Application of input estimation techniques to charge estimation and control in automotive engines

The performance of control, estimation and diagnostics algorithms of modern automotive engine management systems can be significantly enhanced using input observers. This paper describes several case studies along these lines in the area of charge estimation and control for gasoline and diesel engines, and illustrates the conclusions with experimental results obtained from a vehicle and from an engine dynamometer. A self-contained overview of some of the popular input estimation techniques and their properties is provided. Pointers are given that show how input estimators can be integrated within more complex adaptation and control algorithms using specific automotive applications as examples.     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

具有工作状态转换的EIIKF船舶柴油机故障预测

船舶柴油机作为大多数船舶的动力源泉，具有十分重要的地位，其健康状态直接影响了船舶的稳定运行.由于船舶柴油机具有工作环境复杂且工况多变的特点，不利于传统故障预测方法的应用.本文提出了一种增强型间歇性未知输入卡尔曼滤波器，可以有效降低建模的复杂度，应对具有不同的工作状态的参数预测.最后本文提出并使用改进的序贯概率比检验进行残差处理，减小故障误报.仿真结果表明，该方法可以较好地对船舶柴油机系统故障进行预测.     

Data-driven monitoring and validation of experiments on automotive engine test beds

Engine test beds are widely used to estimate automotive engine parameters and design controllers in the preliminary development phase. The controller parameters are optimized to fulfill emission, fuel consumption and driving comfort requirements and they will be further validated on chassis dynamometer and road driving experiments. It is common that the results of two experiments deviate, due to some external disturbances or faults. The main purpose of this paper is to demonstrate the application of data-driven fault diagnosis techniques to detect the deviations in the experiments and analyze their root-causes to reduce the costs and time of the engine design and its control concept. To this end, two different methods are introduced for detection of the problems in the experiment. Based on the results of the detection step, a fault isolation technique has been proposed to support test engineers in finding the cause of the deviations. The results have been demonstrated on an industrial engine test bed and the effectiveness of the methods is discussed.     

基于LabVIEW的柴油机动力性能检测系统

利用了柴油机功率和起动齿圈转速的关系进行柴油机动力性能状态检测，并构建了以虚拟仪器为核心的柴油机动力性能状态采集系统，建立了表征动力性能状态的特征参量和正常故障判定准则，采用LabVIEW开发了检测与故障判定软件系统。     

Model-based fault detection and isolation for a gas–liquid separation unit

A complete fault detection and isolation system is designed for a gas–liquid separation unit. It involves the determination and identification of grey box models, the design of a model-based residual generator, and finally the evaluation of the residuals via a set of statistical tests. The latter are cumulative sum (CUSUM) tests which are combined in such a way that both fault detection and fault isolation can be achieved. The performance of the resulting diagnosis system, such as missed alarm rate, wrong isolation rate and mean detection delay, are studied via simulations.     

An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network

This paper describes a fault diagnosis system for automotive generators using discrete wavelet transform (DWT) and an artificial neural network. Conventional fault indications of automotive generators generally use an indicator to inform the driver when the charging system is malfunction. But this charge indicator tells only if the generator is normal or in a fault condition. In the present study, an automotive generator fault diagnosis system is developed and proposed for fault classification of different fault conditions. The proposed system consists of feature extraction using discrete wavelet analysis to reduce complexity of the feature vectors together with classification using the artificial neural network technique. In the output signal classification, both the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) are used to classify and compare the synthetic fault types in an experimental engine platform. The experimental results indicate that the proposed fault diagnosis is effective and can be used for automotive generators of various engine operating conditions.     

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.     

High performance automotive chassis design: a topology optimization based approach

Automotive chassis design in view of car weight reduction is a challenging task due to the many performance targets that must be satisfied, in particular in terms of vehicle safety. In this paper a methodology for automotive chassis design in involving optimization techniques is presented. In particular, topology, topometry and size optimizations are coupled with fem analyses and adopted in cascade for reaching an optimum chassis configuration. The methodology is applied to the design process of a rear-central engine high performance vehicle chassis. The objective of the optimization process is the chassis weight reduction, yet in fulfilment of structural performance constraints as required by Ferrari standards. The results demonstrate the general applicability of the methodology presented for obtaining the general trusses layout and thicknesses distribution of the structure. The numerical model at this stage shows a significant weight reduction when compared to the chassis of the Ferrari F458 Italia.     

Study of factors impacting remote diagnosis performance on a PLC based automated system

In this paper, we present systematically experimental and analytical evaluations on design of remote fault diagnosis systems for a programmable logic controller (PLC) based automated system. In order to investigate the factors of remote architecture, operator's skill level, and fault's effect on diagnosis performance, comprehensive experimental evaluations, statistical analysis and survey were conducted. The experiment compared three levels of remote architectures, two levels of operators’ skill levels on four typical faults in an automated system. Performance evaluation including detection time, amount of information search, number of diagnostic tests, number of asked questions, number of system runs, and performance score, were extracted from the experiment record. Two-stage statistical analysis including (1) analysis of variance (ANOVA) and (2) least significant difference (LSD) paired comparison was conducted on the performance evaluation data. From the statistical analysis results and expert survey, we concluded that: (1) the architecture sophistication eased the diagnosis on the faults that are related to the measurement signals, and (2) the diagnosis performance also increased with the sophistication of the architecture, but (3) operator's skill level did not significantly affect the diagnosis performance. The survey results on troubleshooters’ opinions and preferences about the diagnosis were also summarized, which can be applied for improvement on design of remote diagnosis system. The proposed evaluation approach is systematic; it can be applied on design and evaluation of diagnostics systems on other PLC based automated systems such as heating, ventilation, and air conditioning (HVAC), robotics assembly.     

