A flywheel error compensation algorithm for engine misfire detection

The crankshaft angular velocity measured at the flywheel is a commonly used signal for engine misfire detection. However, flywheel manufacturing errors result in vehicle-to-vehicle variations in the measurements and have a negative impact on the misfire detection performance. A misfire detection algorithm must be able to compensate for this type of vehicle-to-vehicle variations if it is to be used in production cars to assure that legislations are fulfilled. It is shown that flywheel angular variations between vehicles in the magnitude of 0.05° have a significant impact on the measured angular velocity and must be compensated for to make the misfire detection algorithm robust. A misfire detection algorithm is proposed with flywheel error adaptation in order to increase robustness and reduce the number of mis-classifications. Evaluations using measurements from a number of vehicles on the road are used to quantify the negative impact of the flywheel errors and show that the number of mis-classifications is significantly reduced when performing on-line flywheel error adaptation.     

A methodology for increasing the signal to noise ratio for the misfire detection at high speed in a high performance engine

The paper presents a methodology for pre-processing the combustion time intervals, that is the basic signal used in misfire detection strategies, with the aim of increasing the signal-to-noise ratio to enable a more efficient misfire diagnosis, especially when the engine is running at high speeds and low loads. The performance of the basic misfire detection algorithm shows that in those engine operating conditions the background noise amplitude has approximately the same value of the information related to the misfire presence, thus hiding the misfire event that may not be detected. The proposed methodology is based on the correction of the combustion time signal cycle-by-cycle, using a vector of data that take into account the specific behavior of every cylinder. The vector of data for the combustion time correction is stored in a map inside the control unit and could be continuously updated with an auto-adaptive learning technique.     

Distributed data-driven optimal fault detection for large-scale systems

This paper is concentrated on two new distributed data-driven optimal fault detection approaches in large-scale systems using a group of sensor blocks, each of which accesses part of the process variables. Towards this end, an optimal fault detection problem is first formulated and solved, which lays a foundation for further distributed studies. Based on it, the first distributed data-driven optimal fault detection scheme, consisting of offline distributed learning and online distributed detection, is developed using the average consensus algorithm. To further reduce communication and computation efforts, the second average consensus based fault detection is investigated. Considering that the iteration computations for average consensus algorithm can lead to fault detection delay, a variation of the average consensus based fault detection scheme is proposed with iterative estimation of the covariance matrices of random variables and implementation of the distributed test statistic during the consensus iteration. A numerical example and a case study on the PRONTO heterogeneous benchmark dataset are used to demonstrate the proposed approaches.     

Finite-frequency fault detection for two-dimensional Fornasini–Marchesini dynamical systems

This paper is concerned with the fault detection problem for two-dimensional (2-D) discrete-time systems described by the Fornasini–Marchesini local state-space model. The goal of the paper is to design a fault detection filter to detect the occurrence of faults in finite-frequency domain. To this end, a finite-frequency H_? index is used to describe fault sensitivity performance, and a finite-frequency H_∞ index is used to describe disturbance attenuation performance. In light of the generalised Kalman–Yakubovich–Popov lemma for 2-D systems and matrix inequality techniques, convex conditions are derived for this fault detection problem. Based on these conditions, a numerical algorithm is put forward to construct a desired fault detection filter. Finally, a numerical example and an industrial example are given to illustrate the effectiveness of the proposed algorithm.     

Fault detection and accommodation via neural network and variable structure control

This paper proposes a novel idea that classifies faults into two different kinds: serious faults and small faults, and treats them with different strategies respectively. A kind of artificial neural network (ANN) is proposed for detecting serious faults, and variable structure (VS) model-following control is constructed for accommodating small faults. The proposed framework takes both advantages of qualitative way and quantitative way of fault detection and accommodation. Moreover, the uncertainty case is investigated and the VS controller is modified. Simulation results of a remotely piloted aircraft with control actuator failures illustrate the performance of the developed algorithm.     

基于深度学习的复杂气象条件下海上船只检测

为了解决复杂海情环境下的不同种类和大小的舰船检测问题,提出一种实时的深度学习的目标检测算法。首先,提出了一种清晰图片和模糊图片（雨、雾等图片）判别的方法;然后,在YOLO v2的深度学习框架的基础上提出一种多尺度目标检测算法;最后,针对遥感图像舰船目标的特点,提出了一种改进的非极大值抑制和显著性分割算法,对最终的检测结果进一步优化。在复杂海情和气象条件下的舰船目标公开比赛的数据集上,实验结果表明,相比原始的YOLO v2,该方法的准确率提升了16%。     

Fault detection and accommodation via. neural network and variable structure control

This paper proposes a novel idea that classifies faults into two different kinds： serious faults and small faults, and treats them with different strategies respectively. A kind of artificial neural network （ANN） is proposed for detecting serious faults, and variable structure （VS） model-following control is constructed for accommodating small faults. The proposed framework takes both advantages of qualitative way and quantitative way of fault detection and accommodation. Moreover, the uncertainty case is investigated and the VS controller is modified. Simulation results of a remotely piloted aircraft with control actuator failures illustrate the performance of the developed algorithm.     

