Dong-Sik Kang

High-Resolution Parameter Estimation Method to Identify Broken Rotor Bar Faults in Induction Motors

The classical multiple signal classification (MUSIC) method has been widely used in induction machine fault detection and diagnosis. This method can extract meaningful frequencies but cannot give accurate amplitude information of fault harmonics. In this paper, we propose a new frequency analysis of stator current to estimate fault-sensitive frequencies and their amplitudes for broken rotor bars (BRBs). The proposed method employs a frequency estimator, an amplitude estimator, and a fault decision module. The frequency estimator is implemented by a zoom technique and a high-resolution analysis technique known as the estimation of signal parameters via rotational invariance techniques, which can extract frequencies accurately. For the amplitude estimator, a least squares estimator is derived to obtain amplitudes of fault harmonics, without frequency leakage. In the fault decision module, the fault diagnosis index from the amplitude estimator is used depending on the load conditions of the induction motors. The fault index and corresponding threshold are optimized by using the false alarm and detection probabilities. Experimental results obtained from induction motors show that the proposed diagnosis algorithm is capable of detecting BRB faults with an accuracy that is superior to the zoom-based MUSIC algorithm.

Decision-level fusion based on wavelet decomposition for induction motor fault diagnosis using transient current signal

In this paper, we propose and implement a decision-level fusion model by combining the information of multi-level wavelet decomposition for fault diagnosis of induction motor using transient stator current signal. Firstly, the start-up transient current signals are collected from different faulty motors. Then signal preprocessing is conducted containing smoothing and subtracting to reduce the influence of line frequency in transient current signals. Next, we employ discrete wavelet transform technique to decompose the preprocessed signals into different frequency ranges of products, and then features are extracted from decomposed detail components. Finally, two decision-level fusion strategies, Bayesian belief fusion and multi-agent fusion, are employed. That is, fault features are classified using several classifiers and generated decisions are fused using a specific fusion algorithm. The proposed approach is evaluated by an experiment of fault diagnosis for induction motors. Experiment results show that excellent diagnosis performance can be obtained.

Development of smart sensors system for machine fault diagnosis

Machine fault diagnosis is a traditional maintenance problem. In the past, the maintenance using tradition sensors is money-cost, which limits wide application in industry. To develop a cost-effective maintenance technique, this paper presents a novel research using smart sensor systems for machine fault diagnosis. In this paper, a smart sensors system is developed which acquires three types of signals involving vibration, current, and flux from induction motors. And then, support vector machine, linear discriminant analysis, k-nearest neighbors, and random forests algorithm are employed as classifiers for fault diagnosis. The parameters of these classifiers are optimized by using cross-validation method. The experimental results show that smart sensor system has the similar performance for applying in intelligent machine fault diagnosis with reduced product cost. Developed smart sensors have feasibility to apply for intelligent fault diagnosis.

A Comparison of Classifier Performance for Fault Diagnosis of Induction Motor using Multi-type Signals

Fault detection and diagnosis is the most important technology in condition-based maintenance (CBM) systems, which typically starts from collecting signatures of running machines by multiple sensors for subsequent accurate analysis. Recently, there has been an increasing requirement of selecting special sensors, which are cheap, robust, easily installed, and good classifiers that have accurate classification, stable performance, and short calculating time. This article carries out a comparative study of various classification algorithms for fault diagnosis of electric motors using different types of signals. The authors evaluate experimentally the relative performances of five classifiers using five types of steady-state signals based on three kinds of performance evaluation strategies: training-test, cross-validation, and similar measure. First, the raw signals are collected and features are extracted from the collected signals. Then, the extracted features are classified using the five classification algorithms. Next, an overall comparison of the five classifiers is described, and experiment results are discussed. Finally, conclusions are summarized and suggestions are offered.

Analysis of Inverter-Fed Squirrel-Cage Induction Motor During Eccentric Rotor Motion Using FEM

Asymmetric electromagnetic force caused by the frictional worn bearing, rotor misalignment, and unbalanced rotor, etc., leads to an asymmetrical operation, vibration, and electromagnetic noise. The need for the detection of these rotor eccentricities has lead to the development of monitoring methods by increasing sensitivity and noise immunity. This paper proposes a diagnosis method for an induction motor driven by a PWM-controlled inverter considering the harmonics in a transient magnetic phenomenon. The effects of asymmetrical whirling motion are studied using the current, vibration spectrum, and back EMF of a search coil. The analysis results of rotor eccentricity could be obtained by comparing the characteristics of motors that have normal and irregular air gaps and verified reliability. Simulation and experimental results can be useful for online fault detection monitoring systems for induction motors.

