Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base.The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model.However,due to the complexity of the milling system structure and the uncertainty of the milling failure index,it is often impossible to construct model expert knowledge effectively.Therefore,a milling system fault detection method based on fault tree analysis and hierarchical BRB(FTBRB)is proposed.Firstly,the proposed method uses a fault tree and hierarchical BRB modeling.Through fault tree analysis(FTA),the logical correspondence between FTA and BRB is sorted out.This can effectively embed the FTA mechanism into the BRB expert knowledge base.The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion.Secondly,evidence reasoning(ER)is used to ensure the transparency of the model reasoning process.Thirdly,the projection covariance matrix adaptation evolutionary strategies(P-CMA-ES)is used to optimize the model.Finally,this paper verifies the validity model and the method’s feasibility techniques for milling data sets.

A Novel Belief Rule-Based Fault Diagnosis Method with Interpretability

Fault diagnosis plays an irreplaceable role in the normal operation of equipment.A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the model.Due to the understandable knowledge expression and transparent reasoning process,the belief rule base(BRB)has extensive applications as an interpretable expert system in fault diagnosis.Optimization is an effective means to weaken the subjectivity of experts in BRB,where the interpretability of BRB may be weakened.Hence,to obtain a credible result,the weakening factors of interpretability in the BRB-based fault diagnosis model are firstly analyzed,which are manifested in deviation from the initial judgement of experts and over-optimization of parameters.For these two factors,three indexes are proposed,namely the consistency index of rules,consistency index of the rule base and over-optimization index,tomeasure the interpretability of the optimizedmodel.Considering both the accuracy and interpretability of amodel,an improved coordinate ascent(I-CA)algorithmis proposed to fine-tune the parameters of the fault diagnosis model based on BRB.In I-CA,the algorithm combined with the advance and retreat method and the golden section method is employed to be one-dimensional search algorithm.Furthermore,the random optimization sequence and adaptive step size are proposed to improve the accuracy of the model.Finally,a case study of fault diagnosis in aerospace relays based on BRB is carried out to verify the effectiveness of the proposed method.

Rule-based Fault Diagnosis of Hall Sensors and Fault-tolerant Control of PMSM

Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

Rough Set Theory Based Approach for Fault Diagnosis Rule Extraction of Distribution System

As the first step of service restoration of distribution system,rapid fault diagnosis is a significant task for reducing power outage time,decreasing outage loss,and subsequently improving service reliability and safety.This paper analyzes a fault diagnosis approach by using rough set theory in which how to reduce decision table of data set is a main calculation intensive task.Aiming at this reduction problem,a heuristic reduction algorithm based on attribution length and frequency is proposed.At the same time,the corresponding value reduction method is proposed in order to fulfill the reduction and diagnosis rules extraction.Meanwhile,a Euclid matching method is introduced to solve confliction problems among the extracted rules when some information is lacking.Principal of the whole algorithm is clear and diagnostic rules distilled from the reduction are concise.Moreover,it needs less calculation towards specific discernibility matrix,and thus avoids the corresponding NP hard problem.The whole process is realized by MATLAB programming.A simulation example shows that the method has a fast calculation speed,and the extracted rules can reflect the characteristic of fault with a concise form.The rule database,formed by different reduction of decision table,can diagnose single fault and multi-faults efficiently,and give satisfied results even when the existed information is incomplete.The proposed method has good error-tolerate capability and the potential for on-line fault diagnosis.

Attribute-driven Fuzzy Fault Tree Model for Adaptive Lubricant Failure Diagnosis

Lubricant diagnosis serves as a crucial accordance for condition-based maintenance(CBM)involving oil changing and wear examination of critical parts in equipment.However,the accuracy of traditional end-to-end diagnosis models is often limited by the inconsistency and random fluctuations in multiple monitoring indicators.To address this,an attribute-driven adaptive diagnosis method is developed,involving three attributes:physicochemical,contamination,and wear.Correspondingly,a fuzzy fault tree(termed FFT)-based model is constructed containing the logic correlations from monitoring indicators to attributes and to lubricant failures.In particular,inference rules are integrated to mitigate conflicts arising from the reverse degradation of multiple indicators.With this model,the lubricant conditions can be accurately assessed through rule-based reasoning.Furthermore,to enhance its intelligence,the model is dynamically optimized with lubricant analysis knowledge and monitoring data.For verification,the developed model is tested with lubricant samples from both the fatigue experiment and actual aero-engines.Fatigue experiments reveal that the proposed model can improve the lubricant diagnosis accuracy from 73.4%to 92.6%compared with the existing methods.While for the engine lubricant test,a high accuracy of 90%was achieved.

REALIZATION OF OBJECT-ORIENTED RULE-TYPE EXPERT SYSTEM TEMPLATE

Expert systems (ESs) are being increasingly applied to the fault diagnosis of engines. Based on the idea of ES template (EST), an object-oriented rule-type EST is emphatically studied on such aspects as the object-oriented knowledge representation, the heuristic inference engine with an improved depth-first search (DFS) and the graphical user interface. A diagnositic ES instance for debris on magnetic chip detectors (MCDs) is then created with the EST. The spot running shows that the rule-type EST enhances the abilities of knowledge representation and heuristic inference, and breaks a new way for the rapid construction and implementation of ES.

