Yugang Fan

An attitude control method of unmanned helicopter based on adaptive output feedback

The unmanned helicopter exhibits a complex and nonlinear dynamic behavior and open-loop unstable. This paper describes a attitude control method based on adaptive output feedback for an unmanned helicopter. First, it is assumed that the controlled system satisfies the output feedback linearization conditions. Second, the approximate model of the system is considered as the diffeomorphism of the system. Then, a linear controller and adaptive neural networks are designed to cancel the model errors produced by nonlinear, uncertainty and disturbance. Finally, the boundedness of tracking errors and weight errors are studied with Lyapunov stability theorem. The application results of an unmanned helicopter show that the proposed controller can not only cancel the dynamical error effectively but also improve tracking performance of the attitude control system.

The Rolling Bearing Fault Feature Extraction Based on the LMD and Envelope Demodulation

Since the working process of rolling bearings is a complex and nonstationary dynamic process, the common time and frequency characteristics of vibration signals are submerged in the noise. Thus, it is the key of fault diagnosis to extract the fault feature from vibration signal. Therefore, a fault feature extraction method for the rolling bearing based on the local mean decomposition (LMD) and envelope demodulation is proposed. Firstly, decompose the original vibration signal by LMD to get a series of production functions (PFs). Then dispose the envelope demodulation analysis on PF component. Finally, perform Fourier Transform on the demodulation signals and judge failure condition according to the dominant frequency of the spectrum. The results show that the proposed method can correctly extract the fault characteristics to diagnose faults.

Bearing fault diagnosis method based on singular value decomposition and hidden Markov model

The fault signal feature extraction and fault identification of the bearing has important scientific research significance in the mechanized production. Aiming at this, this paper puts forward bearing fault diagnosis method based on singular value decomposition (SVD) and Hidden Markov Model (HMM). To gain required fault feature information, firstly, it builds Hankel matrix, and conducts decomposition through SVD. SVD method is helpful for gaining effective fault feature information from the complex bearing fault signals, and then apply the achieved characteristic value to build the training model of Markov. The test result proves that the method of this paper has good practicability in the bearing fault identification.

Design and realization of vibration signal acquisition & analysis system based on STM32

Efficient acquisition of vibration signal information is key to the bearing fault diagnosis, a design scheme is hereby proposed for bearing vibration signal acquisition & analysis system based on STM32 in this paper. This system comprised a sensor to obtain vibration signal data, which were transmitted to STM32 micro-controller after A/D conversion, then STM32 controled WI-FI module to transfer these data to PC. Afterwards, Empirical Mode decomposition (EMD) was adopted to analyze and process the vibration data to perform a remote monitoring on the running state of the rolling bearing. The experiment results have shown that the system proposed in this paper is capable of collecting and analyzing vibration signals effectivly of the rolling bearing, it has also provided a good transmission performance at a fast speed and can be introduced to the industry.

Design of a remote stress monitoring system based on GPRS and ZigBee

In consideration that it is impossible to effectively transmit and promptly analyze the monitored data under variable environment in remote areas, this paper designed a remote stress monitoring system based on GPRS and ZigBee. The lower position machine of system consists of optical fiber sensor module and ZigBee module. The lower position machine will pack monitored the data of stress that optical fiber sensor measured through cascade transmission by ZigBee module. Then, the packed data will be sent by GPRS module of coordinator node to a remote server. The software on the upper computer are written in C# language, on which users can check the monitored data of stress when the client connected with the server. In this way, they can handle the alarms in time and set the alarm threshold of ZigBee through the client for remote wireless stress monitoring of steel on a real-time basis. Stable and economical for transmitting data at a fast speed, this system can completely monitor the stress in variable environment on a real-time basis.

An acoustic emission system for detecting failures of check valves based on STM32 and high-speed USB bulk transfer

As core equipment for transporting solid and liquid slurries through pipelines, reciprocating diaphragm pump is important for analyzing and monitoring failures of check valves on a real-time basis. In this paper, a pipeline of China for transporting iron ore concentrate within a long distance is examined. Besides, an STM32F103ZET6-based high-speed data acquisition and transmission system is designed according to the failures of check valves incurred after long-term operation inside reciprocating diaphragm pumps, such as jamming and leak. This system may upload acquired data to PC through USB Bulk transfer, the highest speed can up to 480Mb/s. Be applicable to hot-plugging, it may accurately transmit data at high speed. The upper computer is designed with C# language, so functions could be selected and the analytical results could be stored on visual interfaces. The experimental results suggest that the system designed could not only rapidly and accurately acquire and transmit data, but can also effectively detect failures of the diaphragm pumps.

Research on moving target detection algorithm based on MRA and wavelet threshold

Aiming at solving the noise problem which had lowered the accuracy in the target detection result from the process of image-collection and image-transmission, the paper proposes a new target detection algorithm based on the improved wavelet threshold. Firstly, these images are filtered by a denoising method, which combines the wavelet threshold method with the correlation of the wavelet coefficients, and its “Zoom” feature can eliminate the negative impact of noise; then a combined algorithm which connects background subtraction and two consecutive inter-frame subtraction is set up in order to combine their advantages and improve the effect of target detection. Compared simulation results of the four models, the results show that the target detection method based on multi-scale wavelet threshold is reasonable and effective, and more suitable for the real-time target detection.

Fault-Tolerant Integrated Navigation Algorithm of the Federal Kalman Filter

As the Federal Kalman filter has some good features such as flexibility, and good fault-tolerance, this paper proposes a federal Kalman filter design method and fault-tolerant structure. The structure uses the residual error between the local filter and reference filter for fault detection. In this paper, a simulation study of the integrated navigation system is done. The study shows that the algorithm is very simple, reliable, not only can quickly detect the fault of the external sensors and reference system, but also has good fault-tolerant. It can quickly detect and isolation fault, and let the integration of the system remain high precision.

Bearing fault signal feature extraction based on SVD and generalized S-transform module matrix

In order to extract the weak fault information from rolling bearing vibration signals, a method of feature extraction of bearing vibration signal based on singular value decomposition (SVD) and generalized S-transform module matrix was proposed. Firstly, mutant information is separated from the noise background and smooth signal by using SVD, according to the distribution state of singular value, selecting the transition stage of the singular value to extract mutant signal; then using the mean value of sum of squares of generalized S-transform module matrix amplitude to locate mutant information and the fault feature of bearing vibration signals are extracted for fault diagnosis. This method is used for representing characteristics of the bearing outer circle and inner circle partial fault, and through the fundamental frequency information can accurately detect and identify the type of fault. The result shows that this method proposed here is feasible and effective.

Research on mineral yield in remote monitoring system based on GPRS

For some current problems in mineral production, such as measuring difficulty and low accuracy rate, how to do on the mineral yield accurate and convenient measurement is very important. This paper presents a remote mineral output monitoring system based on GPRS. The overall structure of the system includes monitoring terminal, GPRS wireless transmission network and monitoring service center three parts. After the mineral production was weighted by weighing equipment, the obtained data information will transmit to the microprocessor through RS232. After STM32 on the data processor, storage, and then via RS485 bus to send data to GPRS communication module, and then the data is sent over the Internet to the server. Conversely The client can access the server to control the monitoring of mineral output data, to achieve the real-time and accurate monitoring of mineral production.