An improved convolutional neural network with an adaptable learning rate towards multi-signal fault diagnosis of hydraulic piston pump

Hydraulic piston pump is a vital component of hydraulic transmission system and plays a critical role in some modern industrials. On account of the deficiencies of traditional fault diagnosis in preprocessing of original data and feature extraction, the intelligent methods based on deep learning accomplish the automatic learning of fault information by integrating feature extraction and classification. As a popular deep learning model, convolutional neural network (CNN) has been demonstrated to be potent and effective in image classification. In this research, an improved intelligent method based on CNN with adapting learning rate is constructed for fault diagnosis of a hydraulic piston pump. Firstly, three raw signals are converted into two dimensional time–frequency images by continuous wavelet transform, including vibration signal, pressure signal and sound signal. Secondly, an improved deep CNN model is built with an adaptive learning rate strategy for identifying the different fault types. Moreover, t-distributed stochastic neighbor embedding is employed to visualize the distribution of features learned by the main layers of CNN model. Confusion matrix is used to analyze the classification accuracy of each fault type. Compared with the CNN model without adapting learning rate, the improved model achieves a higher accuracy based on the selected three kinds of signals. Experiments indicate that the improved CNN model can effectively and accurately identify various faults for a hydraulic piston pump.     

基于CNN-LSTM的QAR数据特征提取与预测

针对传统数据驱动的故障诊断方法难以从QAR数据中提取有效特征的问题,提出一种融合卷积神经网络（convolutional neural network,CNN）与长短时记忆网络（long short-term memory,LSTM）的双通道融合模型CNN-LSTM。CNN与LSTM分别作为两个通道,通过注意力机制（attention）融合,从而使模型能同时表达数据在空间维度和时间维度上的特征,并以时间序列预测的方式验证融合模型特征提取的有效性。实验结果表明,双通道融合模型与单一的CNN、LSTM相比,能够更有效地提取数据特征,模型单步预测与多步预测误差平均降低35.3%。为基于QAR数据的故障诊断提供一种新的研究思路。     

基于ISCNN-LightGBM的轴承故障诊断

在传统卷积神经网络与分类器相结合的故障诊断方法中, CNN用于故障特征提取时, 存在着提取的特征质量不高与运行时间较长的问题. 针对以上问题, 本文提出了一种基于改进单层卷积神经网络及LightGBM的故障诊断模型. 该模型通过将特征距离函数嵌入CNN的损失函数中, 提升了CNN特征提取的能力, 增强了CNN与后续分类器之间的联系, 从而提升了整体模型的故障诊断能力. 于此同时, 经过改进的单层的卷积神经网络进一步缩短了模型运行的时间, 提升了模型的诊断效率. 通过对两个不同的公共数据集进行对比实验, 其结果表明, 本文所提诊断模型对多种轴承故障的诊断准确率与诊断效率显著高于其他诊断模型.     

基于融合式神经网络的微生物生长环境关系抽取

为了构建完整的微生物生长环境关系数据库,提出基于卷积神经网络-长短时记忆(CNN-LSTM)的关系抽取系统.结合卷积神经网络(CNN)和长短时记忆(LSTM),实现对隐含特征的深度学习,提取分布式词向量特征和实体位置特征作为模型的特征输入.对比实验验证加入特征后CNN-LSTM模型的优势,并将CNN模型的特征输出作为LSTM模型的特征输入.在Bio-NLP 2016共享任务发布的BB-event语料集上得到目前最好的结果.     

