Papers by Author: Juggrapong Treetrong

Abstract: This paper proposes a new method of motor fault detection. ML Estimation is proposed as a key technique for signal processing. The stator current is used data for motor fault analysis. ML Estimation is generally applied to estimate signals for nonlinear model. The expectation is that the method can provide information for fault analysis. The method is tested on 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. Based on experiments, the method can differentiate conditions clearly and be also able to measure fault severity levels.

Abstract: This paper proposes a new method of motor fault analysis. Gershgorin Disk is proposed as a key technique for processing all the 3 stator phase currents. Gershgorin Disk is broadly applied to estimate sources with unknown signal numbers. This paper introduces the technique for motor fault analysis. The method is expected to provide a high accuracy method of fault analysis. The Gershgorin Disk is tested with a source of 3 sinusoidal stator phase currents under different conditions. There are 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. Based on experiments, the method can show differentiation of motor conditions with high accuracy. They seem also to be able to indicate fault severity levels by observing radii and center of among the disks.

Abstract: This paper proposes a new method for motor fault analysis. Windowed-Zeropadded FFT is applied as a signal processing method. The method is based on both windowing and zero-padding of the signal. The expectation is that the method can provide harmonic amplitude more visible for the purpose of motor fault analysis. The method is tested on 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. The method can provide more visible harmonic amplitudes than other methods, because it can eliminate the leakages and provide smoother plotting. Thus, it can help improve accuracy of motor condition classification and the prediction of the fault severity levels.

Abstract: This paper proposes a new method of motor fault detection by applying the eliminated-signal as data sources for motor fault analysis. Bi-spectrum is used as a key method for processing the signal. The expectation is that the eliminated-signal may contain information for fault analysis. The spectrum and bi-spectrum of the signal are applied as signal processing methods to analyze the motor faults. The method is tested on 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. Based on experiments, the method can differentiate conditions clearly. They seem also to be able to measure fault severity levels by observing the change in among harmonic amplitudes.

Abstract: This paper proposes new procedures of motor fault detection. The proposed methods are based on filtered-signals and eliminated-signals. Generally, the raw stator phase currents collected from the motors are firstly filtered in order to get rid of measurement noises. If the new signals are called “Filtered-Signals” and the signals eliminated from the raw stator phase currents are called “Eliminated-Signals”. The first proposed procedure is to detect the motor faults by spectrum of PSD slope from the filtered-signals. The second proposed procedure is to detect the motor faults by spectrum of the eliminated-signals. The both methods are tested on 3 different motor conditions: healthy, stator fault, and rotor fault motor at full load condition. The experiments show that the both methods can differentiate conditions clearly and they also can indicate the levels of fault severity. Thus, it can be effective when the both methods are applied simultaneously to analyze the faults

Abstract: Because the faults happening in the motor (such as the stator and the rotor faults) can distort the sinusoidal response of the motor RPM and the main frequency, hence the spectrum method has previously been introduced which it relates to both amplitudes and phases among harmonics in a signal. The method popularly applied for fault detection is based on frequency analysis by observing the side band, its harmonics around main frequencies or its other harmonics. Based on the present experiments, the spectrum method by FFT function has ability to distinguish the motor condition. But, the fault severity levels seem to not able to analyze. Hence the time-frequency Analysis (or spectrogram) of the stator phase currents is proposed here. The method is expected to show relation between the phase current signals and the fault levels which make it can detect the faults and also indicate the fault levels. The experiments show that the proposed method can provide good accuracy for fault prediction and fault level quantification. Hence it can conclude that the propose method can be an effective tool for motor fault prediction.