DIAGNOSIS OF INDUCTION MOTOR BEARING DEFECT USING DISCRETE WAVELET TRANSFORM AND ARTIFICIAL NEURAL NETWORK

Induction motor is electromechanical equipment that is widely used in various industrial applications. The research paper presents the detection of the defect to three-phase induction motor bearing using discrete wavelet transforms and artificial neural networks to detect whether or not the motor is damaged. An experimental test rig was made to obtain data on healthy phase currents or damaged bearings on the induction motor using the motor current signature analysis (MCSA) method. Several mother-level wavelets are chosen on the wavelet method from the obtained current signal. The feature of the wavelet results is used as an input of the Artificial Neural Network to classify the condition of the induction motor. The results showed that the system could provide an accurate diagnosis of the condition of the induction motor

Machine learning and deep learning based methods toward Industry 4.0 predictive maintenance in induction motors: Α state of the art survey

Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research. Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithms Findings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated. Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015Peer Reviewe

Experimental diagnosis of inter-turns stator fault and unbalanced voltage supply in induction motor using MCSA and DWER

This paper presents a comparative study between two techniques of signal processing to diagnose both faults the inter-turn short circuit (ITSC) in stator windings and the unbalanced voltage supply (UVS) in induction motors. The first is considered a classical technique called Motor Current Signature Analysis (MCSA) which is based on the processing of the stator current by the Fast Fourier Transform (FFT). The second is anadvanced technique based on a Discrete Wavelet Energy Ratio (DWER) of three stator currents. The aim objective of this paper is to compare the ability and effectiveness of both techniques to detect the ITSC fault and the UVS in induction motors, and distinguishing between them. An experimental implementation tests the two diagnosis techniques.The results obtained show that the MCAS technique by the FFT analysis has a difficult to discriminate between the current harmonics due to the provide voltage unbalance and those originated by ITSC faults. Unlike the DWERtechnique, which has high sensitivity and exceptional ability to detect and distinguish between the two faults that lead to the reliability of the diagnosis system. To demonstrate that the DWER is an accurate and robust diagnosis approach are used the neural network (NN) as a tool to classify the faults (ITSC and USV) where using DWER indicators as NN input. The results obtained of combination between the DWER and NN are effective and proved its ability to detect both faults under different load conditions and distinguish between them accurately with low error (10-5)

A review of intelligent methods for condition monitoring and fault diagnosis of stator and rotor faults of induction machines

Nowadays, induction motor (IM) is extensively used in industry, including mechanical and electrical applications. However, three main types of IM faults have been discussed in the literature, bearing, stator, and rotor. Importantly, stator and rotor faults represent approximately 50%. Traditional condition monitoring (CM) and fault diagnosis (FD) methods require a high processing cost and much experience knowledge. To tackle this challenge, artificial intelligent (AI) based CM and FD techniques are extensively developed. However, there have been many review research papers for intelligent CM and FD machine learning methods of rolling elements bearings of IM in the literature. Whereas there is a lack in the literature, and there are not many review papers for both stator and rotor intelligent CM and FD. Thus, the proposed study's main contribution is in reviewing the CM and FD of IM, especially for the stator and the rotor, based on AI methods. The paper also provides discussions on the main challenges and possible future works

Application of a wavelet neural network approach to detect stator winding short circuits in asynchronous machines

Introduction. Nowadays, fault diagnosis of induction machines plays an important role in industrial fields. In this paper, Artificial Neural Network (ANN) model has been proposed for automatic fault diagnosis of an induction machine. The aim of this research study is to design a neural network model that allows generating a large database. This database can cover maximum possible of the stator faults. The fault considered in this study take into account a short circuit with large variations in the machine load. Moreover, the objective is to automate the diagnosis algorithm by using ANN classifier. Method. The database used for the ANN is based on indicators which are obtained from wavelet analysis of the machine stator current of one phase. The developed neural model allows to taking in consideration imbalances which are generated by short circuits in the machine stator. The implemented mathematical model in the expert system is based on a three-phase model. The mathematical parameters considered in this model are calculated online. The characteristic vector of the ANN model is formed by decomposition of stator current signal using wavelet discrete technique. Obtained results show that this technique allows to ensure more detection with clear evaluation of turn number in short circuit. Also, the developed expert system for the taken configurations is characterized by high precision.Вступ. Нині діагностика несправностей асинхронних машин відіграє значну роль у промисловості. У цій статті запропоновано модель штучної нейронної мережі для автоматичної діагностики несправностей асинхронної машини. Метою цього дослідження є розробка моделі нейронної мережі, що дозволяє генерувати велику базу даних. Ця база може охоплювати максимально можливі несправності статора. Несправності, розглянуті у цьому дослідженні, враховують коротке замикання при великих коливаннях навантаження машини. Крім того, мета полягає в тому, щоб автоматизувати алгоритм діагностики за допомогою класифікатора штучної нейронної мережі. Метод. База даних, що використовується для штучної нейронної мережі, заснована на показниках, отриманих в результаті вейвлет-аналізу струму статора машини однієї фази. Розроблена нейронна модель дозволяє враховувати дисбаланси, що виникають при коротких замиканнях у статорі машини. Реалізована математична модель в експертній системі ґрунтується на трифазній моделі. Математичні параметри, що враховуються в цій моделі, розраховуються онлайн. Характеристичний вектор моделі штучної нейронної мережі формується шляхом розкладання сигналу струму статора з використанням вейвлет-дискретного методу. Отримані результати показують, що дана методика дозволяє забезпечити більше виявлення з чіткою оцінкою числа витків при короткому замиканні. Також розроблена експертна система для конфігурацій, що приймаються, відрізняється високою точністю