基于图卷积网络的迁移学习轴承服役故障诊断

深度学习方法被广泛应用于轴承故障诊断,但在实际工程应用中,轴承服役期间的真实服役故障数据不易收集,缺乏数据标签,难以进行充分的训练。针对轴承服役故障诊断困难的问题,提出了一种基于图卷积网络（GCN）的迁移学习轴承服役故障诊断模型。该模型从数据充足的人工模拟损伤故障数据中学习故障知识,并迁移到真实的服役故障上,以提高服役故障的诊断准确率。具体来说,通过将人工模拟损伤故障数据和服役故障数据的原始振动信号由小波变换转换为同时具有时间和频率信息的时频图,并将得到的时频图输入到图卷积层中进行学习,从而有效地提取源域和目标域的故障特征表示;然后计算源域和目标域的数据分布之间的Wasserstein距离来度量两个数据分布之间的差异,通过最小化数据分布差异,构建了一个能诊断轴承服役故障的故障诊断模型。在不同的轴承故障数据集和不同工作条件下设计了多种不同的任务进行实验,实验结果表明,该模型具有诊断轴承服役故障的能力,同时也能从一个工作条件迁移到另一工作条件,在不同组件类型和不同工作条件之间进行故障诊断。     

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CNN-LSTM-PSO的私有云故障检测

有效对私有云系统进行故障检测对于保障IT系统稳定性及开展可靠性信息活动具有重要的实际意义。为此从私有云系统的历史趋势数据出发,将卷积网络(CNN)和长短期记忆(LSTM)循环神经网络结合,提出了基于粒子群优化算法(PSO)的CNN-LSTM-PSO的混合模型,实现对私有云的故障检测。采用X11算法等技术对数据进行预处理,使用CNN网络提取监控指标时序数据的相关特征信息,并通过训练LSTM网络参数建立CNN-LSTM预测模型,设计了PSO算法对预测模型进行参数选优,减小预测误差,并以高斯正态分布确定阈值范围,实现故障的精准检测。通过和传统单一预测模型以及现有的一些组合预测模型的对比,CNN-LSTM-PSO模型预测后结果的均方根误差、平均绝对误差和平均百分比误差都低于其余模型。实验结果验证了模型在预测效果上具备更高的精度和更快的预测速度,在私有云的故障检测中精确性和实时性都具有良好效果。     

One-dimensional convolutional auto-encoder-based feature learning for fault diagnosis of multivariate processes

Noise and high-dimension of process signals decrease effectiveness of those regular fault detection and diagnosis models in multivariate processes. Deep learning technique shows very excellent performance in high-level feature learning from image and visual data. However, the large labeled data are required for deep neural networks (DNNs) with supervised learning like convolutional neural network (CNN), which increases the time cost of model construction significantly. A new DNN model, one-dimensional convolutional auto-encoder (1D-CAE) is proposed for fault detection and diagnosis of multivariate processes in this paper. 1D-CAE is utilized to learn hierarchical feature representations through noise reduction of high-dimensional process signals. Auto-encoder integrated with convolutional kernels and pooling units allows feature extraction to be particularly effective, which is of great importance for fault detection and diagnosis in multivariate processes. The comparison between 1D-CAE and other typical DNNs illustrates effectiveness of 1D-CAE for fault detection and diagnosis on Tennessee Eastman Process and Fed-batch fermentation penicillin process. The proposed method provides an effective platform for deep-learning-based process fault detection and diagnosis of multivariate processes.     

基于一维卷积神经网络与长短期记忆网络结合的电池荷电状态预测方法

针对电池荷电状态（SOC）预测的精确度与稳定性问题以及深层神经网络的梯度消失问题,提出一种基于一维卷积神经网络（1D CNN）与长短期记忆（LSTM）循环神经网络（RNN）结合的电池SOC预测方法——1D CNN-LSTM模型。1D CNN-LSTM模型将电池的电流、电压和电阻映射到目标值SOC。首先,通过一层一维卷积层从样本数据中提取出高级数据特征,并充分地利用输入数据的特征信息;其次,使用一层LSTM层保存历史输入信息,从而有效地预防重要信息的丢失;最后,通过一层全连接层输出电池SOC预测结果。使用电池的多次循环充放电实验数据训练提出的模型,分析对比不同超参数设置下1D CNN-LSTM模型的预测效果,并通过训练模型来调节模型的权重系数和偏置参数,从而确定最优的模型设置。实验结果表明,1D CNN-LSTM模型具有准确且稳定的电池SOC预测效果。该模型的平均绝对误差（MAE）、均方误差（MSE）和最大预测误差分别为0.402 7%、0.002 9%和0.99%。     

