Bogdan Betea

Comparison between envelope detection methods for bearing defects diagnose

Abstract This paper presents some of the methods which could be used for envelope detection applied to bearing defects vibration signals. These methods are bases on different approach: classical Hilbert transform approach, Shock filters, Gaussian filtering approaches and modulus of the signal with frequencies band truncation. The results obtained applying these methods are compared using frequency spectrum of the envelope signals. There are presented the advantages and disadvantages of compared methods. The bearing diagnoses methods which are study by this research can be used on bearing diagnoses of power generators from hydroelectric or thermoelectric plants.

Spectrum analyzer instrument based on OMAP3530

OMAP3530 is a Multiprocessor System-on-Chip (MPSoC) device which integrates ARM Cortex-A8, C64x+, Graphics Accelerator and various peripherals, its designed to meet mobile multimedia applications so it is a good solution for a spectrum analyzer implementation. In this paper is presented a spectrum analyzer instrument implementation based on OMAP3530 architecture, using FFT algorithm. The FFT algorithm is implemented on DSP and the user interface is implemented on ARM and Linux OS. DSP/BIOS Link, library for the inter-processor communication across the ARM-DSP boundary is used This instrument could be a good solution for vibration analyses using an accelerator sensor, sound or any other periodical input signal. To test this spectrum analyzer instrument during the application development a sound wave was used as input signal. On the ARM side a Linux OS is used, which ensure a proper solution for user interfaces (S-Video, DVI, LCD, touch screen) and interconnections (UART, USB, Ethernet).

Bearing defects diagnosis based on Gaussian filtering shock detection and power spectrum of envelope

This paper proposes a new method of diagnosis the bearing defects using some signal processing tools such as the Gaussian filtering, the envelope detection and the Fourier transform. The Gaussian filtering is used to detect the shock pulses generated by bearing defects. After this phase the shock pulses are allowed to pass the filtering based on the windowing of the obtained Gaussian peaks. The results obtained using the proposed diagnosis method are compared with the results obtained using some other known defect bearing diagnosis methods. The proposed method will prove better capability of defect frequencies enhancement.

Practical aspects of the system identification of a buck converter

Having fast and accurate tools and procedures to determine the transfer function of an unknown or partially known system can be a big advantage during the development process or during system testing. This paper presents practical aspects of the system identification of a power converter using industry tools and software.

Determining the Frequency Response of a DC-DC Converter thru System Identification

Abstract System identification theory provides powerful theoretical tools for the control system design and analysis. Using the ARX model estimation and the system response to a pseudo-random binary sequence the transfer function and the frequency response characteristics are determined for a partially known system.

Time Domain Bearing Diagnosis Using Kurtosis Based Filtering and Fuzzy Logic

On this paper a bearing defects diagnosis method is introduced. This diagnosis method is based on time domain signal processing first step being the kurtosis based filtering. This filtering is based on the detection of the shock pulses generated by bearing defects. The diagnosis is done using the detected defects periods which are the inputs for a fuzzy classifier that provide defect alerts for each kind of bearing localize defect

Numerical Sliding Mode Control for a DC-DC Buck Converter

Due to advances in microprocessor technology the numeric control is becoming increasingly attractive for the power converter designers. This paper proposes a solution based on a voltage-only sliding mode control algorithm for a DC-DC buck converter, it identifies challenges and proposes solutions.

Simulation model of the bearings with defects

A fundamental problem in the development and validation of faults detection and diagnoses is to obtain fault signature data and if is possible to be able to configure fault parameters. To represent physical data, a modeling and simulation is being developed for the vibration signatures of faulted components in bearings. The main approach of this research is to develop a simple model of bearings with defects, model which could be very well simulated and then this simulated model could be further used to validate different diagnosis methods. The mathematical model is going to be used in order to simulate the dynamic behavior of rotating ball bearings which are affected by localize defects on the inner race, outer race or balls. The dynamical model of the bearing is modeled as a three DOF (degree-of-freedom) system.