Wavelet packet analysis for rotor bar breakage in an inverter induction motor

Introduction. In various industrial processes, squirrel cage induction motors are widely employed. These motors can be used in harsh situations, such as non-ventilated spaces, due to their high strength and longevity. These machines are subject to malfunctions such as short circuits and broken bars. Indeed, for the diagnosis several techniques are offered and used. Novelty of the proposed work provides the use of wavelet analysis technology in a continuous and discrete system to detect faults affecting the rotating part of an induction motor fed by a three-phase inverter. Purpose. This paper aims to present a novel technique for diagnosing broken rotor bars in the low-load, stationary induction machine proposed. The technique is used to address the problem of using the traditional Techniques like Fourier Transforms signal processing algorithm by analyzing the stator current envelope. The suggested method is based on the use of discrete wavelet transform and continuous wavelet transform. Methods. A waveform can be monitored at any frequency of interest using the suggested discrete wavelet transform and continuous wavelet transform. To identify the rotor broken bar fault, stator current frequency spectrum is analyzed and then examined. Based on a suitable index, the algorithm separates the healthy motor from the defective one, with 1, 2 and 3 broken bars at no-load. Results. In comparison to the healthy conditions, the recommended index significantly raises under the broken bars conditions. It can identify the problematic conditions with clarity. The possibility of detecting potential faults has been demonstrated (broken bars), using discrete wavelet transform and continuous wavelet transform. The diagnostic method is adaptable to temporary situations brought on by alterations in load and speed. Performance and efficacy of the suggested diagnostic method are demonstrated through simulation in Simulink® MATLAB environment.Вступ. У різних промислових процесах широко використовуються асинхронні двигуни із короткозамкненим ротором. Ці двигуни можуть використовуватися в суворих умовах, наприклад, в приміщеннях, що не вентилюються, завдяки їх високій міцності і довговічності. Ці машини схильні до несправностей, таких як коротке замикання і зламані стрижні. Зрозуміло, що для діагностики пропонується та використовується кілька методик. Новизна запропонованої роботи полягає у використанні технології вейвлет-аналізу в безперервній і дискретній системі для виявлення несправностей, що впливають на частину асинхронного двигуна, що обертається, що живиться від трифазного інвертора. Мета. У цій статті представлена нова методика діагностики зламаних стрижнів ротора в малонавантаженій стаціонарній асинхронній машині. Цей метод використовується для вирішення проблеми використання традиційних методів, таких як алгоритм обробки сигналів перетворення Фур’є, шляхом аналізу огинаючої струму статора. Пропонований метод заснований на використанні дискретного вейвлет-перетворення та безперервного вейвлет-перетворення. Методи. Форма сигналу може відстежуватися на будь-якій частоті, що цікавить, з використанням запропонованого дискретного вейвлет-перетворення і безперервного вейвлет-перетворення. Для виявлення несправності обриву стрижня ротора частотний спектр статора аналізується, а потім досліджується. На основі відповідного індексу алгоритм відокремлює справний двигун від несправного з 1, 2 і 3 зламаними стрижнями на холостому ході. Результати. Порівняно із нормальними умовами рекомендований показник значно підвищується за умов зламаних стрижнів. Він може чітко визначити проблемні умови. Було продемонстровано можливість виявлення потенційних несправностей (зламані стрижні) з використанням дискретного вейвлет-перетворення та безперервного вейвлет-перетворення. Метод діагностики адаптується до тимчасових ситуацій, викликаних змінами навантаження та швидкості. Працездатність та ефективність запропонованого методу діагностики продемонстровано за допомогою моделювання у середовищі Simulink® MATLAB

Mechanical Failures Detection by Vibration Analysis in Rotary Machines Using Wavelet and Artificial Neural Network in the Gera Maranhão Plant.

This article presents aspects of a tool to assist in predictive maintenance based on vibration analysis in rotating machines using wavelet transform and artificial neural networks. The work analyzed the experimental results of applying a methodology based on the combination of the discrete wavelet transform using a Gaussian window with an artificial neural network for condition monitoring of three-phase induction motors. This approach consisted of simulating faulty and flawless signals using software developed in LabVIEW, their processing, appropriate choice of signals, establishing statistical measures of the chosen signs, and forming the input vectors presented to the artificial neural network. The input vectors are constituted based on statistical measures involving measures of central tendency (mean and centroid), measures of dispersion (RMS value and standard deviation), and a measure of asymmetry (Kurtosis). The most promising configuration was the Multiple Perceptron Layer (MPL) network with four hidden layers containing 256 neurons. Such network showed satisfactory performance for both mechanical failures, with a correct range of around 97%. These results proved to be very effective for detecting mechanical failures, thus being an auxiliary instrument in predictive maintenance