Pan Hong-xia

Fuzzy Controller Design of Autonomy Overtaking System

Based on the fuzzy control theory and taking vehicles overtaking process as the research objective, the fuzzy controller is designed and simulated. For the control strategy of intelligent vehicles, most research institutions construct the model according to the given moving trajectory, which has the disadvantages of low anti-jamming capability, great costs and lower response speed. The fuzzy control strategy for intelligent vehicles in this paper is a new dynamical fuzzy controller with three-input and three-output, and its control rule base is composed of 135 pieces of fuzzy reasoning rule. The simulation result proves that the control system of this dynamical fuzzy controller is obviously superior to the traditional system of non-fuzzy controller.

Remote data monitoring system design based on GSM short message service

The development of communication and network technologies has greatly improved the data transmission speed and efficiency. In this paper the design and implementation of a remote data collection and monitoring system was investigated. The proposed system utilized GSM short message service to perform remote data collection and monitoring. The communication software written in VB programming language achieved efficient control of serial interface ports and real-time synchronization of remote data into system database, thus the multi-directional data monitoring was accomplished effectively.

A hybrid PSO-DV based intelligent method for fault diagnosis of gear-box

The gear box fault occur can lead to the fatal breakdown of mechanical system. Back propagation neural network (BPNN) have been proved to be of widespread utility for identifying and classifying gear box faults to prevent serious damage in a mechanical system. Some researchers have used particle swarm optimization (PSO) to train BPNN. However, because the PSO algorithm has several parameters to be adjusted by empirical approach, if these parameters are not appropriately set, the search will become very slow near the global optimum and even trap into local minima. In this paper, a novel hybrid intelligent method for classifying gear box faults based on vibration signal using the particle swarm optimization (PSO) algorithm, differential evolution (DE) algorithm and BPNN named PSO-DV based BP is presented. The proposed PSO-DV includes both faster convergence of PSO and capability escape from local optima of DE. Experiments were performed on a gear-box fault simulator. The fault samples are obtained by simulating corresponding fault on experiment gear-box. In presented work, a classical PSO based BP neural network and PSO-DV based BP neural network are used for gear box fault classification, their relative effectiveness in fault diagnosis is compared. The experimental results verified that proposed hybrid PSO-DV intelligent method can escape from local minima, so has better convergence than BP neural network and classical PSO based BP neural network. Meanwhile, it achieves also very high accuracy rate of recognition and thus provides decision support in fault classification.The gear box fault occur can lead to the fatal breakdown of mechanical system. Back propagation neural network (BPNN) have been proved to be of widespread utility for identifying and classifying gear box faults to prevent serious damage in a mechanical system. Some researchers have used particle swarm optimization (PSO) to train BPNN. However, because the PSO algorithm has several parameters to be adjusted by empirical approach, if these parameters are not appropriately set, the search will become very slow near the global optimum and even trap into local minima. In this paper, a novel hybrid intelligent method for classifying gear box faults based on vibration signal using the particle swarm optimization (PSO) algorithm, differential evolution (DE) algorithm and BPNN named PSO-DV based BP is presented. The proposed PSO-DV includes both faster convergence of PSO and capability escape from local optima of DE. Experiments were performed on a gear-box fault simulator. The fault samples are obtained by simulating corresponding fault on experiment gear-box. In presented work, a classical PSO based BP neural network and PSO-DV based BP neural network are used for gear box fault classification, their relative effectiveness in fault diagnosis is compared. The experimental results verified that proposed hybrid PSO-DV intelligent method can escape from local minima, so has better convergence than BP neural network and classical PSO based BP neural network. Meanwhile, it achieves also very high accuracy rate of recognition and thus provides decision support in fault classification.

Wavelet Neural Network Based on Modified PSO and Its Application in Pattern Recognition

A modified particle swarm optimization--compound model PSO with stochastic inertia weigh is put forward and used to optimize the parameters of wavelet neural network. The trained wavelet neural-network is applied to the Iris classification experiment. The experimental result indicates that the wavelet neural-network training method based on the modified PSO is effective. This is an available approach to solve some problems, such as the pattern recognition, condition monitoring and fault diagnosis, etc.

Automatic control of deep-hole chrome-plated system basing on WinCC

Aim at the drawback of original chrome-plated system and the demand of modified control system, while retaining the original manual control mode, in this paper, SIMENS configuration software WinCC and S7-300 series PLC as well as the original systems automatic equipment are proposed to compose the DCS system, having realized the automation of the entire electroplated production process, and at the same time system remote monitoring function is added to ensure that the entire production process can be carried out smoothly. In this system, PC configuration software mainly completes the function of data acquisition, history database management, parameter setting of production process, simulation display of production scene, warning, report form and printing. Modified system has realized dual-process running simultaneously and automatic control of all the electroplated power supply and temperature of the key slot. Feasibility of the control plan has been confirmed through experimental data. The system has been applied to practice, and since trial operation of the system, the product has been completely qualified and received high praise from the users.

