Rancang Bangun Alat Pendeteksi Penyebab Kerusakan Motor Sebagai Penggerak Mobil Listrik Menggunakan Current Analysis Dengan Artificial Neural Network

Seiring dengan kemajuan zaman, transportasi selalu mengalami perkembangan salah satunya yaitu mobil listrik. Motor induksi merupakan salah satu jenis penggerak yang paling umum untuk digunakan baik dalam kebutuhan industri, rumahan, sarana transportasi, dan lainnya. Motor jenis ini banyak digunakan karena memliki konstruksi yang kokoh, harga murah, pengoperasian mudah dan kecepatan motor relatif konstan. Saat ini, telah banyak alat-alat proteksi untuk motor induksi. Tetapi alat-alat tersebut hanya melindungi bagian instalasi saja. Maka dari itu, diciptakan deteksi dini kerusakan motor. Konsep kerja alat ini yaitu dengan mendeteksi arus yang dihasilkan oleh motor induksi 3 fasa 1,2 KW. Arus tersebut memiliki karakteristik gelombang arus yang nantinya diolah pada mikrokontroler yang dilakukan proses fast fourier transform (FFT) terlebih dahulu agar bisa dilihat spektrumnya, lalu di dihitung (∆dB) selisih frekuensi puncak dengan lower side band (gelombang sebelum puncak tertinggi). dB tersebut yang digunakan untuk karakteristik setiap kondisi dari motornya. Selanjutnya membandingkan hasil arus dengan data yang telah diinputkan pada data base mikrokontroler menggunakan metode artificial neural network (ANN). Kemudian menghasilkan analisa kondisi motor induksi. Pada pengujian ANN, perbandingan target dan output memiliki error sangat kecil yaitu rata-rata error 0,000667826

A randomized neural network for data streams

© 2017 IEEE. Randomized neural network (RNN) is a highly feasible solution in the era of big data because it offers a simple and fast working principle in processing dynamic and evolving data streams. This paper proposes a novel RNN, namely recurrent type-2 random vector functional link network (RT2McRVFLN), which provides a highly scalable solution for data streams in a strictly online and integrated framework. It is built upon the psychologically inspired concept of metacognitive learning, which covers three basic components of human learning: what-to-learn, how-to-learn, and when-to-learn. The what-to-learn selects important samples on the fly with the use of online active learning scenario, which renders our algorithm an online semi-supervised algorithm. The how-to-learn process combines an open structure of evolving concept and a randomized learning algorithm of random vector functional link network (RVFLN). The efficacy of the RT2McRVFLN has been numerically validated through two real-world case studies and comparisons with its counterparts, which arrive at a conclusive finding that our algorithm delivers a tradeoff between accuracy and simplicity

Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection

Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs

Research on vibro-acoustic characteristics of the aluminum motor shell based on GA-BP neural network and boundary element method

Firstly, the paper established a finite element model for a steel motor shell and computed related modals, vibration velocities, stress and strain respectively. Computational results show that the flange and end shield of the motor shell had the maximum vibration velocities and strain because these locations lacked the reinforcing ribs, while the maximum stress was mainly at joints between different structures. Secondly, the steel material was replaced by the aluminum alloy. Mechanical parameters of the motor shell were recomputed and compared with those of the steel structure. Results show that modal frequency on each order increased, which is good for avoiding the structural resonance. In addition, the maximum stress of the structure decreased by 4.4 MPa, and the maximum strain decreased by 0.27 mm, which could effectively improve the fatigue characteristics of the motor shell under long-term excitation. Then, the boundary element method was used to compute radiation noises of the motor shell in far field, where the radiation noise presented an obvious directivity. Finally, the paper proposed a GA-BP neural network model to predict the radiation noise of the motor and compared the prediction results with the boundary element. In the whole analyzed frequency band, the maximum difference between the neural network prediction and the real values did not exceed 5 dB, indicating that it is feasible to predict radiation noises of the motor by the neural network. Additionally, experiments were also conducted and compared with two kinds of numerical methods. Methods proposed in this paper provide some references for realizing the rapid noise reduction and light weight of motors

Modeling and fault diagnosis of broken rotor bar faults in induction motors

Due to vast industrial applications, induction motors are often referred to as the “workhorse” of the industry. To detect incipient faults and improve reliability, condition monitoring and fault diagnosis of induction motors are very important. In this thesis, the focus is to model and detect broken rotor bar (BRB) faults in induction motors through the finite element analysis and machine learning approach. The most successfully deployed method for the BRB fault detection is Motor Current Signature Analysis (MSCA) due to its non-invasive, easy to implement, lower cost, reliable and effective nature. However, MSCA has its own limitations. To overcome such limitations, fault diagnosis using machine learning attracts more research interests lately. Feature selection is an important part of machine learning techniques. The main contributions of the thesis include: 1) model a healthy motor and a motor with different number of BRBs using finite element analysis software ANSYS; 2) analyze BRB faults of induction motors using various spectral analysis algorithms (parametric and non-parametric) by processing stator current signals obtained from the finite element analysis; 3) conduct feature selection and classification of BRB faults using support vector machine (SVM) and artificial neural network (ANN); 4) analyze neighbouring and spaced BRB faults using Burg and Welch PSD analysis

