Fault Detection and Prediction in Induction Motors

Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Electrical and Electronic Engineering, May 2022ccInduction motors are expensive and the backbone of every industry. There would be no production when induction motors break down. It is also costly to repair them after a sudden shutdown. Industries are gradually adapting to predictive maintenance to prevent unnecessary shutdowns and reduce the cost of maintenance. The objective of this paper is to even make the predictive maintenance of inter-turn short circuit fault in induction motors more reliable by adding fault detection and deploying the entire system in an alarm and display system. In this project, secondary current data from a three-phase induction motor has been used because of the current's capabilities of detecting a higher percentage of electrical faults. This is achieved using predictive maintenance toolbox in MATLAB.Ashesi Universit

Condition monitoring and fault detection of inverter-fed rotating machinery

Condition monitoring of rotating machinery is crucial in industry. It can prevent long term outages that can prove costly, prevent injury to machine operators, and lower product quality. Induction motors, often described as the workhorse of industry, are popular in industry because of their robustness, efficiency and the need for low maintenance. They are, however, prone to faults when used improperly or under strenuous conditions. Gearboxes are also an important component in industry, used to transmit motion and force by means of successively engaging teeth. They too are prone to damage and can disrupt industrial processes if failure is unplanned for. Reciprocating compressors are widely used in the petroleum and the petrochemical industry. Their complex structure, and operation under poor conditions makes them prone to faults, making condition monitoring necessary to prevent accidents, and for maintenance decision-making and cost minimization. Various techniques have been extensively investigated and found to be reliable tools for the identification of faults in these machines. This thesis, however, sets out to establish a single non-invasive tool that can be used to identify the faults on all these machines. Literature on condition monitoring of induction motors, gearboxes, and reciprocating compressors is extensively reviewed. The time, frequency, and time-frequency domain techniques that are used in this thesis are also discussed. Statistical indicators were used in the time domain, the Fourier Transform in the frequency domain, and Wavelet Transforms in the time-frequency domain. Vibration and current, which are two of the most popular parameters for fault detection, were considered. The test rig equipment that is used to carry to the experiments, which comprised a modified Machine Fault Simulator -Magnum (MFS-MG), is presented and discussed. The fault detection strategies rely on the presence of a fault signature. The test rig that was used allows for the simulation of individual or multiple concurrent faults to the test machinery. The experiments were carried out under steady-state and transient conditions with the faults in the machines isolated, and then with multiple faults implemented concurrently. The results of the fault detection strategies are analysed, and conclusions are drawn based on the performances of these tools in the detection of the faults in the machinery

Trends and Challenges in Electric Vehicle Motor Drivelines - A Review

Considering the need to optimize electric vehicle performance and the impact of efficient driveline configurations in achieving this, a brief study has been conducted. The drivelines of electric vehicles (EV) are critically examined in this survey. Also, promising motor topologies for usage in electric vehicles are presented. Additionally, the benefits and drawbacks of each kind of electric motor are examined from a system viewpoint. The majority of commercially available EV are powered by a permanent magnet motor or single induction type motors and a standard mechanical differential driveline. Considering these, a holistic review has been performed by including driveline configurations and different battery types. The authors suggest that motors be evaluated and contrasted using a standardized driving cycle

Kritik al-Quran oleh Nasr Hamid Abu Zayd melalui terapan hermeneutics humanistic

Nasr Hamid Abu Zayd dikenali sebagai seorang tokoh yang lantang mengkritik al-Quran pada abad ini. Beliau mempunyai metode yang tersendiri ketika mentafsir al-Quran iaitu mengaplikasi hermeneutik dengan menerapkan elemen humanistic. Artikel ini bertujuan untuk menganalisis elemen humanistic yang dibawa oleh Abu Zayd dalam empat aspek iaitu definisi al-Quran, konsep bahawa wahyu, proses penurunan wahyu dan metode pentafsiran. Empat aspek tersebut dibandingkan secara berterusan dengan al-Quran dan al-Sunnah. Untuk mencapai validity data, artikel yang bersifat kualitatif ini menggunakan metode analisis kandungan yang terdiri daripada karyakarya Abu Zayd sebagai sumber pengumpulan data. Manakala analisis data menggunakan kaedah diskriptif dan perbandingan berterusan. Hasil kajian menunjukkan, elemen humanistic yang telah diterapkan kepada al-Quran telah mencetuskan implikasi terhadap al- Quran, konsep wahyu, tafsiran relatif dan liberalisasi hukum syariah

