• Journal of the Korea Society of Computer and Information
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• v.19 no.11
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• pp.17-24
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• 2014

This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1055-1056
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• 2007

Asymmetric electro-magnetic force caused by the frictional worn bearing, rotor misalignment and unbalanced rotor etc. generates an asymmetrical operation, vibration and electro-magnetic noise. The need for detection of these rotor eccentricities has pushed the development of monitoring methods with increasing sensitivity and noise immunity. This paper is proposed the analysis method of the squirrel-cage induction motor driven by IGBT inverter using finite element method (FEM) and subroutine. The effect of the unbalanced magnetic pull in the inverter-fed induction motor which is in asymmetrical whirling motion is presented. The analysis results of rotor eccentricity could compare with motors which have been made normal air-gap motor and irregular air-gap motor and verify reliability. The simulation and experiment results can be useful for on-line faults detection monitoring system of induction motors.

• Journal of Drive and Control
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• v.16 no.3
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• pp.23-32
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• 2019

This study proposed a fault tolerant control algorithm for electro-hydraulic brake systems where permanent magnet synchronous motor (PMSM) drive is adopted to boost the braking pressure. To cope with motor position sensor faults in the PMSM drive, a braking pressure controller based on an open-loop speed control method for the PMSM was proposed. The magnitude of the current vector was determined from the target braking pressure, and motor rotational speed was derived from the pressure control error to build up the braking pressure. The position offset of the pump piston resulting from a leak in the hydraulic system is also compensated for using the open-loop speed control by moving the piston backward until it is blocked at the end of stroke position. The performance and stability of the proposed controller were experimentally verified. According to the results, the control algorithm can be utilized as an effective means of degraded control for electro-hydraulic brake systems in the case that a motor position sensor fault occurs.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.182-184
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• 2006

A down-scaled simulator is developed to simulate typical faults in induction motor such as short-turn stator winding, broken rotor bar, dynamic and static air-gap eccentricity, bearing trouble, and mechanical unbalance. The simulator is used as an initial builder to develop design algorithm for real-time faults detecting system by processing an abnormal signal and characteristics in each fault.

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.929-940
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• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.1
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• pp.68-79
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• 1998

In this paper, we propose a model-based FDI(fault detetion and isolation) algorithm to detect and isolate fault in a linear system. The proposed algorithm is gased on an HFC(hydrid fault classifier) which consists of an FCART2(fault classifier by ART2 neural network) and an FCFM(fault classifier by fault models) which operate in parallel to isolate faults. The proposed algorithm is functionally composed of three main parts-parameter estimation, fault detection, and isolation. When a change in the system occurs, the estimated parameters go through a transition zone in which errors between the system output and the stimated output and the estimated output cross a predetermined thrseshold, and in this zone the estimated parameters are tranferred to the FCART2 for fault isolation. On the other hand, once a fault in the system is detected, the FCFM statistically isolates the fault by using the error between ach fault model out put and the system output. From the computer simulation resutls, it is verified that the proposed model-based FDI algorithm can be performed successfully to detect and isolate faults in a position control system of a DC motor.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.848-849
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• 2008

This paper presents a simulation model to analyze effects of electrical faults for the Permanent Magnet Synchronous Motor(PMSM) drive system. The major fault modes of system are investigated and an intuitive system model is developed using MATLAB/Simulink. The developed model provides useful environments to inject and remove the various faults. Simulation results show the dynamic performances of system during the transient state from normal to fault, and those will be a great help to make a system more reliable.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.170-177
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• 2014

This paper presents a new method of classification of the induction machine faults using Time Frequency Representation, Particle Swarm Optimization and artificial neural network. The essence of the feature extraction is to project from faulty machine to a low size signal time-frequency representation (TFR), which is deliberately designed for maximizing the separability between classes, a distinct TFR is designed for each class. The feature vectors size is optimized using Particle Swarm Optimization method (PSO). The classifier is designed using an artificial neural network. This method allows an accurate classification independently of load level. The introduction of the PSO in the classification procedure has given good results using the reduced size of the feature vectors obtained by the optimization process. These results are validated on a 5.5-kW induction motor test bench.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.410-413
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• 1998

A passive approach for enhancing fault detection and isolation performance of multiple observer based fault detection isolation schemes(FDIS) is proposed. The FDIS has a hierarchical framework to perform detection and isolation of faults of interest, and diagnosis of process faults. The decision unit comprises of a rule base and fuzzy inference engine and removes some difficulties of conventional decision unit which includes crisp logic and threshold values. Emphasis is placed on the design and evaluation methods of the diagnostic rule base. The suggested scheme is applied for the FDIS design for a DC motor driven centrifugal pump system.

• Proceedings of the IEEK Conference
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• summer
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• pp.371-375
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• 2004

Fault diagnosis is one of the most studied problems in process engineering. Recently, great research interest has been devoted to approaches that use classification methods to detect faults. This paper presents an application of a newly developed classification method - support vector machines - for fault diagnosis in an industrial case. A real set of operation data of a motor pump was used to train and test the support vector machines. The experiment results show that the support vector machines give higher correct detection rate of faults in comparison to rule-based diagnostics. In addition, the studied method can work with fewer training instances, what is important for online diagnostics.