Leao Rodrigues

Bio: Leao Rodrigues is an academic researcher. The author has contributed to research in topics: Fault (power engineering) & Fault detection and isolation. The author has an hindex of 1, co-authored 2 publications receiving 12 citations.

Induction motor broken bar fault detection based on MCSA, MSCSA and PCA: A comparative study

Abstract: The early failure detection of induction motors is considered very important in order to ensure their stability and high performance. Thus, condition monitoring of these motors are essential to ensure that. However, the effectiveness of the fault diagnosis depends on the quality of the fault features selection that is used in the adopted method. In this way, this paper will present a comparative study of three methods based on the electrical signal analysis. The first one is the most known method, motor current signature analysis (MCSA). Another method based on the analysis of spectral current, is the motor square current signature analysis (MSCSA), will also be used. Finally a third method based on principal component analysis (PCA) is also used. The performance of the methods is tested under different fault and load conditions. Experimental results are presented in order to compare the performance between the three methods.

On the use of supercapacitors within stand-alone energy systems

Abstract: Hydrogen is a valuable alternative for long-term energy storage, particularly for renewable energy based stand-alone systems. The described stand-alone system has been developed and installed at the INETI facilities. The exceeding renewable energy (provided by sun and wind) is used to generate hydrogen, which accumulated as an energy buffer, while the fuel cell uses this stored hydrogen to produce electrical energy when there is insufficient solar/wind energy. To provide the stand-alone system with a reliable energy storage it was designed a system for storing hydrogen based on metal hydrides. In order to supply sudden power demands two options were considered: a standard DC battery bank and a supercapacitor bank. Experimental and simulation results are presented in order to show the installation obtained performance.

New vision about the overlap frequencies in the MCSA-FFT technique to diagnose the eccentricity fault in the induction motors

Abstract: This paper presents a new precise vision to diagnose the eccentricity fault using the motor current signature analysis technique (MCSA). The MCSA method was based on analyzing the FFT of the stator current. But, it has many disadvantages, as operating regimes (non-stationary) or his suffering to distingue the characteristic frequencies of several faults such as short circuit, eccentricity, broken rotor bars, etc. Our study will clarify the essential points of overlap in this method under static and dynamic eccentricity fault (SE and DE). This study proposes a development of the new formulas which precisely shows the overlap between the frequencies. According to MCSA method, we treated their problems in detail for eccentricity fault in the induction motors, while being based on the stator current spectrum. In this paper, experimental results were exploited under static and dynamic eccentricity fault.

Determination of faults in 3–Ø Induction Motor By Motor Current Signature Analysis

Abstract: Induction machine is horse power equipment for industry. It is widely used as drive of many industrial processes but their unexpected failure can cause heavy production loss. Motor current signature analysis (MCSA) is monitoring a parameter of condition in machinery viz the instantaneous current which contain all frequency contain for identification of characteristic fault signature at early stage for avoiding unexpected failure like rotor defect (fragments of rotor bar), stator fault and air-gap peculiar fault. This paper present detailed analysis of how fault frequency is inducing as byproduct of unique rotating flux components and how fault frequency is modulated in stator current which was not done so far. The MCSA Simulation is done by using MATLAB R2017a/Simulink.

Improved Park's Vector Method and its Application in Planetary Gearbox Fault Diagnosis

Abstract: Motor current signature analysis is a commonly used fault diagnosis method of drive system parts However, for some complex mechanical parts such as planetary gearbox, motor current signal is complicated, characteristic frequencies are hard to extract, and weak fault is difficult to identify Thus, in this paper, an Im proved Park's vector method based on three-phase current signal analysis is proposed The analytic signal is constructed by conducting Park transform The instantaneous amplitude and the instantaneous frequency, which contain modulated characteristic frequencies, can be calculated according to the analytic signal Fault characteristic frequencies are extracted by the spectral analysis of the instantaneous amplitude and the instantaneous frequency, and automatic fault diagnosis of the gearbox is accomplished by the spectral indexes The simulation result shows that, compared with traditional planetary gearbox signal analysis method, our method has better effect on characteristic frequency extracting of a three-phase current signal The experiment result shows that our method functioning well on small cracks fault diagnosis of the planetary gearbox

Detectability of rotor failure for induction motors through stator current based on advanced signal processing approaches

Abstract: This paper investigated the ability of the diagnosis techniques and detectability of induction motor faults through a stator current. The proposed techniques are based on advanced signal processing tools. These methods can be classified into: high resolution approaches and time–frequency representations. Sadly, the Fast Fourier transform technique cannot give good results such as the spectral leakage, it needs a big number of measurement data samples. To address these problems, the Multiple Signal Classification technique allows for reducing the spectrum noises and to reduce the computation of signal data samples, requires less memory. However, for the diagnosis in time varying conditions, non-stationary approaches are required to diagnose and detection IM failures in variable speed operation or transient. This article is intended for a comparative study between the spectrogram, the scalogram and the Hilbert-Huang transform. In this context, the results exhibit the effectiveness of the methodology to detect induction machine fault in time varying, it is capable to detect a rotor failure. The performances of these approaches are demonstrated in simulation results using the MATLAB environment and in the experimental validation.

Fault diagnosis of an induction motor through motor current signature analysis, FFT & DWT analysis

Abstract: The paper is focused on one of the important technique practised under Condition Based Maintenance (CBM) procedure, which is known as Motor Current Signature Analysis (MCSA) The technique proposes the study of side-band frequencies, which are generated due to fault conditions within the machine These side-band frequencies are later analyzed using Fast Fourier Transforms (FFT) and Discrete Wavelet Transforms (DWT) In the present work, the two different fault conditions namely Broken Rotor Bar (BRB) and Stator Winding Fault (SWF) are jointly analyzed, as a novel approach The results have been compared with the fault diagnosis of healthy machine This contribution can be helpful in further carrying out study on prognostics of the machines, and in reducing the unscheduled breakdown while increasing the reliability of the machine