ANFIS based Direct Torque Control of PMSM Motor for Speed and Torque Regulation

Nowadays, the Permanent Magnet Synchronous Motors (PMSM) are gaining popularity among electric motors due to their high efficiency, high-speed operation, ruggedness, and small size. PMSM motors comprise a trapezoidal electromotive force which is also called synchronous motors. Direct Torque Control (DTC) has been extensively applied in speed regulation systems due to its better dynamic behavior. The controller manages the amplitude of torque and stator flux directly using the direct axis current. To manage the motor speed, the torque error, flux error, and projected location of flux linkage are employed to adjust the inverter switching sequence via Space Vector Pulse Width Modulation (SVPWM). One of the most common problems encountered in a PMSM motor is Torque ripple, which is recreated by power electronic commutation and a better controller reduces the ripples to increase the drive's performance. Conventional controllers such as PI, PID and SVPWM-DTC were compared with the proposed Adaptive Neuro-Fuzzy Inference System (ANFIS) in terms of performance measures such as speed and torque ripple. In this work, the Two-Gaussian membership function of the ANFIS controller is used in conjunction with a PMSM motor to reduce torque ripple up to 0.53 Nm and maintain the speed with a distortion error of 2.33 %

Advancing Fault Diagnosis for Parallel Misalignment Detection in Induction Motors Based on Convolutional Neural Networks

Maintenance of machines is highly necessary to prolong the operational lifespan of induction motors. Prioritizing preventive measures is crucial in order to prevent more significant damage to the machinery. One of these measures includes detecting abnormalities, such as misalignment, in the motor shaft. This research is aimed to detect the misalignment of induction motor experimentally by varying the coupling between normal and parallel misalignment. The signal readings were analyzed in the frequency domain using Fast Fourier Transform (FFT). The results revealed that in the case of coupling misalignment, a peak appeared at f = 13.5 Hz, whereas in the parallel misalignment condition with a 1 cm misalignment, a peak was found at f+fr = 20 Hz. By utilizing the Convolutional Neural Network (CNN) system, normal and parallel conditions can be detected with an accuracy level of 87.5%

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi

A Graphical Causality based Modelling Approach for Condition Monitoring

The wide use of electromechanical systems in critical applications motivates the need for condition monitoring (CM) of such systems. Signal-based methods require simulating all possible malfunction conditions of a system which in reality not possible. Model-based CM is a promising solution and provides a cost-effective and appropriate approach for simulation of all possible operating conditions and can be used for early fault detection and diagnosis. Many models have been utilized to simulate the behaviour of electric machines; however, most of these simulations are based on abstract numerical models rather than a structured illustration of the system. To overcome these problems, a Bond Graph (BG) model with qualitative simulation has been used in this thesis. BGs are efficient at modelling the dynamics of system behaviour based on physical structure, causality and mathematics. Qualitative simulation (QS) represents semantic knowledge concerning the performance of a particular system for qualitative reasoning. QS could be applied with minimum knowledge of system variables and even with incomplete system model . Therefore, this study focuses on the investigation of QS procedures to develop a more effective and realistic approach to monitor an industrial machine. Significantly, the study has also developed a qualitative BG fault detection approach based on temporal causal graphs (TCGs), qualitative reasoning and, forward propagation. It mainly describes the dynamics of an AC induction motor (ACIM), which is commonly used in numerous industries. It also used to detect ACIM electrical faults (broken rotor bar and stator winding imbalance that commonly occur in ACIM). Further promotes the diagnostics performance of by introducing an algorithm for ACIM diagnosis, which is based the qualitative influence of the faults on the motor current. In order to evaluate the proposed QS approach, to enhance the knowledge of the dynamic behaviour of ACIM, a BG model of the AC induction motor has been introduced. The developed BG model can simulate the motor behaviour under different conditions, including a healthy motor, a motor with two broken rotor bar levels (one and two broken bars), and a motor with two different levels of stator winding imbalance. The investigation was based on the motor current spectrum analysis using the FFT signal processing technique. An experimental study investigates the effects of broken rotor bars and stator imbalance on the motor current. The BG model results and corresponding results from the experimental study have been in good agreement. Moreover, it has also been shown that these outcomes are agreed upon with outcomes reported in the literature. The TCG and forward propagation results indicated that this approach could be used for the CM of ACIMs. It can detect the effects of broken rotor bars and stator imbalance on the whole system behaviour, showing that this developed QS approach is an efficient technique for extracting diagnostic information, ending up with accurate fault detection using TCG and qualitative reasoning. The QS technique was validated based on a 20-SIM simulation of the ACIM BG model. The observed results show that a QS approach can accurately detect a broken rotor bar and stator imbalance faults. The investigation continued by examining the qualitative influence of the seeded electrical faults on the motor current signatures. The results from the experimental study confirm that the BG model and qualitative influence give accurate diagnoses. Comparison evaluation has been done to compare the graphical causality-based approach with work in the literature. The graphical causality-based approach represents an efficient and meaningful technique for simulating the dynamic system behavior. The diagnostic approach based on TCG is very effective for the detection of ACIM electrical faults. Moreover, it overcomes the limitations of some qualitative studies in the literature