基于异质元胞自动机的电力信息物理系统仿真模型

为了探究电力信息物理系统的运行特性以及风险演化特征,考虑现代电网跨区域异质互联、信息化的特征,建立基于异质元胞自动机的电力信息物理系统仿真模型。采用异质元胞自动机分别对电力网和信息网进行建模,基于电网线路、变压器等元件的负载率以及潮流容限,制定电力网内的故障传播规则,以信息网节点之间的最短路径为基础,定义信息网内的故障传播数学模型。在构建随机分布式拒绝服务网络攻击和蓄意分布式拒绝服务网络攻击情景的基础上,以IEEE 39节点和IEEE 118节点系统为例,仿真模拟电力信息物理系统遭受网络攻击时的运行状态以及电力网、信息网在不同耦合程度下遭受网络攻击时的损失负荷和损失节点数。仿真结果表明:攻击者知晓电力信息物理系统的网架结构越清晰,越能实施精准攻击,从而使系统损失最大化;电力网和信息网的耦合程度越紧密,系统遭受网络攻击后的损失越严重,验证了所建模型的合理性和可行性。

Design of a fault diagnosis scheme for a class of singular nonlinear systems

A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.

Expert fault diagnosis system for leaching process in zinc hydrometallurgy

Leaching process is the first step in zinc hydrometallurgy, which involves the complex chemical reactions for dissolving zinc bearing material in dilute sulfuric acid. Ensuring the safe running of the process is a key point in the operation. An expert fault diagnosis system for the leaching process was proposed, which has been implemented in a nonferrous metals smeltery. The system architecture and the diagnosis procedure were presented, and the rule models with the certainty factor were constructed based on the empirical knowledge, empirical data and statistical results on past fault countermeasures, and an expert reasoning strategy was proposed which employs the rule models and Beyes presentation and combines forward chaining and backward chaining. [

Fault Diagnosis of a Rotary Machine Based on Information Entropy and Rough Set

There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.

Fault Diagnosis of Machine Based on Fuzzy Reliability Theory

According to life analysis in reliability theory, certain diagnosis rules can be used to diagnose machines' faults. On this basis, considering the indefiniteness in machine working states, the accurate diagnosis rule was extended to fuzzy diagnosis rule by using basic concepts and methods of fuzzy mathematics. The formulas of fault probability under different conditions were deduced. In the end, an example is given and the results of two methods were compared.

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

基于识别规则的注水站离心泵故障预测方法

针对油田注水站通过传统的人工“望闻问切”等手段,分析判断离心泵机组工况现状的不足,提出了一种基于识别规则的智能故障诊断方法,实现了离心泵实时故障诊断。首先,通过采集注水站离心泵正常状态以及异常状态时的参数,接着分析两种状态下这些参数的变化趋势,将其进行对比分析,研究出离心泵在正常状态和异常状态下参数变化的特征。再次,通过这些离心泵机组参数的变化率及其阈值,计算出离心泵机组故障异常的边界条件。最后,研制出一种离心泵故障识别的规则,实现对离心泵故障的智能预测。通过油田离心泵机组的现场验证,该方法预测故障的准确率达到了99.5%,确保了离心泵机组正常生产运行。

基于置信规则库气象站故障检测专家系统

针对气象站故障检测的需求,研制了基于置信规则库的气象站故障检测专家系统。专家系统利用前期建立起来的气象站的气象数据库,根据当前实时气象数据,通过改进的置信规则库模型结合专家经验知识自动完成数据分析和处理、是否有故障判断并对故障程度进行等级划分。工作人员在进行现场故障检测之前,只需通过气象站传感器故障专家系统对异常传感器进行辅助诊断,确定故障等级后依据故障等级携带相应检修设备或需更换的零部件进入现场实施检查和维修。实验测试表明系统对各个设备的总体平均检测准确率从69.8%提高到85.7%,极大提升了检测的效率和准确性。

污染摄入型兴奋剂违规禁赛期减免因素实证研究--以国际体育仲裁院2014-2024年仲裁裁决为样本

污染摄入型兴奋剂违规是运动员通过一定污染源或中介体被动摄入禁用物质导致阳性检测结果,可分为产品污染型、肉类污染型、密切接触型、其他污染型和来源不明型5种类型。通过对国际体育仲裁院近10年相关裁决的实证分析,归纳出实然层面认定影响禁赛期减免的因素及其逻辑联系,并与《世界反兴奋剂条例》的应然要求相对照,形成包括“禁赛期认定逻辑流程图”“非故意检视对照清单”和“过错程度评估对照清单”在内的禁赛期减免模型。该模型不仅有利于国际体育仲裁院和世界反兴奋剂机构提高工作效率与质量,更有利于运动员事前增强污染摄入防范意识、事后积极争取禁赛期减免,维护自身合法权益。

