Fuzzy Fault Diagnosis of a Diesel Engine Non-start

The diesel locomotive plays an important role in the field of transport, and the engine maintenance work is the prerequisite and gnarantee for the locomotive normal working. In this paper, we first establish the fault tree model of locomotive engine 16V240ZJ on the basis of engine non-start as the top event. Then we combines the fitzzy mathematics the- ory and fault tree analysis method for failure diagnosis of 16V240ZJ engine＇s abnormal start-up. We obtained the fuzzy probability curve and top events probability confidence interval by analyzing the fuzzy fault tree qualitatively and quantitatively. It provides a fuzzy analysis basis for solving the problem of 16V240ZJ engine＇s abnormal start-up.

Fault Diagnosis for Manifold Absolute Pressure Sensor(MAP) of Diesel Engine Based on Elman Neural Network Observer

Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can＇t be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015？0.017 5 and sample error is controlled within 0？0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed： complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show： intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis;the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.

Establishment and Optimization of State Feature System of Diesel Engine Fault Diagnosis

For too many state features are used in the diesel engine state evaluation and fault diagnosis, it is not easy to obtain the rational eigenvalues. In the paper, the cylinder subassembly of diesel engine is used to search for the method of establishing state feature system and optimal approach. The signal of diesel engine has been collected when the piston ring and airtight ring are working at different states, then with the Bootstrap method and Genetic Algorithm (GA), an optimum parameter combination is received. Example shows this method is simple and efficient for establishing diesel engine state feature system, Thus, this method is valuable for the virtual state evaluation of similar complex system.

Fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval SVM

The fault detection and diagnosis of diesel engine valve clearance can effectively improve the availability and safety of diesel engine and have extremely important value and significance.Diesel engines generally operate in various stable operating conditions,which have important influence on the fault diagnosis.However,many fault diagnosis methods have been put forward under specific stable operating condition based on vibration signal.As the result of great impact caused by operating conditions,corresponding diagnosis models cannot deal with the fault diagnosis under different operating conditions with required accuracy.In this paper,a fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval support vector machine(SVM)is proposed.Firstly,the fault features with weak condition sensitivity have been extracted according to the influence analysis of fault on vibration signal.Moreover,soft interval constraint has been applied to SVM algorithm to reduce the random influence of vibration signal on fault features.In addition,different machine learning algorithms based on different feature sets are adopted to conduct the fault diagnosis under different operating conditions for comparison.Experimental results show that the proposed method is applicable for fault diagnosis under variable operating condition with good accuracy.

Fault diagnosis of a marine power-generation diesel engine based on the Gramian angular field and a convolutional neural network

Marine power-generation diesel engines operate in harsh environments.Their vibration signals are highly complex and the feature information exhibits a non-linear distribution.It is difficult to extract effective feature information from the network model,resulting in low fault-diagnosis accuracy.To address this problem,we propose a fault-diagnosis method that combines the Gramian angular field(GAF)with a convolutional neural network(CNN).Firstly,the vibration signals are transformed into 2D images by taking advantage of the GAF,which preserves the temporal correlation.The raw signals can be mapped to 2D image features such as texture and color.To integrate the feature information,the images of the Gramian angular summation field(GASF)and Gramian angular difference field(GADF)are fused by the weighted average fusion method.Secondly,the channel attention mechanism and temporal attention mechanism are introduced in the CNN model to optimize the CNN learning mechanism.Introducing the concept of residuals in the attention mechanism improves the feasibility of optimization.Finally,the weighted average fused images are fed into the CNN for feature extraction and fault diagnosis.The validity of the proposed method is verified by experiments with abnormal valve clearance.The average diagnostic accuracy is 98.40%.When−20 dB≤signal-to-noise ratio(SNR)≤20 dB,the diagnostic accuracy of the proposed method is higher than 94.00%.The proposed method has superior diagnostic performance.Moreover,it has a certain anti-noise capability and variable-load adaptive capability.

Determination of Technological Condition of Diesel Engine PZ12V190B Using Fuzzy Method

This article has selected useful diagnostic parameters according to the running condition of PZ12V190B diesel, and built fuzzy vectors of this parameter by using the subjection function to establish a technological condition. By using a statistical method the standard vector has been obtained, so the diesel technological level can be determined by calculating the closing degree between the fuzzy vector and the standard fuzzy vector.

Fault Diagnosis for a Diesel Valve Train Based on Time-Freq uency Analysis and Probabilistic Neural Networks

The cone-shaped kernel distributions of vibration acceleration signals, whichwere acquired from the cylinder head in eight different states of a valve train, were calculatedand displayed in grey images. Probabilistic Neural Networks ( PAW) was used to classify the imagesdirectly after the images were normalized. By this way, the problem of fault diagnosis for a valvetrain was transferred to the classification of time-frequency images. As there is no need to extractfeatures from time-frequency images before classification, the fault diagnosis process is highlysimplified. The experimental results show that the vibration signals can be classified accurately bythe proposed methods.