利用深度学习的施工人员安全隐患行为诊断控制方法

为了对建筑施工现场存在安全隐患的行为进行诊断控制,提出通过深度学习的方式对建筑施工现场工人的不安全行为进行识别;第一,要对人体骨骼运动模型进行提取,将提取得到的信息作为人体姿态以及运动发生变化的新模态信息,并针对以人体姿态为依据实现骨架信息提取这一过程进行简单介绍,再进一步提出CNN-LSTM模型,该模型能够对空间特征提取性能进行优化;过利用BN-Inception作为CNN-LSTM行为识别模型所需要的空间特征提取器,对所有视频帧中包含的空间结构信息进行提取过程的训练;再通过借助长短时记忆网络(LSTM)针对完整视频中的所有帧进行时序信息的建模,最终通过模型所得出的结果即为LSTM在最终时刻的预测输出;通过相关研究能够证明,利用CNN-LSTM模型获取的信息准确率能够达到88.67%,能够对单模态行为识别模型在识别过程中的准确率进行优化。     

基于多标签学习的旋转机械分级复合故障诊断

传统故障诊断方法大多是针对单一故障类型,然而在实际工业中多种故障会同时出现,即复合故障.针对复合故障诊断问题,一些学者引入多标签学习思想,多标签K近邻算法(ML-KNN)就是其中之一.然而ML-KNN算法作为一阶算法,只考虑标签与对应样本数据间的关系,却忽略了标签间的联系.针对该问题提出一种分级多标签学习算法,名为分层多标签K近邻算法(HML-KNN). HML-KNN算法将机械设备的退化阶段和故障类型分为两级,将第1级得到的标签信息进行转化,转化后的信息作为新特征放入第2级进行判断. HML-KNN算法是一种高阶算法,考虑了全局的标签信息,并在算法中包含了标签的特征转化,使得到的结果准确率更高.最后通过XJTU-SY数据集验证HML-KNN算法在处理复合故障诊断问题上的优越性.     

Research on fault diagnosis method of MS-CNN rolling bearing based on local central moment discrepancy

Transfer learning is an excellent approach to deal with the problem that the target domain label can not be adequately obtained when rolling bearing cross-condition fault detection. A transfer learning fault diagnosis method of multi-scale CNN rolling bearings based on local central moment discrepancy is presented in this research. The method maps bearing vibration data to a shared space by building a shared multi-scale feature extraction structure and fully connected layers. The source domain label and target domain pseudo-label are used to divide the category subspace in the shared space. And then the local central moment discrepancy is used to match source and target domain in the category subspace to realize fault knowledge transfer under different conditions. The experimental findings reveal that multi-scale CNN migration diagnosis based on local central moment discrepancy has superior accuracy and stability in diverse diagnostic tasks when compared to classic transfer learning approaches.     

基于CNN-LSTM及深度学习的风电场时空组合预测模型

为了更好地预测风电场的风电功率,提取风电场相邻站点之间时空信息和潜在联系,提出了一种基于卷积神经网络(CNN)、互信息(mutual information, MI)法、长短时记忆网络(LSTM)、注意力机制(AT)和粒子群优化(PSO)的短期风电场预测模型(MI-CNN-ALSTM-PSO)。CNN用于提取不同站点的空间特征,LSTM则用于获取多个站点的风电数据的时间依赖信息,据此设计CNN-LSTM时空预测模型,并结合深度学习算法,如MI特征选择、 AT注意力机制、 PSO参数优化,对模型进一步改进。通过两个海岛风电场的实验数据分析可知,所提模型具有最优的统计误差,CNN-LSTM模型可以高效提取风电场时空信息并进行时间序列预测,而结合深度学习算法(MI、 AT和PSO)后的组合模型能进一步提高风电功率预测精度和稳定性。     

