Fault Diagnosis of Rotating Machines in Offshore Plant Based on Morlet Wavelet Transform

Burak OZTURK,KwangSik KIM,YooIl KIM,JangHyun LEE (사)한국CDE학회 2014 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2014 No.2

The condition monitoring consists of a selection of measurable parameters such as vibration signals which correlate with the health or condition of a machine, and an interpretation of the collected data to determine the machinery fault existence and identify specific components (e.g. gear set, bearings) in the machine that are degrading. Many vibration and unbalanced signals of fault rotating machines in offshore plant have complex time-frequency characteristics. As a timefrequency analysis, wavelet transform is useful for locating transient events, discontinuities and patterns of these faults in non-stationary vibration data. The wavelet transformation can be an efficient way to detect the fault from the signal since the signal always includes the fault information about the rotating and reciprocating equipment inside. This paper introduces the faults diagnosis for offshore rotating machines by the application of Morlet wavelet transformation. Special attention is given to the vibratory and unbalanced faults, namely, rotating unbalance and resonance found in the rotating machine installed in offshore plant. Vibration signals recorded from accelerometer are processed by Morlet wavelet so that the both bearing and misalignment failure diagnosis. Thereafter, time-frequency contour map is introduced into fault diagnosis. The timefrequency contour map can easily show the power distribution of signal in time and frequency domain. Moreover, it is a good way to identify the faults involving a breakdown change. Several typical faults of rotating machines are detected by the time-frequency contour map obtained by the Morlet wavelet. The simulative results show that time-frequency contour map have the capabilities to identify the faults. This method also appeared to be an effective tool to diagnose and to discriminate the different types of machinery faults based on the unique pattern of the wavelet contours. This study shows that the proposed wavelet analysis method is promising to reveal machinery faults at early stage as compared to vibration spectrum analysis. A case study about the implementation of the continuous wavelet transform to the compressor fault diagnosis will be introduced. It is shown that the wavelet transform is a promising condition assessment of the compressors installed on ship and offshore.

Intuitionistic Fuzzy Expert System based Fault Diagnosis using Dissolved Gas Analysis for Power Transformer

Geetha Mani,Jovitha Jerome 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.6

In transformer fault diagnosis, dissolved gas analysis (DGA) is been widely employed for a long period and numerous methods have been innovated to interpret its results. Still in some cases it fails to identify the corresponding faults. Due to the limitation of training data and non-linearity, the estimation of key-gas ratio in the transformer oil becomes more complicated. This paper presents Intuitionistic Fuzzy expert System (IFS) to diagnose several faults in a transformer. This revised approach is well suitable to diagnosis the transformer faults and the corresponding action to be taken. The proposed method is applied to an independent data of different power transformers and various case studies of historic trends of transformer units. It has been proved to be a very advantageous tool for transformer diagnosis and upkeep planning. This method has been successfully used to identify the type of fault developing within a transformer even if there is conflict in the results of AI technique applied to DGA data.

Comparative Study of Three Fault Diagnostic Methods for Three Phase Inverter with Induction Motor

Furqan Asghar,Muhammad Talha,Sung Ho Kim 한국지능시스템학회 2017 INTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGE Vol.17 No.4

In recent times, inverters are considered as the basic building block in an electrical drive system used widely in many industrial drive applications. However, the reliability of these inverters is mainly affected by the failure of power electronic switches. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduce overall efficiency. In this paper, comparative study of three different fault detection and diagnosis systems for three phase inverter is presented. The basic purpose of these fault detection and diagnosis systems is to detect single or multiple faults efficiently. These techniques rely on the neural network for fault detection and diagnosis by using Clarke transformed two-dimensional features extraction, three-dimensional features extraction and features extraction using discrete wavelet transform (DWT) with a different number of features in each technique. Several features are extracted using different mechanisms and used in the neural network as input for fault detection and diagnosis. Furthermore, a simulation study is carried out to analyze the fault detection and diagnosis response of these techniques. Also, a comparative study has been performed by considering fault detection time and accuracy. Comparison results prove the supremacy of three-dimensional feature extraction technique over other two techniques as it can detect and diagnose single, double and triple faults in a single cycle with high accuracy as compared to other two techniques multi-cycles detection.

유입변압기 고장분류를 위한 PNN 기반 Rogers 진단기법 개발

임재윤(Jae-Yoon Lim),이대종(Dae-Jong Lee),지평식(Pyeong-Shik Ji) 대한전기학회 2016 전기학회논문지 P Vol.65 No.4

Stability and reliability of a power system in many respects depend on the condition of power transformers. Essential devices as power transformers are in a transmission and distribution system. Being one of the most expensive and important elements, a power transformer is a highly essential element, whose failures and damage may cause the outage of a power system. To detect the power transformer faults, dissolved gas analysis (DGA) is a widely-used method because of its high sensitivity to small amount of electrical faults. Among the various diagnosis methods, Rogers diagonsis method has been widely used in transformer in service. But this method cannot offer accurate diagnosis for all the faults. This paper proposes a fault diagnosis method of oil-filled power transformers using PNN(Probability Neural Network) based Rogers diagnosis method. The test result show better performance than conventional Rogers diagnosis method.