Condition Monitoring of Induction Motors Based on Stator Currents Demodulation

International audienceOver the past several decades, induction machine condition monitoring have received increasing attention from researchers and engineers. Several induction machine faults detection techniques have been proposed that are based on vibration, temperature, and currents/power monitoring, etc. Motor current signature analysis is a cost-effective method, which has been widely investigated. Specifically, it has been demonstrated that mechanical and electrical induction machine faults can be effectively diagnosed using stator currents demodulation. Therefore, this paper proposes to investigate the use of demodulation techniques for bearing faults detection and diagnosis based on stator currents analysis. If stator currents are assumed to be mono-component signals, the demodulation techniques include the synchronous demodulator, the Hilbert transform, the Teager energy operator, the Concordia transform, the maximum likelihood approach and the principal component analysis. For a multi-component signal, further preprocessing techniques are required such as the Empirical Mode Decomposition (EMD) or the Ensemble EMD (EEMD). The studied demodulation techniques are demonstrated for bearing faults diagnosis using simulation data, issued from a coupled electromagnetic circuits approach-based simulation tool, and experiments on a 0.75kW induction machine test bed

An ensemble of intelligent water drop algorithm for feature selection optimization problem

Master River Multiple Creeks Intelligent Water Drops (MRMC-IWD) is an ensemble model of the intelligent water drop, whereby a divide-and-conquer strategy is utilized to improve the search process. In this paper, the potential of the MRMC-IWD using real-world optimization problems related to feature selection and classification tasks is assessed. An experimental study on a number of publicly available benchmark data sets and two real-world problems, namely human motion detection and motor fault detection, are conducted. Comparative studies pertaining to the features reduction and classification accuracies using different evaluation techniques (consistency-based, CFS, and FRFS) and classifiers (i.e., C4.5, VQNN, and SVM) are conducted. The results ascertain the effectiveness of the MRMC-IWD in improving the performance of the original IWD algorithm as well as undertaking real-world optimization problems

Study of the Thermal Behavior of a Three-phase Induction Motor under Fault Conditions

Electric motors play an important role in the industry, because nowadays almost everything in the industry works with the auxiliary of them, either for low or high power ratings. It is possible to divide the electric motors in induction motors and synchronous motors, however the most used in the industry are the induction motors. So, it is very important to monitor its behavior throughout the time. Due to their working conditions, which sometimes can be very adverse, the motor losses can increase the inner machine temperature causing degradation of the materials which will lead to serious faults. Most severe faults may lead to a machine breakdown and interruption of the industrial production inflicting severe financial loss. The main goal of this dissertation is to create a computational model of a three-phase squirrel cage induction motor to study and analyze its thermal behavior under healthy and faulty conditions. This will be made through the finite elements method (FEM), where Flux2D 12.1 (Cedrat) software will be used. Initially the computational modeling will focus on the electromagnetic study, in order to calculate the motor losses. After that, those values will be inserted in the thermal simulation to better understand the thermal behavior of the motor. The experimental tests will be carried out with the aid of five temperature sensors (PT100), where the acquisition of the experimental data will be done through a software developed in LabView programming language. As well as that, the results obtained experimentally will be compared with those obtained computationally. However, only the results of two sensors can be compared, since two of them are placed throughout the three-dimensional perspective of the motor and one is placed inner of the motor frame, which will not be defined in the simulation.Os motores elétricos desempenham um papel bastante importante na indústria, pois hoje em dia quase tudo na indústria funciona com o auxílio destes, seja em reduzidas ou elevadas potências. É possível dividir os motores elétricos em motores de indução e motores síncronos, no entanto os mais utilizados na indústria são os de indução, sendo então bastante importante monitorizar o seu comportamento ao longo do tempo. Devido às suas condições de funcionamento, que por vezes são bastante adversas, as perdas do motor podem aumentar causando a degradação dos materiais e levando a falhas graves, que podem prejudicar toda a produção de uma indústria, infligindo graves perdas financeiras O objetivo principal desta dissertação é criar um modelo computacional de um motor de indução trifásico de rotor em gaiola de esquilo para estudar e analisar o seu comportamento térmico, tanto sob condições normais como de avaria. Este trabalho será desenvolvido através do método de elementos finitos (FEM), sendo assim utilizado o software Flux2D 12.1 (Cedrat). Inicialmente a modelação computacional focar-se-á no estudo eletromagnético, de forma a calcularem-se as perdas do motor. Posteriormente, esses valores serão inseridos na simulação térmica, de forma a compreender-se melhor o comportamento térmico do motor. Os ensaios experimentais terão o auxílio de cinco sensores de temperatura (PT100) onde a aquisição dos dados experimentais é efetuada através de um software desenvolvido na linguagem de programação LabView. Posteriormente, os resultados obtidos experimentalmente serão comparados com os resultados obtidos computacionalmente. Porém, apenas os resultados de dois dos sensores podem ser comparados, pois existem dois sensores ao longo da perspetiva tridimensional do motor e um que está situado na periferia interna da carcaça, a qual não será definida na simulação

A Review of Modeling and Diagnostic Techniques for Eccentricity Fault in Electric Machines

Research on the modeling and fault diagnosis of rotor eccentricities has been conducted during the past two decades. A variety of diagnostic theories and methods have been proposed based on different mechanisms, and there are reviews following either one type of electric machines or one type of eccentricity. Nonetheless, the research routes of modeling and diagnosis are common, regardless of machine or eccentricity types. This article tends to review all the possible modeling and diagnostic approaches for all common types of electric machines with eccentricities and provide suggestions on future research roadmap. The paper indicates that a reliable low-cost non-intrusive real-time online visualized diagnostic method is the trend. Observer-based diagnostic strategies are thought promising for the continued research

An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented