Seungdeog Choi

A Simple Real-Time Fault Signature Monitoring Tool for Motor-Drive-Embedded Fault Diagnosis Systems

The reference frame theory constitutes an essential aspect of electric machine analysis and control. In this study, apart from the conventional applications, it is reported that the reference frame theory approach can successfully be applied to real-time fault diagnosis of electric machinery systems as a powerful toolbox to find the magnitude and phase quantities of fault signatures with good precision as well. The basic idea is to convert the associated fault signature to a dc quantity, followed by the computation of the signals average in the fault reference frame to filter out the rest of the signal harmonics, i.e., its ac components. As a natural consequence of this, neither a notch filter nor a low-pass filter is required to eliminate fundamental component or noise content. Since the incipient fault mechanisms have been studied for a long time, the motor fault signature frequencies and fault models are very well-known. Therefore, ignoring all other components, the proposed method focuses only on certain fault signatures in the current spectrum depending on the examined motor fault. Broken rotor bar and eccentricity faults are experimentally tested online using a TMS320F2812 digital signal processor (DSP) to prove the effectiveness of the proposed method. In this application, only the readily available drive hardware is used without employing additional components such as analog filters, signal conditioning board, external sensors, etc. As the motor drive processing unit, the DSP is utilized both for motor control and fault detection purposes, providing instantaneous fault information. The proposed algorithm processes the measured data in real time to avoid buffering and large-size memory needed in order to enhance the practicability of this method. Due to the short-time convergence capability of the algorithm, the fault status is updated in each second. The immunity of the algorithm against non-ideal cases such as measurement offset errors and phase unbalance is theoretically and experimentally verified. Being a model-independent fault analyzer, this method can be applied to all multiphase and single-phase motors.

Implementation of a Fault-Diagnosis Algorithm for Induction Machines Based on Advanced Digital-Signal-Processing Techniques

In this paper, a complete cross-correlation-based fault-diagnostic method is proposed for real-time digital-signal-processor (DSP) applications that cover both the fault-monitoring and decision-making stages. In practice, a motor driven by an inverter or utility line is run at various operating points where the frequency, amplitude, and phase of the fault signatures vary unexpectedly. These changes are considered to be one of the common factors that yield erroneous fault tracking and unstable fault detection. In this paper, the proposed algorithms deal with the ambiguities of line-current noise or sensor-resolution errors and operating-point-dependent threshold issues. It is theoretically and experimentally verified that a motor fault can be continuously tracked when the sensor errors are within a limited range through the adaptively determined threshold definition of noise conditions. The offline experiments are performed via Matlab using actual line-current data obtained by a data-acquisition system. These results are verified on a DSP-based motor drive in real time where drive sensors and a digital signal processor are employed both for motor-control and fault-diagnostic purposes.

Iterative Condition Monitoring and Fault Diagnosis Scheme of Electric Motor for Harsh Industrial Application

This paper presents a robust diagnosis technique by iteratively analyzing the pattern of multiple fault signatures in a motor current signal. It is mathematically and experimentally proved that the proposed diagnosis algorithm provides highly accurate monitoring performance while minimizing both false detection and miss detection rate under high noise and nonlinear machine operating condition. These results are verified on a digital-signal-processor-based motor drive system where motor control and fault diagnosis are performed in real time.

Optimal design of five-phase permanent magnet assisted synchronous reluctance motor for low output torque ripple

This paper presents the optimal design of five-phase permanent magnet assisted synchronous reluctance motor (PMa-SynRM) for low torque ripple. PMa-SynRMs are similar to interior permanent magnet (IPM) motors in structure but with reduced permanent magnets, PMa-SynRMs are more economical. In this study, lumped parameter model (LPM) is used in the approach to initially design the five-phase PMa-SynRM. Thousands of models are designed by LPM, which are then converged to optimized model using differential evolution strategy (DES). Optimization is done with maximum efficiency and minimum torque ripple as objective. The optimized 3 kW five-phase PMa-SynRM is then analyzed by finite element method (FEM) for fine tuning. Simulation results for back electromotive force (EMF), developed torque, torque ripple, cogging torque, and other necessary motor parameters such as d and q-axis inductances variation over respective axis currents are verified by fabricated prototype.

Comparison of optimized permanent magnet assisted synchronous reluctance motors with three-phase and five-phase systems

This paper presents comparison of optimized permanent magnet assisted synchronous reluctance motor (PMa-SynRM) with three-phase and five-phase architectures. The three-phase and five-phase PMa-SynRMs are designed with magnetic equivalent circuits (lumped parameter model (LPM)) and 2D finite element analysis (FEA) approach. Objective function with torque ripple, motor cost and efficiency is used to derive optimal design of each motor. Multiple parametric simulations are done to minimize the objective function through differential evolution strategy (DES). Both PMa-SynRMs are fabricated with same power rating (3kW) and same volume. Torque pulsation, back electromotive force (EMF), flux linkage, d- and q-axis inductances versus their respective currents and cogging torque are intensively simulated through FEA and are experimentally tested on the prototypes.