FUZZY PETRI NET FOR FAULT DIAGNOSIS

Because of the stochastic property of fault occurrence and fuzziness offault phenomenon, machine fault diagnosis technique in use, such as fault tree analysis,cause consequence tree method, etc., cannot exactly describe the properties of fault phe-nomenon and coherence of fault space. In this paper, based on the theory of generalPetri net, fault tree technique and theory of fuzzy set, a theory system of fuzzy Petri net(FPN) suitable for fault diagnosis is established, which is applied to an example of faultdiagnosis for FMS. This method has the properties of of rbjectivity, strong expressionability, easy inference, etc., which can solve the problems of stochastic property andfuzziness of fault.

Research on the applications of infrared technique in the diagnosis and prediction of diesel engine exhaust fault

This paper mainly introduces the basic principles,the methods and the applications of infrared technique in the diagnosis and prediction of diesel engine exhaust faults. The test-bed for monitoring diesel engine exhaust faults by thermal infrared imager has been designed. In different running conditions, the exterior surface radiation temperatures of the exhaust pipe of the 6135G-1 diesel engine have been measured by infrared imaging system. According to the principle of infrared temperature measurement, the real temperatures of the exterior surface of the exhaust pipe have been calculated. Based on the principle of heat transfer, the method of calculating the exhaust temperatures according to the exterior surface radiation temperatures of exhaust pipe measured by thermal infrared imager is built. The relationship between diesel engine exhaust temperatures and faults has been analyzed. It is shown that the application of infrared inspection and diagnosis to the identifying of diesel engine exhaust faults is feasible and effective.

Optimization of a dynamic uncertain causality graph for fault diagnosis in nuclear power plant

Fault diagnostics is important for safe operation of nuclear power plants(NPPs). In recent years, data-driven approaches have been proposed and implemented to tackle the problem, e.g., neural networks, fuzzy and neurofuzzy approaches, support vector machine, K-nearest neighbor classifiers and inference methodologies. Among these methods, dynamic uncertain causality graph(DUCG)has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicate and, in many cases, results redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a fuzzy decision tree(FDT) by extracting from the DUCG a fuzzy rule base that resumes the used symptoms at the basis of the FDT. Genetic algorithm(GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation. The results show that the FDT, with GA-optimized symptoms and diagnosis strategy, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.

Complete Modeling for Systems of a Marine Diesel Engine

这份报纸基于物理、半物理、数学、热力学的方程论述一台海洋的柴油机引擎的一个模拟器模型，它允许快预兆的模拟。整个引擎系统被划分成几功能的块：冷却，润滑油，空气，注射，燃烧和排出物。亚模型和单个块的动态特征根据在参考书 6M26SRP1 下面为 SIMB 同伴从一张海洋的柴油机引擎测试凳子收集的引擎工作原则方程和试验性的数据被建立。全面引擎系统动力学用 Matlab/Simulink 的亚块和 S 功能被表示为一套同时的代数学、微分的方程。这个模型的模拟，在 Matlab/Simulink 上实现了被验证了并且能被用来获得引擎性能，压力，温度，效率，热版本，曲柄角度，燃料率，在不同亚块的排出物。模拟器将被使用，在未来工作，到学习处于有缺点的条件的引擎性能，和罐头被用来象设计者一样在差错诊断和评价( FDI )帮助海洋的工程师预言冷却系统的行为，润滑油系统，注射系统，燃烧，排出物，以便优化不同部件的尺寸。这个程序是为差错模拟器的一个平台，到在为差错参数改变值例如的亚块引擎输出上调查影响：有缺点的燃料注射者，漏的柱体，穿的燃料泵，破活塞戒指，脏 turbocharger，脏空气过滤器，脏空气冷却器，空气漏，水漏，油漏和污染，热 exchanger 犯规，泵穿，注射者的失败(并且许多其它) 。

Data fusion for fault diagnosis using multi-class Support Vector Machines

Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.

故障树和模糊贝叶斯网络在管廊运维风险评估中的应用研究

地下综合管廊是城市的生命线,一旦出现问题就会对人们生命财产安全造成巨大损害。为了系统地分析管廊运维风险,建立了基于模糊贝叶斯网络的风险评估框架。首先,通过分析管廊运维风险源与风险形成的原因以确定风险事件和风险类别;其次,建立管廊运维风险故障树来梳理风险因素之间的逻辑关系,将故障树映射为贝叶斯网络;最后,结合专家模糊评价,构建地下综合管廊运维风险评估模型。案例分析结果显示:中间事件“火灾、爆炸”与“危害气体浓度过高”的发生概率较高。敏感性分析结果显示:“运维人员操作和维护不当”是导致管廊运维风险发生的根本事件,因此需要制定严格管理措施及规范,加强对运维人员的素质培训,以降低管廊运维过程中各种风险的发生概率。

