Induction Motor Faults and Artificial Intelligence Based Conditioning and Monitoring Techniques

Three phase induction motors have been intensively utilized in industrial applications, mainly due to their efficiency and reliability. These motors have good properties such as increased stability, robustness, durability, large power to weight ratio, low production costs and controllability easiness. All machines realize various stresses during operational conditions. These stresses might lead to some modes of failures or faults. Condition monitoring is necessary in order to prevent faults. These faults, are necessary to be identi?ed and categorized, as soon as possible as they can end up in serious damages if not detected in due time. Different techniques of fault monitoring for induction motors are broadly classified as techniques based on model, signal processing, and soft computing. In recent years the monitoring and fault detection of electrical machines have moved from traditional techniques to Artificial Intelligence (AI). In this paper an attempt has been made to review different faults on induction motors and the applications of neural/fuzzy artificial intelligence techniques for induction motor condition monitoring. A brief description of various AI techniques highlighting the merits and demerits of each has been discussed. The futuristic trends on condition monitoring of induction motors are also indicated

Development of an induction motor condition monitoring test rig And fault detection strategies

Includes bibliographical references.This thesis sets out to develop an induction motor condition monitoring test rig to experimentally simulate the common faults associated with induction motors and to develop strategies for detecting these faults that employ signal processing techniques. Literature on basic concepts of induction motors and inverter drives, the phenomena of common faults associated with induction motors, the condition monitoring systems were intensively reviewed

Monitoring and damping unbalanced magnetic pull due to eccentricity fault in induction machines: A review

© 2017 IEEE. Condition monitoring can diagnose the inception of fault mechanisms in induction motors, thus avoiding failure and expensive repairs. Therefore, there is a strong need to develop an efficient condition monitoring. The main target is to achieve a relatively low cost and/or non-invasive system which is still powerful in terms of monitoring for online detection of developing faults. The presented paper addresses rotor eccentricity faults and studies conventional monitoring techniques for induction motors. In order to reduce the unbalanced magnetic pull (UMP) in case of an eccentric rotor, the eccentricity-generated additional airgap flux waves should be reduced. The radial forces in an induction motor are calculated, and the characteristics of unbalanced magnetic pull are described

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

Monitoring and Damping UMP Due Eccentricity Fault in Induction Machines: A Review

© 2016 IEEE. Three-phase induction machines are reliable and widely used in industrial plants. The efficient condition monitoring can diagnose the inception of fault mechanisms in induction motors thus avoiding failure and expensive repairs. Therefore, there is a strong need to develop a more efficient condition monitoring. The main target is to achieve a relatively low cost and/or non-invasive system which is still powerful in terms of monitoring for online detection of developing faults. This digest adresses rotor eccentricity faults and study of conventional monitoring techniques for induction motor faults. In order to reduce the UMP in case of an eccentric rotor, the eccentricity-generated additional airgap flux waves should be reduced. Additional, the characteristics of UMP in induction machines are addressed. Methods to reduce the side-band flux waves and hence attenuate the UMP will be addressed

Health Monitoring of Induction Motor Through Vibration Analysis

Machinery monitoring is the process of monitoring a parameter of condition in machinery, such that a significant change is indicative of a failure in development. Temperature, vibration, noise, current, voltage, acoustic emission, etc. – all these measurements are used for machine condition monitoring. Measuring vibration signals of the Non-Destructive Testing (NDT) method is widely used to detect machine faults. There are many studies for the prediction of mechanical wear, fault and failure in this area for several decades. Signal processing techniques are used to obtain vital characteristic information from the vibration signals. This paper attempts to summarize the results of an evaluation of vibration analysis techniques as a method for diagnosis for three-phase induction motors

BASIC ALGORITHM FOR INDUCTION MOTORS ROTOR FAULTS PRE-DETERMINATION

Due to importance of squirrel cage induction motor in today’s industry, the fault detection on that type of motors has become a highly developed area of interest for researchers. The electrical machine is designed for stable operations with minimum noise and vibrations under the normal conditions. When the fault emerges, some additional distortions appear. The necessity to detect the fault in an early stage, to prevent further damage of the equipment due to fault propagation, is one of the most important features of any condition monitoring or diagnostic techniques for electrical machines nowadays. In this paper possible induction motors faults classified and basic algorithm for rotor faults pre-determination is presented

Current based condition monitoring of electromechanical systems. Model-free drive system current monitoring: faults detection and diagnosis through statistical features extraction and support vector machines classification.