Experimental evaluation of using the surge PD test as a predictive maintenance tool for monitoring turn insulation quality in random wound AC motor stator windings

Turn insulation degradation is one of the major root causes of stator insulation failure leading to motor breakdown. The surge test is the only test available for testing the integrity of turn insulation; however, it is a high voltage pass/fail test that provides an indication only if an arc is instigated between the turns of weakened turn insulation, and therefore does not provide information regarding remaining lifetime. The surge PD test measures the partial discharge (PD) activity under surge excitation, and is used to date for assuring that voltage source PWM inverter-fed motors (IEC 60034-18-41 type I) are PDfree. In this paper, the potential of using the surge PD test as a predictive maintenance tool for turn insulation quality assessment is evaluated. Under the expectation that increasing PD activity in the voids formed by insulation degradation may be detectable before turn insulation failure, the test is performed periodically under accelerated thermal degradation on 6 windings. It is shown that change in the PD inception voltage under the surge PD test can be clearly observed before any other insulation test indicator. The results suggest that the surge PD test can be used for monitoring the condition of turn insulation for providing an early indication of stator insulation problems without the risk of puncturing turn insulation.

Development of diagnosis algorithm for induction motor using flux sensor

The development of the diagnosis algorithm is carried out for identifying health and faulted conditions in three-phase induction motors. The algorithm consists of feature calculation, feature extraction, and feature classification procedures in sequence. For that, the non-linear feature extraction method with kernel function is used and the classifier of k-nearest neighbors is introduced to decide motor conditions among normal motor, broken rotor bar, short-turn stator windings, and bearing faults. Signal for this algorithm is acquired flux sensor. It is to measure the change of magnetic flux at the air-gap. To get the effective features related with faults, the peak ratio of some frequencies related with line frequency is introduced. This work proposes the efficient diagnosis method for induction motors by developing the powerful algorithm. The calculated features show a good linearity according to faults severities. Moreover, the final results show a good classification rate on motor conditions.

Study on the Transfer Functions for Detecting Windings Displacement of Power Transformers with Impulse Method

The paper investigates three types of transfer function methods for detecting displacements of winding in a model transformer. To acquire these transfer functions, the measuring method of input voltage, current and its response is used in impulse method. The applied impulse voltages had three rising times, which were short rising time (less than 0.6 ㎲), medium rising time (about 0.8 ㎲) and long rising time (about 1 ㎲) in front waves. Every 10 measurements of voltage and current waves were averaged from 50 measurements of voltage and current waves. These transfer functions were tested in normal, 24mm elevated and 48mm elevated windings conditions and were analyzed with correlation coefficients and spectrum deviations. In the analysis, the results depend on the types of transfer functions and the rising times of input voltages.

A method for rotor vibration monitoring of induction motor by air-gap flux detection

This paper presents results of the finite-element (FE) analysis and experiment of air-gap flux variation in induction motor when rotor eccentricity phenomenon occurs. An accurate modeling and analysis of rotor vibration in the motor are made using air-gap flux simulation, and search coils are used for measuring the actual magnetic flux. The simulated vibration system with 4-poles, 380 [V], 7.5 [kW], 1,768 [rpm] induction motor is built, and search coils are designed and inserted under the stator wedge of the motor. The FE analysis results are compared with experiment test results, and it shows that it is an effective method for developing on-line monitoring system on rotor vibration of an induction motor.

MUSIC-based Diagnosis Algorithm for Identifying Broken Rotor Bar Faults in Induction Motors Using Flux Signal

The diagnosis of motor failures using an on-line method has been the aim of many researchers and studies. Several spectral analysis techniques have been developed and are used to facilitate on-line diagnosis methods in industry. This paper discusses the first application of a motor flux spectral analysis to the identification of broken rotor bar (BRB) faults in induction motors using a multiple signal classification (MUSIC) technique as an on-line diagnosis method. The proposed method measures the leakage flux in the radial direction using a radial flux sensor which is designed as a search coil and is installed between stator slots. The MUSIC technique, which requires fewer number of data samples and has a higher detection accuracy than the traditional fast Fourier transform (FFT) method, then calculates the motor load condition and extracts any abnormal signals related to motor failures in order to identify BRB faults. Experimental results clearly demonstrate that the proposed method is a promising candidate for an on-line diagnosis method to detect motor failures.