应用粗糙集提取柴油机故障数据特征

根据柴油机故障数据的特点,采用粗糙集理论对其进行特征提取研究。由于实际测量的参数大多为连续数据,而粗糙集只能处理离散数据,提出了一种适用于粗糙集的SOM网络离散化方法;给出一种基于简化差别矩阵的快速属性约简算法;以6135D型柴油机故障诊断数据为例进行特征提取,成功地将原始8个属性约简为3个,为后续研究工作打下了基础。     

一类不确定线性系统的鲁棒故障观测器设计

研究线性时不变系统的鲁棒故障观测器设计问题,给定故障检测率,降低误报率作为优化指标,对故障和未知扰动在残差信号中的输出进行滤波。再考虑不确定性影响,运用模型匹配方法,提出了具有不确定性线性系统的鲁棒故障观测器设计方法,并以线性矩阵不等式形式给出问题求解算法。通过求解具有线性矩阵不等式约束的凸优化问题获得观测器的全局最优解。通过仿真表明,改进的设计方法可有效保障残差信号对故障信号的灵敏度,并改善了诊断性能。     

现代船用柴油机智能故障诊断系统的研究

现代船用柴油机是机一电．液等各种子系统组成的大型机电设备,结构复杂决定了对其故障进行诊断的困难性。基于目前发展较快的船用柴油机故障诊断技术及较成熟的专家系统与人工神经网络理论,对专家系统和BP神经网络的融合方法在船用柴油机故障诊断方面的应用进行了有益的探讨,并提出了利用专家系统和BP神经网络的融合方法建立的船用柴油机神经网络故障诊断专家系统基本结构。     

A gain-varying UIO approach with adaptive threshold for FDI of nonlinear F16 systems

A discrete gain-varying unknown input observer (UIO) method is presented for actuator fault detection and isolation (FDI) problems in this paper. A novel residual scheme together with a moving horizon threshold is proposed. This design methodology is applied to a nonlinear F16 system with polynomial aerodynamics coefficient expressions, where the coefficient expressions for the F16 system and UIOs may be slightly different. The simulation results illustrate that a satisfactory FDI performance can be achieved even when the F16 system is under the environment of model uncertainties, exogenous noise and measurement errors.     

故障诊断系统的H_2优化设计

研究了一类受随机扰动影响的线性时不变系统的故障诊断滤波器(Faultdiagnosisfilter,简称FDF)设计问题。分别应用从扰动、故障到残差的传递函数的H2范数作为残差系统对扰动的鲁棒性和对故障信号的灵敏度度量指标,以最小化基于鲁棒性/灵敏度的性能指标函数为目标,将FDF设计问题归结为H2优化问题。推导并证明了问题可解的充分条件,给出了一种FDF设计的迭代线性矩阵不等式(IterativeLinearMatrixInequality,ILMI)算法,并进一步讨论了残差的评价问题。算例验证了该算法的有放性。     

Detecting sensor fault for nonlinear systems in T–S form under sampled-data measurement: Exact direct discrete-time design approach

A direct discrete-time design methodology for sampled-data sensor fault detection for nonlinear systems in Takagi–Sugenos form is proposed. Contrary to the conventional schemes in this way that rely on an approximate discrete-time model of the nonlinear system, our result is established based on an exact one. Condition to design the observer and the residual gain under an H-/H _∞ performance criterion is presented in matrix inequality format. An example is given to illustrate the effectiveness of the proposed methodology.     

A fault detection scheme for linear discrete-time systems with an integrated online performance evaluation

This paper is concerned with the design of the fault detection systems, into which a residual generation, evaluation and threshold are integrated, for linear discrete time-varying processes over a finite horizon. In the proposed design scheme, the residual generation is realised in the context of H_∞ fault estimation with a prescribed attenuation level. This attenuation level is minimised by using the Krein-space linear estimation theory and, subsequently, an H_∞ fault estimator with the minimum attenuation level is designed in terms of the solution to a set of Riccati-like recursions. For the residual evaluation and decision making purpose, the false alarm rate and fault detection rate indicators are introduced in the norm-based framework, which is integrated into the decision making procedure. For the online computations of the false alarm rate and fault detection rate indicators, further estimates delivered by the H_∞ fault estimator are applied without additional (online) computations. By means of checking the change in the false alarm rate and fault detection rate indicators, a decision is then made. In this way, the fault detection performance can be significantly improved. Finally, one application example is exploited to demonstrate the application of the proposed integrated fault detection and performance evaluation schemes.     

船舶柴油机故障诊断仿真系统研究

该文阐述了船舶柴油机故障诊断仿真系统的模型、结构与功能,着重介绍了基于RBF神经网络故障诊断方法。本系统具有结构简单、功能齐全等优点,对开发实船柴油机故障诊断的辅助分析系统和培训轮机员熟悉处理故障有实际意义。