• Title/Summary/Keyword: Machine Fault Diagnosis

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Fault diagnosis system of the short circuit conditions in windings for synchronous generator (동기발전기 권선단락사고 고장진단 시스템)

  • Jang, Nakwon;Lee, SungHwan
    • Journal of Advanced Marine Engineering and Technology
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    • v.37 no.5
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    • pp.520-526
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    • 2013
  • As the increasing of capacity and technology of power facilities, rotating machines are getting higher at capacity and voltage scale. Thus the monitoring and diagnosis of generators for fault detection has attracted intensive interest. In this paper, we developed fault diagnosis system for monitoring the fault operations in bad power systems. In order to verify the performance of this fault diagnosis system, we made the small scaled testing system which has the same winding structure of the real synchronous generator. The magnetic flux patterns in air-gap of a small-scale generator under various fault states as well as a normal state are tested by hall sensors and the fault detection system.

An Availability of Low Cost Sensors for Machine Fault Diagnosis

  • SON, JONG-DUK
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2012.10a
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    • pp.394-399
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    • 2012
  • In recent years, MEMS sensors show huge attraction in machine condition monitoring, which have advantages in power, size, cost, mobility and flexibility. They can integrate with smart sensors and MEMS sensors are batch product. So the prices are cheap. And the suitability of it for condition monitoring is researched by experimental study. This paper presents a comparative study and performance test of classification of MEMS sensors in target machine fault classification by 3 intelligent classifiers. We attempt to signal validation of MEMS sensor accuracy and reliability and performance comparisons of classifiers are conducted. MEMS accelerometer and MEMS current sensors are employed for experiment test. In addition, a simple feature extraction and cross validation methods were applied to make sure MEMS sensors availabilities. The result of application is good for using fault classification.

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Engine Fault Diagnosis Using Sound Source Analysis Based on Hidden Markov Model (HMM기반 소음분석에 의한 엔진고장 진단기법)

  • Le, Tran Su;Lee, Jong-Soo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39A no.5
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    • pp.244-250
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    • 2014
  • The Most Serious Engine Faults Are Those That Occur Within The Engine. Traditional Engine Fault Diagnosis Is Highly Dependent On The Engineer'S Technical Skills And Has A High Failure Rate. Neural Networks And Support Vector Machine Were Proposed For Use In A Diagnosis Model. In This Paper, Noisy Sound From Faulty Engines Was Represented By The Mel Frequency Cepstrum Coefficients, Zero Crossing Rate, Mean Square And Fundamental Frequency Features, Are Used In The Hidden Markov Model For Diagnosis. Our Experimental Results Indicate That The Proposed Method Performs The Diagnosis With A High Accuracy Rate Of About 98% For All Eight Fault Types.

Fault Detection and Diagnosis based on Fuzzy Algorithm in the Injection Molding Machine (사출 성형기 Barrel 온도의 실시간 데이터베이스화와 퍼지알고리즘 기반의 고장 검출 및 진단)

  • 배성준;김훈모
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2002.12a
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    • pp.463-467
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    • 2002
  • In this paper, we construct the fault detection and diagnosis system based on fuzzy algorithm in the injection molding machine. Data of operating injection molding machine are acquired in database in order to raise the reliability of detection and diagnosis.

Computer-Aided Vibration Signal Processing and Fault Monitoring System of Electrical-Fan Motors (컴퓨터를 이용한 선풍기모터의 진동신호처리 및 이상진단에 관한 연구)

  • Sin, Jung-Ho;Hwang, Gi-Hyeon;Choe, Yeong-Hyu;Park, Ju-Hyeok
    • 한국기계연구소 소보
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    • s.17
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    • pp.61-68
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    • 1987
  • The main objective of this paper is to develop the computer-aided vibrational signal processing and monitoring system of rotating machinery. This system has an automatic data acquisition capability and analyze for machine fault diagnosis. By spectrum analysis, machine’s failure can be identified. The monitoring system enables diagnosis of the fault in rotating machinery. In this study, the conventional electrical fans are selected as a model case. The date processing and fault monitoring system proposed here can be applied to the automation of the inspection process in assembling motor-shaft systems. The automatic inspection can enhance the product quality and keep it stable. Since the proposed system is developed for personal computers, it might be cheap in cost and easy in installation.

