• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.16 no.4 s.109
• /
• pp.404-413
• /
• 2006

Induction motors are the workhorse of our industry because of their versatility and robustness. The diagnosis of mechanical load and power transmission system failures is usually carried out through mechanical signals such as vibration signatures, acoustic emissions, motor speed envelope. The motor faults including mechanical rotor imbalances, broken rotor bar, bearing failure and eccentricities problems are reflected in electric, electromagnetic and mechanical quantities. The recent research has been directed toward electrical monitoring of the motor with emphasis on inspecting the stator current of the motor, The stator current spectrum has been widely used for fault detection in induction motor systems. The motor current signature analysis is the useful technique to assess machine electrical condition. This paper describes the motor condition detected by the current signatures Paralleled with vibration signatures analysis of induction motors with the roller bearing and the journal bearing type for large vertical pumps in power plant as examples to discuss for motor fault detection and diagnosis.

• Journal of Power Electronics
• /
• v.11 no.2
• /
• pp.163-172
• /
• 2011

Oxidation models of a rotor bar and end ring segment in an induction motor are presented to simulate the behavior of an induction machine working with oxidized rotor parts which are modeled as rotor faults in progress. The leakage inductance and resistance of the rotor parts arc different from normal values because of the oxidation process. The impedance variations modify the current density and magnetic flux which pass through the oxidized parts. Consequently, it causes the rotor asymmetry which induces abnormal harmonics in the stator current spectra of the faulty machine. The leakage inductances of the oxidation models are derived by the Ampere's law. Using the proposed oxidation models, the rotor bar and end ring faults in progress can be modeled and simulated with the motor current signature analysis (MCSA). In addition, the oxidation process of the rotor bar and end ring segment can motivate the rotor asymmetry, which is induced by electromagnetic imbalances, and it is one of the major motor faults. Results of simulations and experiments are compared to each other to verify the accuracy of the proposed models. Experiments are achieved using 3.7 kW, 3-phase, and squirrel cage induction motors with a motor drive inverter.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.24 no.8
• /
• pp.55-61
• /
• 2010

In this paper, we proposed selective extraction method of frequency information and PCA-ELM based diagnosis system for three-phase induction motors. As the first step for diagnosis procedure, DFT is performed to transform the acquired current signal into frequency domain. And then, frequency components are selected according to discriminate order calculated by variance As the next step, feature extraction is performed by principal component analysis (PCA). Finally, we used the classifier based on Extreme Learning Machine (ELM) with fast learning procedure. To show the effectiveness, the proposed diagnostic system has been intensively tested with the various data acquired under different electrical and mechanical faults with varying load.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.16 no.1 s.106
• /
• pp.27-33
• /
• 2006

Fault diagnosis and condition monitoring for rotating machines are important for efficiency and accident prevention. The process of fault diagnosis is to extract the feature of signals and to classify each state. Conventionally, fault diagnosis has been developed by combining signal processing techniques for spectral analysis and pattern recognition, however these methods are not able to diagnose correctly for certain rotating machines and some faulty phenomena. In this paper, we add a minimum detection error algorithm to the previous method to reduce detection error rate. Vibration signals of the induction motor are measured and divided into subband signals. Each subband signal is processed to obtain the RMS, standard deviation and the statistic data for constructing the feature extraction vectors. We make a study of the fault diagnosis system that the feature extraction vectors are applied to K-means clustering algorithm and minimum detection error algorithm.

