In this paper, a dc-dc buck converter with digital PI-controlled is analyzed and designed considering all design parameters such as inductance current variation, output voltage ripple etc. The designed dc-dc buck converter provides stable output voltage against to load changes and output voltage variations. Buck converter control method relies on voltage mode controlled PWM (Pulse width Modulation) with digital PI (Proportional Integral) controller. The design criteria, operating mode selection, suitable material selection, etc. of low cost and high-performance buck converter are explained in detail. Finally, the designed converter is carried out experimentally and the experimental results shows the effectiveness of designed converter under different load profiles.

This paper presents a separation method to discern broken rotor bar fault from low frequency load torque oscillation thorough radial leakage flux spectrums in induction motors (IMs). Broken rotor bar fault can usually be detected using classical motor current based analysis (MCSA), but it may not provide reliable results since its performance depend on motor topology, stator winding and load type. Particularly, if a motor is subjected to load fluctuation, then oscillation related signatures exhibit similar behavior that of broken bar which leads misleading signatures. In this paper, radial leakage flux spectrum is exhaustively analyzed thorough a fluxgate sensor to discern these two effects in IMs. It is shown that there are some additional characteristics broken bar signatures such as 3sf _s and (f _s -f _r )±2sf _s in radial leakage flux which do not appear in low frequency load torque oscillation case. A 2-Dimensional (2D) finite element analysis and experiments are carried to show that using leakage flux can provide a separation method and more reliable results than classical MCSA in IMs.

The relative positions of broken bars can potentially change the current signature content, filter some of the components and hence lead to misleading in diagnostic process. Especially, if the positions of broken bars are in half pole pitch distance, the characteristic sidebands harmonics get lower which leads to decrease diagnostics ability of stator current based analysis. This paper proposes to analyze double bar breakages faults by monitoring the three-axes vibration (-x, -y, and -z) signals through an accelerometer in detail. The characteristics fault signatures are presented in axial-x, radial-y and gravity-z axis vibrations and a neural network-based classifier (Multi-Layer Perceptron) is utilized to classify the type of double bar breakages. The findings are verified through the experiments. It is shown that some of characteristics fault signatures such as 2sfs, 3sfs and 2fr±2fs at corresponding vibration spectra can provide more reliable result to detect and classify the broken bar fault in induction motors (IMs).

Permanent magnet synchronous motors (PMSMs) have superior features such as less volume and weight, long-life, high performance compared to induction motors. In this study, a comparative design of PMSMs is provided by considering a 2.2 kW induction motor nameplate parameters which is commonly used in industrial applications. A parametric analysis is used in order to design stator slot and magnet geometries of Surface-Mounted Permanent Magnet Synchronous (SPM) and Interior Permanent Magnet (IPM) Motors to get the high efficiency, low torque ripple. Ansys@Maxwell-2D software using time stepping finite element method is utilized to verify the advantages of designed motors compare to induction motors. In addition, material consumptions of both PMSMs and induction motor are compared to show the effectiveness of proposed motors in mechanically. It is shown that designed SPM and IPM motors have higher efficiency, lower torque ripple and volume than that of induction motors.

Vibration analysis is considered the most common and effective means of detecting mechanical faults such as imbalance, misalignment, and looseness in induction motor driven systems. Most mechanical problems result in an increase in vibration at multiples of the rotor speed frequency (1x) making it difficult to discern the source of vibration. In case of a fault alarm, the maintenance engineer usually performs a walk-around test to identify the source of vibration for planning maintenance, and therefore, is exposed to safety risks. In this paper, a new remote and automated test method for identifying the source of mechanical vibration during the starting transient of induction motors is proposed. The level and speed-dependency of vibration during rotor acceleration are used for identifying imbalance from other mechanical defects that produce 1x vibration. Test results on a 380 V, 5.5 kW induction motor under mechanical defects are given for verification. It is shown that the proposed method can provide automated identification of the source of vibration enabling maintenance to be performed in a safe, low cost, and efficient manner. The data acquired and analyzed for the testing are described and shared through this paper.

The aim of this paper is to detect broken rotor bar fault at the presence of low frequency load torque oscillation in inverter-fed induction motors. The low frequency load torque oscillation in induction motor may sometimes have the same effect as broken rotor bar fault on the stator current. Especially, when load torque oscillation frequency is close to twice the slip frequency, additional processing need to be done to separate these two effects from each other. To discern these two effects, Analytical Signal Angular Fluctuation (ASAF) spectrum is used. Experimental results are presented for separating broken rotor bar fault from low frequency load torque oscillation.