Two-level fault diagnosis RBF networks for auto-transformer rectifier units using multi-source features

Lin, Yi,Ge, Hongjuan,Chen, Shuwen,Pecht, Michael The Korean Institute of Power Electronics 2020 JOURNAL OF POWER ELECTRONICS Vol.20 No.3

The auto-transformer rectifier unit (ATRU) is one of the most widely used avionic secondary power supplies. Timely fault identification and location of the ATRU is significant in terms of system reliability. A two-level fault diagnosis method for the ATRU using multi-source features (MSF) is proposed in this paper. Based on the topology of the ATRU, three key electrical signals are selected and analyzed to extract appropriate features for fault diagnosis. Mathematic expressions and simulation results of the feature signals under different fault modes are presented in the paper. Therefore, a unique MSF system is developed and a two-level fault diagnosis method based on radial basis function network groups is proposed. On the first level, the overall fault set is classified into three subsets and then on the second level, three radial basis function neural networks are constructed and trained to realize accurate fault localization. To verify the diagnosis performance of the proposed method, several comparative tests are implemented on a 12-pulse ATRU system, which shows that this method has a lower computational cost, better diagnostic accuracy and increased stability when compared with alternative methods.

Intuitionistic Fuzzy Expert System based Fault Diagnosis using Dissolved Gas Analysis for Power Transformer

Mani, Geetha,Jerome, Jovitha The Korean Institute of Electrical Engineers 2014 Journal of Electrical Engineering & Technology Vol.9 No.6

In transformer fault diagnosis, dissolved gas analysis (DGA) is been widely employed for a long period and numerous methods have been innovated to interpret its results. Still in some cases it fails to identify the corresponding faults. Due to the limitation of training data and non-linearity, the estimation of key-gas ratio in the transformer oil becomes more complicated. This paper presents Intuitionistic Fuzzy expert System (IFS) to diagnose several faults in a transformer. This revised approach is well suitable to diagnosis the transformer faults and the corresponding action to be taken. The proposed method is applied to an independent data of different power transformers and various case studies of historic trends of transformer units. It has been proved to be a very advantageous tool for transformer diagnosis and upkeep planning. This method has been successfully used to identify the type of fault developing within a transformer even if there is conflict in the results of AI technique applied to DGA data.

A New Support Vector Machine Model Based on Improved Imperialist Competitive Algorithm for Fault Diagnosis of Oil-immersed Transformers

Zhang, Yiyi,Wei, Hua,Liao, Ruijin,Wang, Youyuan,Yang, Lijun,Yan, Chunyu The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.2

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

A New Support Vector Machine Model Based on Improved Imperialist Competitive Algorithm for Fault Diagnosis of Oil-immersed Transformers

Yiyi Zhang,Hua Wei,Ruijin Liao,Youyuan Wang,Lijun Yang,Chunyu Yan 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.2

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

Expert System for Fault Diagnosis of Transformer

Kim, Jae-Chul,Jeon, Hee-Jong,Kong, Seong-Gon,Yoon, Yong-Han,Choi, Do-Hyuk,Jeon, Young-Jae Korean Institute of Intelligent Systems 1997 한국지능시스템학회논문지 Vol.7 No.1

This paper presents hybrid expert system for diagnosis of electric power transformer faults. The expert system diagnose and detect faults in oil-filled power transformers based on dissolved gas analysis. As the preprocessing stage, fuzzy information theory is used to manage the uncertainty in transformer fault diagnosis using dissolved gas analysis. The Kohonen neural network takes the interim results by applying fuzzy informations theory as inputs, and performs the transformer fault diagnosis. The Proposed system tested gas records of power transformers from Korea Electric Power Corporation to verify the diagnosis performance of transformer faults.

Research on a fault diagnosis method of rolling bearings using variation mode decomposition and deep belief network

Huimin Zhao,Hailong Liu,Junjie Xu,Chen Guo,Wu Deng 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.9

The working conditions of rolling bearings during the running change in real time. Aiming at the problem of fault diagnosis of rolling bearing under complex working conditions, a new fault diagnosis (VHDBN) method based on variation mode decomposition (VMD), Hilbert transform (HT) and deep belief network (DBN) is proposed in this paper. Firstly, the proposed fault diagnosis method performs the VMD decomposition for the time domain signal in order to obtain a series of intrinsic mode functions (IMFs), and Hilbert envelope spectrum is obtained by Hilbert transform. The Hilbert envelope spectrum is used to construct the feature matrix, which is used as an input of the DBN network in order to obtain a fault diagnosis model. In order to test and verify the effectiveness of the proposed fault diagnosis method, the experimental data of rolling bearings under variable load is used in here. The experimental results show that the VMD-Hilbert envelope spectrum can better reflect the fault characteristics than the time domain spectrum, and the proposed fault diagnosis method under variable load has higher recognition accuracy than other comparison methods.