Fault detection and diagnostic method of diesel engine by combining rule-based algorithm and BNs/BPNNs

The stable operation of diesel engine is critical to the normal production of the industry, and the prevention, monitoring, and identification of faults are of great significance. At present, the fault research on diesel engines still has some defects, such as only few types of faults diagnosis are identified, the accuracy of fault diagnosis is still low, and fault identification is located at a fixed speed. A novel fault detection and diagnostic method of diesel engine by combining rule-based algorithm and Bayesian networks (BNs) or Back Propagation neural networks (BPNNs) is proposed. The signals are processed by wavelet threshold denoising and ensemble empirical mode decomposition. The signal-sensitive feature values are extracted from the decomposed intrinsic mode function. Seven faults are roughly identified using rule-based algorithm and finely identified using BNs or BPNNs. Results show the proposed fault diagnosis method has a good diagnostic performance for a wide range of rotation speeds when the training data for BNs and BPNNs are from fixed speeds. In addition, the influences of the layers of decomposed signals, sensor noise and external excitation interference on the fault diagnostic performance are also researched.     

[数据驱动的智能服务]面向大规模个性化的交互式云制造模式

结合云制造以及大规模个性化的特点，分析大规模个性化的应用发展以及阐述用户需求的现状，提出了一种面向大规模个性化的交互式云模式。设计了云环境下大规模个性化智能交互过程以及需求交互转换过程模型；建立了大规模个性化的交互式云体系架构以及工作流程；进行交互式云平台搭建，以实现用户定制需求的个性化、大规模、低成本的快速提供与生产。以汽车定制为例，对需求分析及转换过程进行论述，验证了该平台的可行性和有效性。     

CBR和RBR融合的牵引变压器运维策略

牵引变压器在运行中易受到高电压、大电流、机械应力及其他环境因素的影响而产生发热、放电、绝缘不良等故障,为了制定合理的运维检修策略从而提高运行中牵引变压器的故障处理水平,提出一种基于规则推理(RBR)和基于案例推理(CBR)融合的牵引变压器运维决策方法:利用RBR获取能反映牵引变压器状态的关键参数,并根据规则库的知识储备得到初步检修方案;设计案例检索算法在状态检修案例库中匹配相似案例,提取检修策略;根据RBR的初步方案对CBR结果进行修改与复用,综合得到最优运维策略,指导检修工作。收集了60例目标案例验证融合决策模型的准确性,结果表明平均决策准确率可达81.67%,且通过实验可判断源案例数量的增加与决策准确率之间呈正相关关系。     

The nature of choice in mass customized house building

There is a need to explore the actual uptake of specification options by clients in the context of the self-build housing industry. In so doing all the components in a house that can be customized were categorized so as to highlight the features that must be customizable and those that can be standardized. Through a longitudinal study of a German house builder, data from 16 projects over a 35-year time horizon have been collected. The extent of choice made in these projects has been determined by comparing the original standard building specification with the actual finalized fit-out specification of the project. In scrutinizing the collected data it became apparent that the number of changes made by the clients increased considerably over the timespan studied. More importantly, clients appreciated the freedom of choice as they were prepared to spend increasing amounts on customization. In particular specification options were made in the sanitary, internal design and façade categories. Moreover the results revealed that offering a high degree of choice is appropriate for most, but not all, components for the case house builder. Key areas for choice include those relating to sanitary ware and the façade.     

一种基于构件的军用软件故障诊断方法

针对基于构件的军用软件使用过程中外部原因所造成的软件故障,从典型实例中分析软件故障的特征,软件各构件的运行条件、构件间的依赖关系,结合军用软件部署后的跟踪调研工作,总结其故障机理;然后根据专家系统原理研究故障的推理方法,利用成熟的CLIPS专家系统工具构建故障诊断知识库,使用CLIPS嵌入VC＋＋的方法实现军用软件故障诊断工具;通过分析诊断结果,军用软件故障诊断工具可以实现目标问题构件的快速定位,诊断效率高。     

基于本体和规则的扬弹机故障诊断研究

通过对OWL(Web Ontology Language,OWL)本体和SWRL规则语言进行研究,将基于本体和规则的知识表示和智能推理技术应用到舰船扬弹机的故障诊断中。构建了舰船扬弹机的OWL本体及相关的故障诊断SWRL(Semantic Web Rule Language,SWRC)规则,在此基础上开发出一个故障诊断的系统,并实现了诊断推理过程。该系统能帮助设备维护员快速定位故障发生原因,并提供可选用排除故障的基本措施。     

A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for Benign Prostatic Hyperplasia

This paper deals with application of fuzzy intelligent systems in diagnosing severity level and recommending appropriate therapies for patients having Benign Prostatic Hyperplasia. Such an intelligent system can have remarkable impacts on correct diagnosis of the disease and reducing risk of mortality. This system captures various factors from the patients using two modules. The first module determines severity level of the Benign Prostatic Hyperplasia and the second module, which is a decision making unit, obtains output of the first module accompanied by some external knowledge and makes an appropriate treatment decision based on its ontology model and a fuzzy type-1 system. In order to validate efficiency and accuracy of the developed system, a case study is conducted by 44 participants. Then the results are compared with the recommendations of a panel of experts on the experimental data. Then precision and accuracy of the results were investigated based on a statistical analysis.