Fault Diagnosis Implementation of Induction Machines based on Advanced Digital Signal Processing Techniques

In this paper, a comprehensive cross correlation-based fault diagnostic method is proposed for real time DSP implementation. It covers both fault monitoring and decision making stages. In practice, a motor driven by an adjustable speed drive is run at various operating points where the frequency, amplitude and phase of the fault signatures varies with time. These dynamic changes are considered as one of the common factor that yields erroneous fault tracking and unstable fault detection. In this paper, the proposed algorithms deals with the operating point dependent ambiguities and threshold issues. It is theoretically and experimentally verified that the motor fault can continuously be tracked when the operating point changes within a limited range.

Optimal Control Method of Magnetic Switch Used in High-Voltage Power Supply

In high-voltage systems such as in pulse power, commonly used semiconductor devices cannot be applied to the generation part of high-voltage pulse. However, unlike semiconductor, the magnetic switch cannot control its own ON and OFF operation. Moreover, with the magnetic switch the system efficiency goes down with load variation, and arcs caused by saturation of the energy in reactor have a negative effect on the whole system. This letter presents the way to effectively control the magnetic switch applied to high-voltage pulse power supplies such as plasma generators. The efficiency of the system is optimized by automatically controlling the magnetic switch based on the load variation. Then, the energy the load requires is supplied to the system, which leads to the stability of the system. The proposed method is confirmed by experiments on a designed system.

Optimal sustainable fault tolerant control of five-phase permanent magnet assisted synchronous reluctance motor

This paper presents the optimal sustainable fault tolerant control of a five-phase permanent magnet synchronous reluctance motor (PMa-SynRM). Advanced fault tolerant control system has been required for applications where high reliability and safety is required including hybrid/electric vehicles and aerospace industry. The proposed fault tolerant control strategy is based on advanced vector control of multiphase machine which provide safe machine operation under various phase loss fault conditions. To achieve effective and sustainable fault tolerant operation of PMa-SynRM which utilizes reluctance torque through large saliency ratio, the optimum torque angle has been derived to deliver the maximum output torque while reducing the phase currents to lessen saturation effect in the machine. The optimal set of currents during the fault has been found to provide sufficiently smooth and long-time fault tolerant operation under fault condition. Extensive theoretical analysis, finite element analysis (FEA), and MATLAB simulation has been carried out to derive proposed method. The experimental result has been found by utilizing the 5hp dynamo system controlled by TI DSP F28335.

Fault diagnosis and condition monitoring of bearing using multisensory approach based fuzzy-logic clustering

This paper investigates the application of multisensor fault feature extraction and fuzzy-logic based clustering for the condition monitoring of bearing. Multiple independent sensors on an electric motor drive system provide valuable early indication of a fault, and can be effectively utilized to perform high reliable and optimal fault detection. Through utilizing common sensors including current sensor and vibration sensors in motor, motor current signature analysis (MCSA) and vibration analysis have been used to extract the bearing fault energy. The discrete wavelet transform (DWT) has been applied to monitor energy of the bearing fault signals. Then, the fuzzy c-mean (FCM) has been developed to utilize the data from single sensor and multisensor to identify the severity of bearing fault. Extensive theoretical analysis and experimental test has been performed to demonstrate the advantages of proposed approach. The validity of this study has been confirmed through analysis of the 1/6 HP single phase induction motor and drive system.

Obtaining optimized designs of multi-phase PMa-SynRM using lumped parameter model based optimizer

This paper focuses on multi-objective optimization of multi-phase Permanent Magnet Assisted Synchronous Reluctance Motor (PMa-SynRM) using Lumped Parameter Model based Optimizer (LPO). Optimized PMa-SynRM are cost efficient compared to Interior Permanent Magnet Motor (IPM). Five-phase PMa-SynRMs offer lower torque ripple and higher fault tolerant capability. Depending on various industry application and their application environment, PMa-SynRMs should be customized with appropriate design specification including different current rating, torque ripple, converter cost, magnet cost and overall machine cost. The developed LPO can efficiently extract optimized multi-phase PMa-SynRM models from different design families through defined Objective Functions (OFs). To prove the accuracy of developed LPO for different OFs, four PMa-SynRM models have been obtained while minimizing torque ripple, phase current, machine cost, and magnet costs respectively. Finally, Finite Element Analysis (FEA) of these models has been performed to validate accuracy of the LPO by comparing predicted performances through LPO and FEA.