基于DBNs的轮毂电机机械故障在线诊断方法

为实现电动汽车用轮毂电机运行状态在线监测及其安全评估,提出一种基于动态贝叶斯网络(dynamic Bayesian networks,简称DBNs)的轮毂电机机械故障在线诊断方法。首先,以轮毂电机运行安全为目标,着重考虑车速对轮毂电机振动信号的影响程度,在时域和频域中提炼出多个敏感度高的特征参数来表征轮毂电机的运行状态,并将其作为DBNs的观测节点;其次,基于速度片构建轮毂电机机械故障诊断模型,解决其运行状态在相邻时间片之间无法构建转移概率分布的问题,根据不同速度片之间的转移概率分布,建立以二速度片展开的DBNs,实现对轮毂电机机械故障的在线诊断;最后,通过轮毂电机综合台架试验,验证了该方法对轮毂电机机械故障在线诊断的有效性。     

Performance analysis of empirical models for predicting rock mass deformation modulus using regression and Bayesian methods

Deformation modulus of rock mass is one of the input parameters to most rock engineering designs and constructions. The field tests for determination of deformation modulus are cumbersome, expensive and time-consuming. This has prompted the development of various regression equations to estimate deformation modulus from results of rock mass classifications, with rock mass rating (RMR) being one of the frequently used classifications. The regression equations are of different types ranging from linear to nonlinear functions like power and exponential. Bayesian method has recently been developed to incorporate regression equations into a Bayesian framework to provide better estimates of geotechnical properties. The question of whether Bayesian method improves the estimation of geotechnical properties in all circumstances remains open. Therefore, a comparative study was conducted to assess the performances of regression and Bayesian methods when they are used to characterize deformation modulus from the same set of RMR data obtained from two project sites. The study also investigated the performance of different types of regression equations in estimation of the deformation modulus. Statistics, probability distributions and prediction indicators were used to assess the performances of regression and Bayesian methods and different types of regression equations. It was found that power and exponential types of regression equations provide a better estimate than linear regression equations. In addition, it was discovered that the ability of the Bayesian method to provide better estimates of deformation modulus than regression method depends on the quality and quantity of input data as well as the type of the regression equation.     

A deep neural network based classifier for brain tumor diagnosis

Classification process plays a key role in diagnosing brain tumors. Earlier research works are intended for identifying brain tumors using different classification techniques. However, the False Alarm Rates (FARs) of existing classification techniques are high. To improve the early-stage brain tumor diagnosis via classification the Weighted Correlation Feature Selection Based Iterative Bayesian Multivariate Deep Neural Learning (WCFS-IBMDNL) technique is proposed in this work. The WCFS-IBMDNL algorithm considers medical dataset for classifying the brain tumor diagnosis at an early stage. At first, the WCFS-IBMDNL technique performs Weighted Correlation-Based Feature Selection (WC-FS) by selecting subsets of medical features that are relevant for classification of brain tumors. After completing the feature selection process, the WCFS-IBMDNL technique uses Iterative Bayesian Multivariate Deep Neural Network (IBMDNN) classifier for reducing the misclassification error rate of brain tumor identification. The WCFS-IBMDNL technique was evaluated in JAVA language using Disease Diagnosis Rate (DDR), Disease Diagnosis Time (DDT), and FAR parameter through the epileptic seizure recognition dataset.     

Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system

Fault isolation is known to be a challenging problem in machinery troubleshooting. It is not only because the isolation of multiple faults contains considerable number of uncertainties due to the strong correlation and coupling between different faults, but often massive prior knowledge is needed as well. This paper presents a Bayesian network-based approach for fault isolation in the presence of the uncertainties. Various faults and symptoms are parameterized using state variables, or the so-called nodes in Bayesian networks (BNs). Probabilistically causality between a fault and a symptom and its quantization are described respectively by a directed edge and conditional probability. To reduce the qualitative and quantitative knowledge needed, particular considerations are given to the simplification of Bayesian networks structures and conditional probability expressions using rough sets and noisy-OR/MAX model, respectively. By adopting the simplified approach, symptoms under multiple-fault are decoupled into the ones under every single fault, while the quantity of the conditional probabilities is simplified into the linear form of the faults quantity. Prior knowledge needed in Bayesian network-based diagnostic model is reduced significantly, which decreases the complexity in establishing and applying this diagnosis model. The computational efficiency is improved accordingly in the simplified BN model, after eliminating the redundant symptoms. The fault isolation methodology is illustrated through an example of diesel engine fuel injection system to verify the developed model.     

2019年中国硫磺市场数据统计与分析

介绍了2019年中国硫磺产量、进口量及市场数据。2019年中国硫磺产量约7440 kt,同比增长9.4%;进口硫磺量11730 kt,同比增长8.8%。2019年中国进口日本、韩国液体硫磺量为2039.9 kt,与2018年基本持平。2019年中国硫磺市场一路走低,年度降幅为50%,在此过程中相关数据屡次刷出新的记录。2019年全国港口库存硫磺均值数达到1890 kt,数据最高点达到2750 kt。2020年中国硫磺国产数量继续有增长预期,而进口数量或许要看港口库存消耗及下游的整体表现,综合来看,2020年的硫磺市场背负的压力并不比前几年小。     

基于贝叶斯统计的测量系统误差控制方法仿真

针对传统误差控制方法存在耗时较长、误报率较高以及误差控制精度较低等问题,提出了一种基于贝叶斯统计的测量系统误差控制方法。通过含有区间灰数的模糊子集所处的故障状态,得到测量系统产生误差的条件,利用贝叶斯网络推理算法以及相关的区间运算规则,对规划模型求解,获取测量系统产生误差的概率以及相关部件的重要特征信息。根据求解结果以及相关的空间交汇原理构建待测点坐标、待测距离以及有关于经纬仪观测角的数据模型,通过分析测量系统产生误差的原因,选取满足误差存在条件的数据对网络进行训练,令其逼近非线性函数,完成对测量系统误差的有效控制。实验结果表明,所提方法能够快速、准确地对测量系统产生的误差进行控制。     

考虑边信息的多层贝叶斯需求预测模型

随着工业互联网经济的发展,需求的不确定性日益增大,为提高需求预测的准确性,提出一个考虑边信息的多层贝叶斯需求预测模型(DFSI)。DFSI模型通过构造隐层的网络结构以实现对客户需求更加精确的刻画,该隐层结构主要包含两组参数:一组用于描述客户需求在时间上固有的连续性特征,另一组则用于融合相关的边信息特征。进一步,以贝叶斯推断为理论基础,以最大化后验概率为目标,推导出了DFSI的优化目标,并基于梯度下降方法设计了相应的求解算法。使用京东商城及某制造企业的真实销售数据对提出的模型进行了检验。结果显示,与常用的需求预测模型相比,DFSI能获得更好的预测结果。     

基于特征贡献率的机械故障分类方法

为提高往复压缩机、航空发动机等复杂机械故障分类的准确率，依据特征参数对不同故障的敏感度存在差异的特性，提出一种狄利克雷过程混合模型(Dirichlet process mixture model,简称DPMM)与贝叶斯推断贡献(Bayesian inference contribution,简称BIC)相结合的分析方法。采用DPMM方法自学习机械振动信号高维特征的统计分布模型，并依据BIC理论计算得到各特征参数对模型的贡献率，通过对比观测数据与各类故障数据特征贡献率间的差异实现故障分类。试验结果表明，该方法的平均分类准确率比基于高斯混合模型(Gaussian mixture model,简称GMM)的故障诊断方法的平均分类准确率提高19.29%，比基于Relief算法的故障诊断方法的平均分类准确率提高32.71%，且该方法的时效性高，泛化性能强，能够更有效地进行复杂机械故障分类。