Parity-relation-based state-of-health monitoring of lead acid batteries for automotive applications

Effective vehicular power management requires accurate knowledge of battery state, including state-of-charge (SOC) and state-of-health (SOH). This paper presents an integrated algorithm for reliable battery SOH monitoring. The dynamics of lead acid batteries during engine cranking is investigated, and a new battery model is presented. Moreover, a parity-relation-based integrated method for battery SOH monitoring is proposed. It is shown that the diagnostic residual combines the SOH information provided by both battery resistance and voltage loss during engine cranking, hence enhancing diagnostic performance. Extensive evaluation results using real vehicle cranking data have verified the effectiveness of the proposed method.     

基于自适应锚框的裂缝目标检测算法研究

随着交通的发展，桥梁在运输过程中扮演着越来越重要的角色,桥梁也更加多样化。因此面对大量工况不同的桥梁，发展一种能便捷学习新工况的智能化裂缝检测技术显得尤为重要。为提高目标检测算法的准确率和效率，本文将裂缝原始图像切分成3种不同分辨率和尺寸的切片，训练网络识别不同尺寸的裂缝。同时为了增加算法的后续拓展性，设计一种根据训练集标注尺寸自适应调整锚框的手段，让算法在后续使用过程中针对不同工程情况需要增加训练数据时，能直接添加数据进行训练，自动调整最佳锚框尺寸，使该算法在实际使用中具有学习改进的空间。与原始YOLOv3网络和文献中的算法对比，本文算法的精确度平均达到91%以上且扩展性更好。     

A possibilistic clustering approach to novel fault detection and isolation

In this paper, a new approach for fault detection and isolation that is based on the possibilistic clustering algorithm is proposed. Fault detection and isolation (FDI) is shown here to be a pattern classification problem, which can be solved using clustering and classification techniques. A possibilistic clustering based approach is proposed here to address some of the shortcomings of the fuzzy c-means (FCM) algorithm. The probabilistic constraint imposed on the membership value in the FCM algorithm is relaxed in the possibilistic clustering algorithm. Because of this relaxation, the possibilistic approach is shown in this paper to give more consistent results in the context of the FDI tasks. The possibilistic clustering approach has also been used to detect novel fault scenarios, for which the data was not available while training. Fault signatures that change as a function of the fault intensities are represented as fault lines, which have been shown to be useful to classify faults that can manifest with different intensities. The proposed approach has been validated here through simulations involving a benchmark quadruple tank process and also through experimental case studies on the same setup. For large scale systems, it is proposed to use the possibilistic clustering based approach in the lower dimensional approximations generated by algorithms such as PCA. Towards this end, finally, we also demonstrate the key merits of the algorithm for plant wide monitoring study using a simulation of the benchmark Tennessee Eastman problem.     

An autonomous valve stiction detection system based on data characterization

This paper proposes a valve stiction detection system which selects valve stiction detection algorithms based on characterizations of the data. For this purpose, novel data feature indexes are proposed, which quantify the presence of oscillations, mean-nonstationarity, noise and nonlinearities in a given data sequence. The selection is then performed according to the conditions on the index values in which each method can be applied successfully. Finally, the stiction detection decision is given by combining the detection decisions made by the selected methods. The paper ends demonstrating the effectiveness of the proposed valve stiction detection system with benchmark industrial data.     

非线性摄动系统的鲁棒故障诊断滤波器设计ILMI算法

The robust fault detection filter design for uncertain linear systems with nonlinear perturbations is formulated as a two-objective optimization problem. Solvable conditions for the exis- tence of such a robust fault detection filter are given in terms of matrix inequalities (MIs), which can be solved by applying iterative linear matrix inequality (ILMI) techniques. Particularly, compared with two existing LMI methods, the developed algorithm is more generalized and less conservative. An illustrative example is given to show the effectiveness of the proposed method.     

Induction motors bearing fault detection using pattern recognition techniques

This paper proposes a systematic procedure based on a pattern recognition technique for fault diagnosis of induction motors bearings through the artificial neural networks (ANNs). In this method, the use of time domain features as a proper alternative to frequency features is proposed to improve diagnosis ability. The features are obtained from direct processing of the signal segments using very simple calculation. Three different cases including, healthy, inner race defect and outer race defect are investigated using the proposed algorithm. The ANNs are trained with a subset of the experimental data for known machine conditions. Once the network is trained, efficiency of the proposed method is evaluated using the remaining set of data. The obtained results indicate that using time domain features can be effective in accurate diagnosis of various motor bearing faults with high precision and low computational burden.     

