Advanced Engineering Forum Vols. 2-3

Abstract: A new condition classification method is put forward based on the analysis of vibration signals. Machine working condition can be recognized by the combination of wavelet packet decomposition (WPD) and multi-scale entropy (MSE). Firstly, vibration signal of machine is decomposed by wavelet packet with the appropriate decomposition layer. Then, each sub-signal in different frequency band is analyzed with the multi-scale entropy. Through analyzing the multi-scale entropy distribution curves of sub-signals for different operating conditions in each frequency band, entropy of certain frequency bands and scales will be chosen as the feature vector, which is used to distinguish different machine conditions. This method presents a novel perspective for rolling bearing default diagnosis and is tested to be very effective to classify different bearing operating conditions through series of experiments.

Abstract: With the development of information technology and use of intelligent sophisticated equipment. Factory automation has improved continuously, the situation of maintenance surplus and maintenance insufficient had often occurred. Through the application research of MES, in-depth understudied fault diagnosis and maintenance technology. In-depth analyzed the theory of equipment integrated maintenance in MES. Designed implementation program for equipment integrated maintenance management for MES by the thought of integrated. Used the idea of segmentation in time and space to establish the model of decomposition for maintenance target. Extraction state information of equipment and parts. Finally, designed operation framework model for equipment integrated maintenance for MES. Analyzed the mode of operation and the process integration of maintenance management detail. Establish integrated maintenance system for equipment in MES implementation to improve the effectiveness of equipment maintenance.

Abstract: This research aims at revealing the rules that the impact of kernel function and its parameters on the performance of kernel principle component analysis (KPCA) for dimensionality reduction. KPCA was performed on nine databases by using different kernel functions and a series of equal space kernel parameters. The relation charters between kernel parameters and the number of kernel principle components were constituted. It found that the Gussian kernel and its parameter above 25 are the best choice for rotating machinery feature vector dimensionality reduction by using KPCA. This study presents a reference and gist for the application of KPCA in rotating machinery fault diagnostic case.

Abstract: This research aims at defining a character of rotating machinery——KIC, that the typical rolling bearing and gear failure modes can be effectively identified by using the character. Firstly, A correlation coefficient matrix is composed by the correlation coefficient between two-two inde-pendent component derived from kernel independent component (KICA). Then the KIC is defined by the correlation coefficient matrix. Experimental results show that the KIC has a good effect for identifying the bearing and gear fault modes, so it can be used as sensitive character for rotating machinery fault diagnosis.

Abstract: The wavelet ridge method to analyze chaos is described, and the wavelet ridge method is applied to analysis of the nonlinear vibration of blooming mill which exists chaos. The results show that the wavelet ridge can tell the periodic motion, quasi-periodic motion or chaotic motion by analysising the time history of one component of the system state variables. Compared to the other researching methods, such as the Poincaré sections or the phase diagram, we can find the wavelet ridge is more suitable to high dimensional chaotic systems and the clutter of instantaneous frequency which is represented by the wavelet ridge can distinguish between strong and week chaos motion. And it can provide more accurate partial details and features of chaotic motion.

Abstract: In this paper, we proposed an improved Second Derivative Gaussian (SDG); the endoscopic image of the engine fault was treated by a combination of wavelet and improved SDG, in order to enhance the quality of the image and denosing. The results shows that the propose method can reduce the noise and improve the detail of the image. Provide support for further diagnosis.

Abstract: The model based diagnosis method can detect both the fault location and the fault extent of the rotating machinery at the same time as it can combine the dynamics and fault diagnosis method together. However, the modal test of the supporting part is necessary when using the existing model based method, and the precision of the modal test results make a strong impact on the diagnosis results. In order to improve this case, an improved model based diagnosis method is presented herein by combining the fixed-interface modal synthesis method with the traditional model based diagnosis method. The DOF of the unidentified parameter due to the supporting parts are partitioned to interface DOF, and the displacement information of the supporting parts can be measured by the mounted sensors. Therefore, the model based diagnosis method can be carried out by only using the parameters of the rotor without considering the effects from the supporting, which can greatly improve the diagnosis precision and efficiency. This method is fit for the rotor systems with unidentified supporting parameter, and the rotor systems with unknown nonlinear supporting parameter. Both the numerical simulation results and the experiment results in the example proved the efficiency of this method.

Abstract: Designing the mine staff management system based on the RFID technology and GIS technology. The personnel ID information can be collected by the locating substation and transmitted to monitor center by the wireless network and cables. User’s personnel ID information which is collected by the locating substation can be used to check the staff attendance and offer the rescuer some information with the trapped victims.

Abstract: For the purpose of improving the future domestic train control systems and securing interoperability, according to the global development trends of train control systems, it is presented that the test results of interoperability between wayside train control systems installed in existed line, and the onboard train control system. Due to the safety-critical characteristics of train systems, the site test in the section where the wayside equipment is installed may lead to a danger against safety. Therefore, by way of constructing a simulation environment of train control systems, the T/R data systems of the equipment for interoperability are confirmed and the interoperability test are obtained by applying these systems to onboard equipment.

Abstract: It is important to secure the technology of electrical devices diagnosis to provide reliability of electrical power supplies in the modern society. In this paper, the electrical utilities diagnosis system is developed by the core technology for the high frequency partial discharge (HFPD) detection. The system detects the partial discharge generated in the electrical devices and analyzes the stability for operation status of the electrical utilities on uninterruptible power supply condition. In addition, we evaluate the stability and reliability for the electrical utilities diagnosis system by the experiments in the laboratory. In result, we find the partial discharge signal patterns by the trouble types of the electrical devices. In the future, we‘ll prevent the power failure accident through monitoring the inside strange status of the electrical devices.