Research on Fault Diagnosis of Gearbox Based on Particle Swarm Optimization Algorithm

In this paper, base on studying learning rate of PSO, in order to adjust the social part and the cognition part proportions, learning rate change linearly with velocity-formula evolving is made; the BP neural network PSO training heavily increases the congruence speed of the networks to avoid involving local extremum. According to actual data of two levels gearbox in vibration lab, signals are analyzed and their feature values are abstracted. By applying trained BP neural networks to diagnosing gearbox faults got sound effect

Fault feature extraction based on KPCA optimized by PSO algorithm

For blindness of the parameter settings in kernel principal component analysis (KPCA), kernel function parameter optimized by particle swarm optimization algorithm (PSO) is proposed, and KPCA is applied to feature extraction. The mathematical model of kernel function parameter optimized is constructed firstly, then the particle swarm optimization algorithm with adaptive accelerate (CPSO) is used to optimize it. The optimized KPCA is applied to feature extraction of gearbox typical faults. The results indicate that KPCA after parameter optimized can effectively reduce the dimensions of feature vector of gearbox, and it has a better fault classification performance than linear principal component analysis (PCA). This method has an advantage in nonlinear feature extraction of mechanical failure signal.

Fault diagnosis of circuit board based on fault tree

PCB fault detection and positioning is always a complex and difficult work. This thesis designed a fault diagnosis system for PCB circuit, which uses voltage signals as incentive signals and the voltage or current response signals as the output. Fault tree is established and fault searching and positioning method introduced fault dictionary analysis according to the fault tree. Through simulation analysis, it is indicated that fault diagnosis of circuit board based fault tree analysis is feasible.PCB fault detection and positioning is always a complex and difficult work. This thesis designed a fault diagnosis system for PCB circuit, which uses voltage signals as incentive signals and the voltage or current response signals as the output. Fault tree is established and fault searching and positioning method introduced fault dictionary analysis according to the fault tree. Through simulation analysis, it is indicated that fault diagnosis of circuit board based fault tree analysis is feasible.

The Research of Higher-order Cumulant Spectrum for Vibration Signals of Gearbox

In the fault diagnosis of gearbox, the extraction of the fault signal is a key problem. The practical testing vibration signal of gearbox is no stable or Gauss distributing. In different fault states, the vibration signal has different Gauss property and symmetry property, usually including stronger noise and low SNR. Faint fault information is often totally flooded in the noise, so it is very difficult to extract the signal characteristic. Because signals higher-order cumulant is not sensitive to the adding Gauss noise and symmetry non-Gauss noise, if applied in the fault diagnosis of the gearbox, it can separate signal and noise effectively, improve the SNR and intensify fault information. Based on the contrast and analysis of the vibration signal of some second gearbox under different states, this paper has extracted the power spectrum and higher-order cumulant spectrum (bispectrum) of the gear vibration signals, has set up the characteristic vector of bispectrum used in fault diagnosis. The analysis result shows that, compared with power spectrum, the characteristic extracting from the higher-order cumulant spectrum is more sensitive to the fault characteristic, and easy to realize the digital characteristic extracting in the intelligent diagnosis, can identify the fault of gearbox effectively

Leveling control technology of hydraulic system based on fuzzy decoupling algorithm

Hydraulic leveling system has been widely used in modern national defense and civilian technologies. The control of hydraulic automatic leveling is a complex nonlinear time-varying system, and during the hydraulic leveling, the system will appear the “implicated coupling” problems between hydraulic legs as well as between the angle and legs. In this paper, the “surface -adjust-surface” leveling technique is proposed by the leveling method. The method is based on the target surface with multi-point and multi-direction control by outputting control variables from leveling the relative position between leveling surface and target surface. We design the decoupling fuzzy controller of MIMO(multiple input multiple output) nonlinear dynamic solution. Besides, we adopt fuzzy decoupling algorithm to correct the output variables on line, so as to solve the coupling problem in the process of leveling and realize the multi-point adjustment. After on-site commissioning and target practice test, the result shows that the method not only has the advantage of simple principle and stable-reliable control performance, but also perfectly realizes precise and rapid leveling of hydraulic overloading platform. It can be promoted applies in the multi-spot automatic leveling system