Search

In order to solve the problem of small sample data and difficulty to judge the working state of machinery machine in oil analysis, an oil analysis method based on kernel density estimation and three-line values method is proposed. First, kernel density estimation is used to solve the unbiased estimation of the oil sample data, and the statistical average and standard deviation of the samples are obtained by the probability density function. Then, combined with the three-line values method, the normal line, warning line and danger line are generated to judge the working state of the engine, and the results are obtained. Finally, the proposed method is used to analyze and judge the oil data of an engine, based on the given criteria. The results imply that the method is effective.

The high speed railway (HSR) is a complex system with many subsystems and components. The reliability of its core subsystems is a key consideration in ensuring the safety and operation efficiency of the whole system. As the service time increases, the degradation of these subsystems and components may lead to a range of faults and deteriorate the whole system performance. To ensure the operation safety and to develop reasonable maintenance strategies, fault detection and isolation is an indispensable functionality in high speed railway systems. In this paper, the traction power supply system, bogie system, civil infrastructure system, and control and signaling system of HSR are briefly summarized, and then different fault diagnosis methods for these subsystems are comprehensively reviewed. Finally, some future research topics are discussed.

At present, China's railway safety status monitoring is mainly base on the way of manual inspection and rail inspection vehicle. The security status monitoring of infrastructure, train running and environment of railway line is mainly based on data acquisition equipment of ground platform, which can successfully provide a monitoring service. Furthermore, we put forward space-air-vehicle-ground integration network to monitor the safety status of railway running comprehensively, especially for infrastructure, railway environment, and train running status etc. In this paper, we present all-round space-air-vehicle-ground integration network detecting program based on unmanned aerial vehicle (UAV). The article focuses on the introduction of the methods of the fastener safety status, foreign object detection and researches on security risk warning and application service. The program can improve the rail safety level of Large-scale road network and provides platform support for all-round railway security detecting system.

A non-contact volume measurement method for irregular objects is proposed in this paper based on 3D reconstruction technology, where a linear laser and a camera are used. The line-surface model of laser surface and feature points is first established in a camera coordinate system. The point cloud coordinates of measured object are calculated using the geometric measurement principle and transformed into the world coordinate system from the camera coordinate system. The differential object point cloud is obtained by subtracting the reconstructed model from the initial background model, and the object volume is calculated by integrating the differential depth point cloud data. To extract accurate center of light stripe, a fitting algorithm is proposed using the truncated gaussian distribution. The measurement error is less than 4.5% at the distance of 2 meters away from the measurement devices. The experimental results show that the proposed method can accurately reconstruct 3D objects and accurately measure the irregular objects volume.

Snow detection and removal from video images is very challenging. Normally the snowflakes affect only on a very small region of an image, hence the confusion to determine which region should be considered and which one should not. In this paper, a frame difference method with five successive frames is first presented to detect the snow pixels from image background, but the method didn't work well in the case of heavy snow. Then a new technique has been implemented which uses the L 0 gradient minimization approach to remove the snow pixels. This technique can control how many non-zero gradients are resulted in the image, and is independent of local features, but instead locates important edges globally. These salient edges are preserved and the low amplitude and insignificant details are diminished. The snow pixels are then removed in this way. Experimental results show that this method is a highly efficient algorithm even under heavy snow conditions, while preserving the details of the image.

Boolean network (BN) is a popular mathematical model for revealing the behavior of a genetic regulatory network, and observability plays a vital role in understanding the underlying network feature. However, the observability of attractor cycles, which is an interesting and important problem, has not been addressed in the literature. In this paper, we first proposed a novel problem on attractor observability in BNs. Identification of the minimum set of consecutive nodes can be used to determine uniquely the attractor cycle from the others in the network. We then develop a linear-time algorithm to identify the desired set of nodes. The proposed approaches are demonstrated and verified by numerical examples. The computational results are given to illustrate both the efficiency and effectiveness of our proposed methods.

Modeling genetic regulatory networks is an important issue in systems biology. Various models and mathematical formalisms have been proposed in the literature to solve the capture problem. The main purpose in this paper is to show that the transition matrix generated under semi-tensor product approach (Here we call it the probability structure matrix for simplicity) and the traditional approach (Transition probability matrix) are similar to each other. And we shall discuss three important problems in Probabilistic Boolean Networks (PBNs): the dynamic of a PBN, the steady-state probability distribution and the inverse problem. Numerical examples are given to show the validity of our theory. We shall give a brief introduction to semi-tensor and its application. After that we shall focus on the main results: to show the similarity of these two matrices. Since the semi-tensor approach gives a new way for interpreting a BN and therefore a PBN, we expect that advanced algorithms can be developed if one can describe the PBN through semi-tensor product approach.

To describe the relationship between failure modes and maintenance cycle, an preventive maintenance cycle optimization model for the metro vehicle is introduced. The fault data is statistically analyzed in accordance with the Heinrich safety rule. And the fault distribution type of the system is determined using the AMSAA(Army Materiel Systems Analysis Activity) model. Then, the optimal mathematical model for maintenance cycle is presented. Finally, the genetic algorithm method is used to obtain the optimal maintenance cycle. The experimental results show that the genetic algorithm is capable of reaching the global optimal solution quickly. The model can be used to provide decision-making guidance and theoretical support for the vehicle maintenance departments.