基于改进EEMD-MB1DCNN的船用柴油机缸套-活塞环故障诊断

针对船用中高速柴油机缸套-活塞环振动信号非线性非平稳性以及同类型不同损伤程度故障发生时振动信号时频域特征相似、故障难以识别等问题,利用振动信号辨识故障,提出一种基于改进集成经验模态分解方法和多模块一维卷积神经网络端到端缸套-活塞环故障诊断方法,通过设计固有模态分量IMF信息质量筛选准则对EEMD分解出的IMFs进行重新排序,获得包含更多凸显故障特征成分的重构信号,输入到上述神经网络模型,通过振动信号分析并与现有方法比较,评估所设计IMF信息质量筛选准则与所搭建模型的性能,试验结果显示该方法能准确、有效地识别缸套-活塞环故障类型。在判断该易损件同类型不同磨损程度故障诊断中有较高的准确率,能对故障状况进行有效的特征提取与故障分类。

基于知识图谱与模糊贝叶斯推理的航空发动机故障诊断

针对航空发动机结构功能复杂,存在贝叶斯网络构建难、节点条件概率难以获得精确值的问题,提出基于知识图谱与模糊贝叶斯网络的故障推理诊断方法。首先,以历史故障数据为依据,构建航空发动机故障知识图谱;其次,提出“知识图谱-贝叶斯网络”的映射方法,用于快速构建贝叶斯网络;然后,引入模糊集合论,解决工程实际中概率参数的不确定性问题;最后,以航空发动机滑油系统故障进行实例验证,结果表明所提方法既能提高贝叶斯网络的构建效率,又能实现故障诊断的不确定性推理,可用于诊断策略优化和设备可靠性提升,具有较强的工程应用价值。

基于t-SNE-VNWOA的船舶柴油机故障诊断

文中提出一种基于t-SNE-VNWOA-LSSVM故障诊断模型,并进行了台架试验.试验设置了正常工况、供气不足、燃烧提前和单缸断油四种工况,将各种工况采集的缸盖振动信号进行快速傅里叶变换(FFT),提取了13个时域和频域特征,利用t分布邻域嵌入算法(t-SNE)对数据降维、可视化故障特征.结合鲸鱼优化算法(VNWOA)对分类器(LSSVM)初始参数δ2和γ寻优,搭建其故障识别模型,将遗传算法(GA)和粒子群算法(PSO)的寻优诊断结果与之对比.结果表明:基于t-SNE-VNWOA-LSSVM故障诊断模型精度高达96.57%,且具有良好的稳定性及诊断速度.

基于振动信号分析的船用柴油机故障诊断系统开发

船用柴油机在线诊断和监测对于保障船舶安全航行具有非常重要的作用。经验法和建立数学模型的方法在实际应用中受到非常大的限制,本文提出一种基于振动信号分析的船用柴油机故障诊断系统,分析了柴油机的基本结构和工作流程,对柴油机的不同故障振动信号特征进行分析,在此基础上设计了故障诊断系统的结构,包括振动信号采集、特征提取以及故障诊断模块,通过将柴油机历史振动数据和故障类型建立映射,并基于柴油机振动信号特征使用故障诊断模型输出诊断结果。

柴油机燃烧室部件声发射诊断方法研究

本文基于声发射信号和迁移学习,提出一种新的柴油机燃烧室故障诊断方法。研究在TBD234V6型柴油机上模拟了喷油器堵塞、启阀压力减小和排气阀漏气故障等,用CompactRIO硬件进行信号采集,并针对燃烧室部件故障后声发射信号的特征进行分析。研究表明,以特征参数提取和迁移学习为基础的故障诊断方法能更准确地识别不同故障类型,相对于传统机器学习算法,其准确度更高,泛化能力也更强,对于数据样本较少和不同数据分布的情况下也有较好适应性。此研究对于保证柴油机燃烧室部件的健康状况、确保船舶安全航行具有重要意义。

应用优化DHKELM的柴油机故障诊断方法

为准确、高效地对柴油机故障进行诊断,提出应用优化深度混合核极限学习机(deep hybrid kernel extreme learning machine, DHKELM)的柴油机故障诊断方法。该方法以各样本的频谱幅值作为故障特征,归一化处理后作为DHKELM模型的输入,从而实现对柴油机各故障状态的识别。相较极限学习机,该模型具有更深层次的结构,引入了混合核函数以及自动编码器,可以准确区分易混淆的故障类型,提高诊断准确率。针对DHKELM模型中各个超参数难以确定的问题,提出利用改进麻雀搜索算法(improved sparrow search algorithm, ISSA)对模型中各超参数进行寻优,充分发挥模型的故障诊断性能。实验结果表明,在实验室实测数据中,所提方法较传统方法具有较好的故障诊断精度,为柴油机故障诊断提供了一种新的思路。

矿用防爆柴油机智能故障诊断技术研究

针对矿用防爆柴油机故障排除耗时、依赖经验和检修工具落后等问题,设计了一种集防抖、故障处理、主从识别和信息存储于一体的故障诊断管理系统,显著提高了维护效率与可靠性。同时,结合售后维修需求,提出了一种搭载融合多种无线通信技术的智能诊断仪的远程故障诊断方案,实现了对防爆柴油机的远程故障诊断、实时监测和远程升级等功能,为煤矿行业的高效运营提供了有力支持。