大型雷电定位系统中实时自适应联合检测算法

雷电电磁辐射的持续时间具有随机性。采用能量法检测雷电数据块,当信号长度远短于数据块长度时,将会产生噪声淹没信号现象而引起检测概率降低的问题。发现可利用峰度来描述含有短雷电信号的数据块的波形特征,而且能量块检测与特征检测具有互补特性。为了提高检测概率,将能量块检测和特征检测相结合,利用自动筛选思想和删余检测技术实时估计背景噪声,提出了实时自适应联合雷电检测算法。通过对实采的雷电数据进行实验,结果表明,所提出的检测算法能够明显提高检测概率,表明了其有效性和实用性。     

Dynamic threshold generators for robust fault detection in linear systems with parameter uncertainty

The problem of developing robust thresholds for fault detection is addressed. An inequality for the solution of a linear system with uncertain parameters is provided and is shown to be a valuable tool for developing dynamic threshold generators for fault detection. Such threshold generators are desirable for achieving robustness against model uncertainty in combination with sensitivity to small faults.The usefulness of the inequality is illustrated by developing an algorithm for detection of sensor faults in a turbofan engine. The proposed algorithm consists of a state observer with integral action. A dynamic threshold generator is derived under the assumption of parametric uncertainty in the process model. Successful simulations with measurement data show that the algorithm is capable of detecting faults without generating false alarms.     

An ILMI Approach to RFDF for Uncertain Linear Systems with Nonlinear Perturbations

The robust fault detection filter design for uncertain linear systems with nonlinear perturbations is formulated as a two-objective optimization problem. Solvable conditions for the existence of such a robust fault detection filter are given in terms of matrix inequalities (MIs), which can be solved by applying iterative linear matrix inequality (ILMI) techniques. Particularly, compared with two existing LMI methods, the developed algorithm is more generalized and less conservative. An illustrative example is given to show the effectiveness of the proposed method.     

Subspace aided data-driven design of robust fault detection and isolation systems

This paper deals with subspace method aided data-driven design of robust fault detection and isolation systems. The basic idea is to identify a primary form of residual generators directly from test data and then make use of performance indices to make uniform the design of different type robust residuals. Four algorithms are proposed to design fault detection, isolation and identification residual generators. Each of them can achieve robustness to unknown inputs and sensitivity to sensor or actuator faults. Their existence conditions and multi-fault identification problem are briefly analyzed as well and the application of the method proposed is illustrated by a simulation study on the vehicle lateral dynamic system.     

Fault Detection Based On Online Probability Density Function Estimation

This study presents a new fault detection scheme based on the probability density function (PDF) of system output. Unlike the classical fault detection and diagnosis methods, in the proposed method, distribution of the system output is estimated online. To achieve this goal, an algorithm is introduced to estimate PDF online using fuzzy logic. Furthermore, convergence of this algorithm is investigated. Then, a residual is constructed that can show the existence of a fault in the system. The main advantages of the proposed method are robustness against measurement noise, even though it does not need the exact model and measured data of inputs and states. Simulation results show that this scheme can detect abrupt faults very well.     

一种照度自适应车道检测算法及多核平台实现

针对车道线检测,基于图像白平衡算法和灰度直方图,自适应地提取出感兴趣区域,并自适应确定Canny边缘检测算法的高低阈值.通过对概率霍夫变换得到的直线点集进行RANSAC拟合,满足了在不同光照条件下的自适应车道线检测,并基于英伟达Jetson TK1嵌入式开发板结合开源GUI库Qt,使用其Qt Quick开发出一套车道线检测系统.     

基于可变权重损失函数和难例挖掘模块的Faster R-CNN改进算法

基于深度卷积神经网络的目标检测算法已成为目标检测领域中的研究热点,它包括基于区域提议的两阶段目标检测算法和基于位置回归的一阶段目标检测算法。Faster R-CNN是两阶段目标检测的典型算法之一,但是,训练数据集中简单样本-〖KG-*8〗难分样本数量不平衡,以及样本数据的类间不平衡,都是影响Faster R-CNN检测精度的重要原因。本文提出一种基于可变权重损失函数Focal Loss和难例挖掘模块的改进Faster R-CNN算法。具体地,在网络的分类部分引入Focal Loss函数,通过权重调节样本数据的类间不平衡,改善简单样本-〖KG-*8〗难分样本的数量不平衡;同时,修改网络结构,引入难例挖掘模块,进一步平衡简单样本-〖KG-*8〗难分样本的数量,提高网络的检测性能。本文采用不同数据集,不同基础网络来测试提出的算法性能。实验结果表明,在VGG-16基础网络下,本文算法在Pascal VOC 2007数据集上平均检测精度较原算法提高了0.9个百分点,在Pascal VOC 07+12数据集上提高了1.7个百分点;在Res-101基础网络上,在Pascal VOC 2007数据集上平均检测精度较原算法提高了1.3个百分点,在Pascal VOC 07+12数据集上提高了1.5个百分点。