• Journal of the Korea Society of Computer and Information
• /
• v.18 no.12
• /
• pp.11-19
• /
• 2013

This paper proposes an algorithm using vibration signals and texture analysis for mechanical fault diagnosis of an induction motor. We analyze characteristics of contrast and pattern of an image converted from vibration signal and extract three texture features using gray-level co-occurrence model(GLCM). Then, the extracted features are used as inputs of a multi-level support vector machine(MLSVM) which utilizes the radial basis function(RBF) kernel function to classify each fault type. In addition, we evaluate the classification performance with varying the parameter from 0.3 to 1.0 for the RBF kernel function of MLSVM, and the proposed algorithm achieved 100% classification accuracy with the parameter of the RBF from 0.3 to 1.0. Moreover, the proposed algorithm achieved about 98% classification accuracy with 15dB and 20dB noise inserted vibration signals.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2013.10a
• /
• pp.338-345
• /
• 2013

Induction motors play an increasing importance in industrial manufacturing. Therefore, the state monitoring systems also have been considering as the key in dealing with their negative effect by absorbing faulty symptoms in motors. There are numerous proposed systems in literature, in which, several kinds of signals are utilized as the input. To solve the multiple faults problem of induction motors, like the proposed system, the vibration signals is good candidate. In this study, a new signal processing scheme was utilized, which transforms the time domain vibration signal into the spatial domain as an image. Then the spatial features of converted image then have been extracted by applying the dominant neighbourhood structure (DNS) algorithm. In addition, these feature vectors were evaluated to obtain the fruitful dimensions, which support to discriminate between states of motors. Because of reliability, the conventional one-against-all (OAA) multi-class support vector machines (MCSVM) have been utilized in the proposed system as classifier module. Even though examined in severity levels of signal-to-noise ratio (SNR), up to 15dB, the proposed system still reliable in term of two criteria: true positive (TF) and false positive (FP). Furthermore, it also offers better performance than five state-of-the-art systems.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1157-1165
• /
• 2019

This paper discussed the results of the development and application of the machine learning algorithm to the induction motor for the preventive diagnostic system using current and magnetic flux signals. The optimal 29 features were extracted for identifying faulted types of induction motor. In particular, any load rate was derived using the tendency of the difference value from the center of the 7th harmonic frequency to the sideband of the current signal, and the corresponding classification accuracy showed about 84.6% by the KPCA feature reduction technique and the k-NN determination algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2005.11a
• /
• pp.400-405
• /
• 2005

The diagnosis of mechanical load and of power transmission system failures is usually carried out through mechanical signals such as vibration signals, acoustic emissions, motor speed envelope. If the mechanical load comes from an electrical machine the mechanical failures could be detected previously. Mechanical rotor imbalances and rotor eccentricities are reflected in electric, electromagnetic and mechanical quantities. Therefore, many surveillance schemes apply to the Fourier spectrum of a line current in order to monitor the motor condition. Due to the interaction of the currents and voltages, both these current harmonics are also reflected by a single harmonic component in the frequency spectrum of the electric power. Motor Current Signature Analysis is the usuful technique to assess machine electrical condition.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.5
• /
• pp.1116-1121
• /
• 2013

Mechanical imbalances are common mechanical faults in induction motors. Vibration monitoring techniques have been widely used for the diagnosis of mechanical faults in induction motors, but electrical detection methods have been preferred in recent years. For many years, researchers have concentrated on the Motor Current Signature Analysis (MCSA). This paper examines the effect of mechanical imbalances to induction machine electrical parameters. Instantaneous Power Signature Analysis (IPSA) technique used to detect these faults. In the paper, a full analysis of the proposed technique is presented, and experimental results for healthy and faulty motors have been shown and discussed.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.4
• /
• pp.408-413
• /
• 2014

In this paper, we propose a fault diagnosis method based on Park's Vector Approach using the Euler's theorem. If we interpreted it as Euler's theorem, it is possible to easily find the phase angle difference between the healthy condition and the fault condition. And, we analyzed the variation of the phase angle and performed the diagnostic method of the induction motor using feature vectors that were obtained by using a Fourier transform. The analysis of time and speed variation of the motor was performed and, as a result, we could find more soft variations than rough variations. In particular, the analysis of the distortion through each phase shows that two-turn and four-turn shorted motors are linearly separable. In this experiment, we know that the maximum breakdown threshold value for determining steady-state fault detection is 49.0788. Simulation and experimental results show the more detectable than conventional method.