移动通信网络的中性集故障诊断方法研究

随着故障诊断技术从面向网络设备逐渐向面向用户、面向业务的转变,故障识别能力和故障处理能力的不断提高,快速、准确地检测重大故障并及时收集重大故障信息,对缩短故障时长、提高工作效率、提升网络服务水平都具有重大意义。基于中性集故障诊断方法,本文提出一种故障特征权重和阶段数据权重的计算方法,并将此方法应用于移动通信网络的话音业务的故障诊断过程中。根据收集到的数据的统计分析结果,判断移动通信网络中未知故障样本的故障类型。通过举例分析,验证了本文所提出的故障特征权重和阶段数据权重设计方法的优越性。     

MDT-CNN-LSTM模型的股价预测研究

股价预测一直是投资者在股票市场中关注的焦点.近年来,深度学习技术在这一领域得到广泛应用.在融合卷积神经网络(CNN)和长短时记忆网络(LSTM),构建CNN-LSTM模型的基础上,引入多向延迟嵌入的张量处理技术MDT(mutiway-delay-embedding),对每日股票因子向量进行因子重构,生成汉克尔矩阵,按时...     

基于神经网络技术的机器人并发故障自动诊断方法

为了提高柔性负载抓握机器人的故障检测能力,提出基于神经网络技术的机器人并发故障自动诊断方法.运用高分辨的智能传感器信息识别技术,结合刚度和强度等机械结构特征分析,构建柔性负载抓握机器人的故障信息采集模型,采用变刚度原理,提取柔性负载抓握机器人的振荡信息特征,通过谱特征检测和动态信息融合进行柔性负载抓握机器人的故障信息的...     

基于深度学习的股票趋势预测算法

针对股票趋势预测难的问题,提出一种利用CNN和LSTM进行特征提取,并结合注意力机制和对抗训练的股票趋势预测算法——AACL（Adversarial Attentive CNN-LSTM）算法。该算法利用CNN提取股票的整体趋势信息,LSTM提取股票的短期波动信息,并通过注意力机制将多个股票联系起来,捕捉股票之间的涨跌关系。算法还引入了对抗训练,通过对数据进行干扰,提高算法的鲁棒性。为了验证算法的有效性,在KDD17、ACL18和China50这3个数据集上进行实验,并与现有的算法进行比较,实验结果表明本文提出的算法可以获得最优的预测效果。     

一种改进的支持向量数据描述故障诊断方法

针对故障诊断中故障类样本难于获取以及不均衡类问题,提出了基于粒子群和滑动窗口的支持向量数据描述（M-SVDD）故障诊断方法.该方法利用粒子群优化支持向量数据描述的核参数,同时引入滑动窗口技术,通过大窗口大小来控制故障诊断模型的训练样本数,根据小窗口的预测误差变化动态调整大窗口的大小.采用该方法对铜转炉吹炼过程进行故障诊断的实验结果表明,该方法能有效抑制过拟合现象,具有故障敏感性高、泛化能力强等特点.     

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.     

Rolling element bearing fault diagnosis using wavelet transform

This paper is focused on fault diagnosis of ball bearings having localized defects (spalls) on the various bearing components using wavelet-based feature extraction. The statistical features required for the training and testing of artificial intelligence techniques are calculated by the implementation of a wavelet based methodology developed using Minimum Shannon Entropy Criterion. Seven different base wavelets are considered for the study and Complex Morlet wavelet is selected based on minimum Shannon Entropy Criterion to extract statistical features from wavelet coefficients of raw vibration signals. In the methodology, firstly a wavelet theory based feature extraction methodology is developed that demonstrates the information of fault from the raw signals and then the potential of various artificial intelligence techniques to predict the type of defect in bearings is investigated. Three artificial intelligence techniques are used for faults classifications, out of which two are supervised machine learning techniques i.e. support vector machine, learning vector quantization and other one is an unsupervised machine learning technique i.e. self-organizing maps. The fault classification results show that the support vector machine identified the fault categories of rolling element bearing more accurately and has a better diagnosis performance as compared to the learning vector quantization and self-organizing maps.