A non-invasive, on-line method for detection of mechanical (rotor, bearings eccentricity) and stator winding faults in a 3-phase induction motors from observation of motor line current supply input. The main aim is to avoid the consequence of unexpected failure of critical equipment which results in extended process shutdown, costly machinery repair, and health and safety problems. This thesis looks into the possibility of utilizing machine learning techniques in the field of condition monitoring of electromechanical systems. Induction motors are chosen as an example for such application. Electrical motors play a vital role in our everyday life. Induction motors are kept in operation through monitoring its condition in a continuous manner in order to minimise their off times. The author proposes a model free sensor-less monitoring system, where the only monitored signal is the input to the induction motor. The thesis considers different methods available in literature for condition monitoring of induction motors and adopts a simple solution that is based on monitoring of the motor current. The method proposed use the feature extraction and Support Vector Machines (SVM) to set the limits for healthy and faulty data based on the statistical methods. After an extensive overview of the related literature and studies, the motor which is the virtual sensor in the drive system is analysed by considering its construction and principle of operation. The mathematical model of the motor is used for analysing the system. This is followed by laboratory testing of healthy motors and comparing their output signals with those of the same motors after being intentionally failed, concluding with the development of a full monitoring system. Finally, a monitoring system is proposed that can detect the presence of a fault in the monitored machine and diagnose the fault type and severityMinistry of Higher Education, Libya; Switchgear & Instruments Ltd

Nondestructive Tests for Induction Machine Faults Diagnosis

A maintenance program must include several techniques of monitoring of the electric motor\u27s conditions. Among these techniques, probably the two classic ones are related to megger and impulse test. Unfortunately, in both cases, inherent drawbacks can expose the electrical motor at a high voltage that could deteriorate insulation condition making difficult its use on industrial environment. As the electrical machines have several different components (e.g., bearings, rotor bars, shaft, and stator windings), the fault frequencies can be excited by mechanical and/or electrical faults making the identification of the real condition difficult. This chapter describes several methods of the nondestructive tests for induction motors based on the motor current signature analysis (MCSA), magnetic flux, and vibration analysis. The method of analysis is a good alternative tool for destructive tests and fault detection in induction motors. Numerical and experimental results demonstrate the effectiveness of the proposed technique. This chapter also presents a model suitable for computer simulation of induction motor in a healthy state and with general asymmetries that can be analyzed simultaneously. The model makes it possible to conduct research on different characteristics of engines and outstanding effects produced by the faults

On-line Condition Monitoring, Fault Detection and Diagnosis in Electrical Machines and Power Electronic Converters

The objective of this PhD research is to develop robust, and non-intrusive condition monitoring methods for induction motors fed by closed-loop inverters. The flexible energy forms synthesized by these connected power electronic converters greatly enhance the performance and expand the operating region of induction motors. They also significantly alter the fault behavior of these electric machines and complicate the fault detection and protection. The current state of the art in condition monitoring of power-converter-fed electric machines is underdeveloped as compared to the maturing condition monitoring techniques for grid-connected electric machines. This dissertation first investigates the stator turn-to-turn fault modelling for induction motors (IM) fed by a grid directly. A novel and more meaningful model of the motor itself was developed and a comprehensive study of the closed-loop inverter drives was conducted. A direct torque control (DTC) method was selected for controlling IM’s electromagnetic torque and stator flux-linkage amplitude in industrial applications. Additionally, a new driver based on DTC rules, predictive control theory and fuzzy logic inference system for the IM was developed. This novel controller improves the performance of the torque control on the IM as it reduces most of the disadvantages of the classical and predictive DTC drivers. An analytical investigation of the impacts of the stator inter-turn short-circuit of the machine in the controller and its reaction was performed. This research sets a based knowledge and clear foundations of the events happening inside the IM and internally in the DTC when the machine is damaged by a turn fault in the stator. This dissertation also develops a technique for the health monitoring of the induction machine under stator turn failure. The developed technique was based on the monitoring of the off-diagonal term of the sequence component impedance matrix. Its advantages are that it is independent of the IM parameters, it is immune to the sensors’ errors, it requires a small learning stage, compared with NN, and it is not intrusive, robust and online. The research developed in this dissertation represents a significant advance that can be utilized in fault detection and condition monitoring in industrial applications, transportation electrification as well as the utilization of renewable energy microgrids. To conclude, this PhD research focuses on the development of condition monitoring techniques, modelling, and insightful analyses of a specific type of electric machine system. The fundamental ideas behind the proposed condition monitoring technique, model and analysis are quite universal and appeals to a much wider variety of electric machines connected to power electronic converters or drivers. To sum up, this PhD research has a broad beneficial impact on a wide spectrum of power-converter-fed electric machines and is thus of practical importance