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Aiding the operator during novel fault diagnosis

  • Yoon, Wan-C.;Hammer, John-M.
    • Journal of the Ergonomics Society of Korea
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    • v.6 no.1
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    • pp.9-24
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    • 1987
  • The design and philosophy are presented for an intelligent aid for a hyman operator who must diagnose a novel fault in a physical system. A novel fault is defined as one that the operator has not experienced in either real system operation or training. When the operator must diagnose a novel fault, deep reasoning about the behavior of the system components is required. To aid the human operator in this situation, four aiding approaches which provide useful information are proposed. The aiding information is generated by a qualitative, component-level model of the physical system. Both the aid and the human are able to reason causally about the system in a cooperative search for a diagnosis. The aiding features were designed to help the hyman's use of his/her mental model in predicting the normal system behavior, integrating the observations into the actual system behavior, or finding discrepancies between the two. The aid can also have direct access to the operator's hypotheses and run a hypothetical system model. The different aiding approaches will be evaluated by a series of experiments.

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A Study on a Fault Detection and Isolation Method of Nonlinear Systems using SVM and Neural Network (SVM과 신경회로망을 이용한 비선형시스템의 고장감지와 분류방법 연구)

  • Lee, In-Soo;Cho, Jung-Hwan;Seo, Hae-Moon;Nam, Yoon-Seok
    • Journal of Institute of Control, Robotics and Systems
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    • v.18 no.6
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    • pp.540-545
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    • 2012
  • In this paper, we propose a fault diagnosis method using artificial neural network and SVM (Support Vector Machine) to detect and isolate faults in the nonlinear systems. The proposed algorithm consists of two main parts: fault detection through threshold testing using a artificial neural network and fault isolation by SVM fault classifier. In the proposed method a fault is detected when the errors between the actual system output and the artificial neural network nominal system output cross a predetermined threshold. Once a fault in the nonlinear system is detected the SVM fault classifier isolates the fault. The computer simulation results demonstrate the effectiveness of the proposed SVM and artificial neural network based fault diagnosis method.

Diagnosis of a Pump by Frequency Analysis of Operation Sound (펌프의 작동음 주파수 분석에 의한 진단)

  • Lee Sin-Young
    • Transactions of the Korean Society of Machine Tool Engineers
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    • v.13 no.5
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    • pp.81-86
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    • 2004
  • A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method for a detection of machine malfuction or fault diagnosis.

Fault Diagnosis of Induction Motor by Hierarchical Classifier (계층구조의 분류기에 의한 유도전동기 고장진단)

  • Lee, Dae-Jong;Song, Chang-Kyu;Lee, Jae-Kyung;Chun, Myung-Guen
    • Journal of Institute of Control, Robotics and Systems
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    • v.13 no.6
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    • pp.513-518
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    • 2007
  • In this paper, we propose a fault diagnosis scheme tor induction motor by adopting a hierarchical classifier consisting of k-Nearest Neighbors(k-NN) and Support Vector Machine(SVM). First, some motor conditions are classified by a simple k-NN classifier in advance. And then, more complicated classes are distinguished by SVM. To obtain the normal and fault data, we established an experimental unit with induction motor system and data acquisition module. Feature extraction is performed by Principal Component Analysis(PCA). To show its effectiveness, the proposed fault diagnostic system has been intensively tested with various data acquired under the different electrical and mechanical faults with varying load.

A Fault Diagnostic Method for Position Sensor of Switched Reluctance Wind Generator

  • Wang, Chao;Liu, Xiao;Liu, Hui;Chen, Zhe
    • Journal of Electrical Engineering and Technology
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    • v.11 no.1
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    • pp.29-37
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    • 2016
  • Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.