圆锥轴承在实验台上所测取的数据进行网络训练。用振动信号为网络输入向量,给出训练结果。仿真实例表明,采用小波神经网络能够很好地对故障进行分类,其收敛速度明显要快于相同条件BP神经网络,有效地实现了滚动轴承的故障诊断。     

非线性电路的神经网络故障诊断方法

针对非线性动态电子电路,提出一种基于神经网络的故障诊断方法。通过故障字典的建立,对电路故障响应进行预处理后得到的故障特征作为神经网络的输入,然后利用神经网络对各种状态下的特征向量进行分类决策,对故障类别进行辨识,并对电路进行了可测性分析,从而实现非线性电路的故障诊断。详细的仿真过程及结果表明, 该方法有效地解决了非线性电路辨识难的问题,能较好地对故障模式进行分类,取得了满意的诊断效果。     

A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images

As an essential part of hydraulic transmission systems, hydraulic piston pumps have a significant role in many state-of-the-art industries. Thus, it is important to implement accurate and effective fault diagnosis of hydraulic piston pumps. Owing to the heavy reliance of shallow machine learning models on the expertise and experience of engineers, fault diagnosis based on deep models has attracted significant attention from academia and industry. To construct a deep model with good performance, it is necessary and challenging to tune the hyperparameters (HPs). Since many existing methods focus on manual tuning and use common search algorithms, it is meaningful to explore more intelligent algorithms that can automatically optimize the HPs. In this paper, Bayesian optimization (BO) is employed for adaptive HP learning, and an improved convolutional neural network (CNN) is established for fault feature extraction and classification in a hydraulic piston pump. First, acoustic signals are transformed into time–frequency distributions by a continuous wavelet transform. Second, a preliminary CNN model is built by setting initial HPs. The range of each HP to be optimized is identified. Third, BO is employed to select the optimal combination of HPs. An improved model called CNN-BO is constructed. Finally, the diagnostic efficiency of CNN-BO is analyzed using a confusion matrix and t-distributed stochastic neighbor embedding. The classification performance of different models is compared. It is found that CNN-BO has a higher accuracy and better robustness in fault diagnosis for a hydraulic piston pump. This research will provide a basis for ensuring the reliability and safety of the hydraulic pump.     

一种大规模模拟电路快速故障诊断新方法

针对传统大规模模拟电路故障诊断方法在多故障条件下的故障定位过程复杂、测前工作量大等问题, 提出了一种新的故障诊断方法——成组撕裂法。将大规模模拟电路按照拓扑特性和成组撕裂准则进行撕裂, 得到低维度的故障特征向量; 基于模式识别思想, 选用具有高度并行分类能力的神经网络作为分类器, 隐含层传递激发函数选择具有快速收敛特性的小波函数。经仿真验证该方法能实现故障特征向量的快速分类并得出故障诊断结果。与目前已有的互校验（multiple-test-condition, MTC）和交叉撕裂搜索法相比, 该方法有测前工作量小、诊断次数和计算量少、对多故障检测能力和工程实践性强等特点。     

Finding iterative roots with a spiking neural network

In recent years, both multilayer perceptrons and networks of spiking neurons have been used in applications ranging from detailed models of specific cortical areas to image processing. A more challenging application is to find solutions to functional equations in order to gain insights to underlying phenomena. Finding the roots of real valued monotonically increasing function mappings is the solution to a particular class of functional equation. Furthermore, spiking neural network approaches in solving problems described by functional equations, may be an useful tool to provide important insights to how different regions of the brain may co-ordinate signaling within and between modalities, thus providing a possible basis to construct a theory of brain function. In this letter, we present for the first time a spiking neural network architecture based on integrate-and-fire units and delays, that is capable of calculating the functional or iterative root of nonlinear functions, by solving a particular class of functional equation.     