知识产权侵权平台责任论:从被动免责到主动治理

在电子商务蓬勃发展的数字时代,避风港规则预设的适用场景已不复存在。以先进网络科技为支撑的电商平台远非昔日技术中立的传输通道和存储空间。得益于智能科技,平台过滤和预防知识产权侵权的能力极大提升。平台内经营者均属具备相应资质、资本的注册用户,平台制止侵权的紧迫性远不如侵犯网络著作权的案件。因此,对于平台内经营者知识产权侵权,平台应否承担帮助侵权责任,其裁判理当回归传统的过错责任原则,“红旗标准”和“通知—删除”规则可在过错认定中发挥重要的辅助作用。只要直接侵权者能够被查证并具备赔偿能力,法院就应判令其承担全部赔偿责任。作为敦促平台经营者加强平台治理的连带责任,只能在极其例外的情形下适用。

基于赋权Apriori算法的新型列车运行控制系统故障定位方法

新型列车运行控制系统作为保障我国西部高原铁路运营安全的核心技术装备,具有通信网络复杂、故障模式多、参数输入域广、参数维度高等特点。针对西部高原铁路新型列车运行控制系统多输入参数组合测试问题,选取临时限速场景,提出一种赋权关联规则的故障定位方法。首先,分析新型列车运行控制系统结构及临时限速功能的实现过程,按照层次化理论建立临时限速场景参数输入表,生成测试用例;然后,根据测试用例执行结果,以欧氏距离模型分别计算输入参数组合之间和测试用例之间的相似度,并为各个输入参数组合分配故障致因权重;接着,在输入参数组合与执行结果之间挖掘带权重的定向关联规则,得到不同维度下达到关联规则阈值的故障诱因可疑度,可疑度最高的n-way组合即为故障定位结果;最后,依托京张智能高速铁路列车运行控制系统仿真测试平台,验证所提方法的有效性。实验结果表明:临时限速场景下,故障组合与非故障组合之间的可疑度差值大于0.2,非故障组合之间的可疑度差值小于0.06,故障组合与非故障组合之间的可疑度差异明显;所提方法与FIC-BS、IterAIFL和Delta-debugging方法相比较,查全率、查准率和综合性评价指标的均值分别达到85%、70%和83%,均高于对比的方法,具有更好的故障定位效果。该方法可为新型列车运行控制系统功能测试提供一定的参考。

面向智能辅助决策的飞行器安控系统构建研究

新一代人工智能技术能够提高飞行器试验安控系统的辅助决策能力,满足地面安控目标数量增多、故判规则量增大、故判时效性高等新需求,具有广阔的应用前景。在对飞行器试验地面安控需求分析的基础上,设计面向智能辅助决策的地面安控系统总体框架,增加了数据与模型管理模块;接着构建了辅助决策知识库,主要包括故障弹判据、速度判据、位置判据和预示落点判据等规则库;在此基础上提出数据和规则协同驱动的故障预测方法。该系统既利用历史积累试验数据对深度神经网络模型进行训练,又利用规则库提高专家经验对模型预测精度的贡献,能够提高故障智能预测能力,满足飞行器安控智能辅助决策需求。

基于关联规则的油田大型机泵设备振动故障数据采集方法

为了在不同工况下获取完整的振动故障数据,设计关联规则的油田大型机泵设备振动故障数据采集方法.提取大型机泵设备振动故障数据特征,在数据标准阈值中设定预警值与报警值,获取故障数据的全频域信息;基于关联规则方法优化振动故障数据采集完整性,通过分解、排序、更新三个步骤在处理噪声的同时保证数据完整性;引入连续采集算法判定振动数据故障阈值,优化完成油田大型机泵设备振动故障数据采集.实验表明,该数据采集方法可以在六种工况下,有效获取机泵设备回旋振动与纵向振动的完整故障数据,具备实际可行性.

基于故障树和产生式规则的故障诊断专家系统设计

[目的]为充分运用核动力装置的运行管理经验辅助核动力操纵人员进行故障诊断,设计一种船用核动力装置故障诊断专家系统。[方法]首先,根据故障树与产生式规则之间的逻辑一致性,提出一种将故障树知识转化为产生式规则的方法;然后,对采用正、反向混合推理方法的专家系统知识库和推理机进行优化设计,并依据故障树最小割集和重要度分析结果设计正向推理策略以简化推理流程;最后,根据人工对故障状态判断的思路设计状态监测模块,实时采集关键设备参数以转化为专家系统可识别的设备信息。[结果]结果显示,采用所提方法可解决专家系统知识获取困难的问题,能在保证推理准确度的前提下提升推理效率,实现了专家系统的在线故障诊断功能。[结论]研究表明采用所提方法可提升专家系统获取知识的能力和推理效率,对保障核动力装置的运行管理安全具有重要意义。