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Induction motor fault classification via entropy and column correlation features of 2D represented vibration data

100%

Basaran M. , Fidan M.

tom Vol. 23, no. 1

132--142

Due to long-term use under challenging conditions, the sub-elements of induction motors may suffer certain defects over time. Such defects impair the vibration characteristics of the motors in different ways, depending on the type of defect. Therefore, the change in vibration characteristic provides indicators about the fault type and can be used in preventive maintenance strategies to ensure safe operation of the system. In this work, discrete-time vibration data were transformed into 2-dimensional grey-level images and decomposed into individual components by the Wavelet decomposition method. Features based on entropy and column correlation were extracted from these components and used to classify motor faults by using the Support Vector Machine method implemented by using the Sequential Minimal Optimisation algorithm. When the selected classifier is compared with other popular Machine Learning algorithms, it is observed that motor faults are more successfully classified, and these observations are presented in detail with comparative classification performance results.

A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines

75%

Cui J. , Shi G. , Gong C.

2017

tom Vol. 24, nr 4

701--720

Fault detection and location are important and front-end tasks in assuring the reliability of power electronic circuits. In essence, both tasks can be considered as the classification problem. This paper presents a fast fault classification method for power electronic circuits by using the support vector machine (SVM) as a classifier and the wavelet transform as a feature extraction technique. Using one-against-rest SVM and one-against-one SVM are two general approaches to fault classification in power electronic circuits. However, these methods have a high computational complexity, therefore in this design we employ a directed acyclic graph (DAG) SVM to implement the fault classification. The DAG SVM is close to the one-against-one SVM regarding its classification performance, but it is much faster. Moreover, in the presented approach, the DAG SVM is improved by introducing the method of Knearest neighbours to reduce some computations, so that the classification time can be further reduced. A rectifier and an inverter are demonstrated to prove effectiveness of the presented design.

A Fast Classification Method of Faults in Power Electronic Circuits Based on Support Vector Machines

75%

Cui J. , Shi G. , Gong C.

nr 4

Data compression using Prony's method and wavelet transform in power quality monitoring systems

63%

Zygarlicki J. , Mroczka J.

2006

tom Vol. 13, nr 3

325-251

The paper presents a new method for data compression that can be used in power quality monitoring systems. Described algorithm offers high compression ratio and keeps good accuracy of the reconstructed signals. The algorithm uses a modified Prony's method for initial power waveform modeling (basic parameters of estimation) as well as wavelet transform for additional reduction of compression artifacts present in the Prony model of the analysed signal. The combination of the Prony's method and wavelet transform enables obtaining effective compression of real signals observed in monitoring systems. It also gives information about harmonics and transient oscillatory components of power waveforms that can be used for the power quality analysis. Examples of real signals recorded in power monitoring systems are presented in the paper as well as discussion of compression efficiency for different sets of compression artefacts level.

Artykuł opisuje metodę kompresji sygnału elektroenergetycznego rejestrowanego w celu analizy jakości energii. Opisany algorytm umożliwia uzyskanie wysokiego współczynnika kompresji sygnału przy zachowaniu dużej dokładności rekonstrukcji oryginału. Algorytm wykorzystuje zmodyfikowaną metodę Pronego analizy sygnałów w celu wyznaczenia podstawowego modelu sygnału oraz dodatkowo dla zmniejszenia błędów rekonstrukcji stosuje takie dyskretną analizę falkową. Połączenie tych metod umożliwia efektywną kompresję rzeczywistych sygnałów z urządzeń pomiarowych oraz dodatkowo realizuje analizę składowych harmonicznych i zdarzeń o charakterze oscylacyjnym przebiegów, co może ułatwić późniejszą analizę zjawisk zachodzących w monitorowanych obiektach. Artykuł zawiera analizę przykładowych rzeczywistych sygnałów z systemu monitorującego, pokazuje analizę wydajności algorytmu dla różnych założonych błędów kompresji oraz opisuje błędy generowane przez metodę.