基于径向基神经网络的新型齿轮故障诊断方法

非平稳工况下的齿轮故障检测是一项非常困难的工作,由于齿轮振动信号的复杂性,导致故障特征提取和故障诊断困难.针对这些问题,基于径向基(radial basis function, RBF)神经网络,提出一种在变速条件下齿轮的故障诊断方法 CIHDRFD.首先利用自适应白噪声的完整集成经验模态分解(complete ensemble empirical mode decomposition with adaptive noise, CEEMDAN),将原始振动信号分解为多个固有的模态函数(intrinsic mode function, IMF),并通过计算其信息熵(information entropy, IE)筛选出IE最小的4个IMF作为特征IMF;然后利用希尔伯特变换(hilbert transform, HT)处理特征IMF并求出Hilbert包络谱,利用Hilbert包络谱构建故障特征向量;最后利用改进的双RBF神经网络进行故障检测.通过搭建齿轮故障检测平台验证CIHDRFD方法的有效性,实验结果表明, CIHDRFD方法适用于齿轮故障诊断,在速度波动为3%的情况下,诊断准确率...     

基于粗糙集与神经网络的故障诊断研究

通过引入粗糙集理论,利用可辨识矩阵约简算法对故障诊断决策表进行属性约简,剔除其中不必要的属性,然后构造改进的BP神经网络作为粗糙集的后端处理机,构造了基于粗糙集与神经网络的故障诊断模型。仿真结果表明,该方法可以有效地减少输入层个数,简化神经网络结构,减少网络的训练时间,在故障诊断中有良好的应用前景。     

A small spiking neural network with LQR control applied to the acrobot

This paper presents the results of a computer simulation which, combined a small network of spiking neurons with linear quadratic regulator (LQR) control to solve the acrobot swing-up and balance task. To our knowledge, this task has not been previously solved with spiking neural networks. Input to the network was drawn from the state of the acrobot, and output was torque, either directly applied to the actuated joint, or via the switching of an LQR controller designed for balance. The neural network’s weights were tuned using a (μ + λ)-evolution strategy without recombination, and neurons’ parameters, were chosen to roughly approximate biological neurons.     

AKSNet: A novel convolutional neural network with adaptive kernel width and sparse regularization for machinery fault diagnosis

Convolutional kernels have significant affections on feature learning of convolutional neural network (CNN). However, it is still a challenging problem to determine appropriate kernel width. Moreover, some features learned by convolutional layers are still redundant and noisy. Thus, adaptive selection of kernel width and feature selection of feature maps are key techniques to improve feature learning performance of CNNs. In this paper, a new deep neural network (DNN) model, adaptive kernel sparse network (AKSNet) is proposed to extract multi-scale fault features from one-dimensional (1-D) vibration signals. Firstly, an adaptive kernel selection method is developed, where multiple branches with different kernels are used to extract multi-scale features from vibration signals. Channel-wise attention is developed to fuse features generated by these kernels to obtain different informative scales. Secondly, a spatial attention is used for dynamic receptive field to focus on salient region of feature maps. Thirdly, a sparse regularization layer is embedded in the deep network to further filter noise and highlight impaction of the feature maps. Finally, two cases are adopted to verify effectiveness of AKSNet-based feature learning for bearing fault diagnosis. Experimental results show that AKSNet can effectively extract features from multi-channel vibration signals and then improves fault diagnosis performance of the classifier significantly. AKSNet shows better recognition performance in comparison with that of shallow neural networks and other typical DNNs.     

A Gaussian-guided adversarial adaptation transfer network for rolling bearing fault diagnosis

Most current unsupervised domain networks try to alleviate domain shifts by only considering the difference between source domain and target domain caused by the classifier, without considering task-specific decision boundaries between categories. In addition, these networks aim to completely align data distributions, which is difficult because each domain has its characteristics. In light of these issues, we develop a Gaussian-guided adversarial adaptation transfer network (GAATN) for bearing fault diagnosis. Specifically, GAATN introduces a Gaussian-guided distribution alignment strategy to make the data distribution of two domains close to the Gaussian distribution to reduce data distribution discrepancies. Furthermore, GAATN adopts a novel adversarial training mechanism for domain adaptation, which designs two task-specific classifiers to identify target data to consider the relationship between target data and category boundaries. Massive experimental results prove that the superiority and robustness of the proposed method outperform existing popular methods.