91 research outputs found

    Stray magnetic field based health monitoring of electrical machines

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    PhD ThesisElectrical machines are widely used in industrial and transportation applications which are essential to industrial processes. However, the lack of reliability and unpredictable life cycles of these machines still present opportunities and challenges for condition monitoring research. The breakdown of an electrical machine leads to expensive repairs and high losses due to downtime. The motivation of this research is to improve the reliability of electrical machines and to classify different kinds of failures via non-intrusive methods for condition-based maintenance and early warning of failure. Major potential failure types in electrical machines are winding and mechanical failures, which are caused by dynamic load state, component ageing and harsh working environments. To monitor and characterise these abnormal situations in the early stages, this thesis proposes stray magnetic field-based condition monitoring allowing fault diagnosis with the help of finite element models and advanced signal processing technology. By investigating the interaction between stray flux variations and machine failure, different kinds of faults can be classified and distinguished via numerical and experimental studies. A non-intrusive stray flux monitoring system has been developed and can provide both static and transient stray flux information and imaging. The designed monitoring system is based on a giant magnetoresistance (GMR) sensor used to capture low stray flux fields outside the electrical machineā€™s frame. Compared with other monitoring systems, its small size, low cost, non-inventive and ease of setting up make the designed system more attractive for in many long-term monitoring applications. Additionally, integration with the wireless sensor network (WSN) means that the latterā€™s unique characteristics makes the proposed system suitable for electrical machine monitoring in industrial applications replacing existing expensive wired systems. The proposed system can achieve real-time data collection and on-line monitoring with the help of spectrogram and independent component analysis. Three cases studies are conducted to validate the proposed system with different failures and loading states, using load fatigue, winding short-circuit failure and mechanical testing. In these case studies, electrical and mechanical failures and dynamic loads are investigated, collecting stray flux information with different kinds and sizes of electrical machines using both simulation and experimental approaches. Stray flux information is collected for different situations of winding failure, unbalanced load and bearing failures. Comprehensive transient feature extraction using spectrogram is implemented with respect to multiple failures and load variations. Spectrograms of stray flux can provide time-frequency information allowing the discrimination of different failures and load states. Different faults can be distinguished through independent component analysis of stray flux data. Compared with traditional and current detection strategies, stray flux-based monitoring can not only provide failure indicator and better resolution but also gives location information. Additionally, by applying different feature extraction methods, different failure types can be separated based on stray flux information, which is likely to be difficult to achieve using traditional monitoring approaches. However, stray flux monitoring systems suffer from issue of noise and instability, and more case studies and investigations are needed for further refinement

    THE USE OF EFFICIENCY INDICATORS FOR THE DETECTION OF COMBINED MOTOR ASYMMETRY FAULTS AND THEIR EFFECTIVENESS WHEN USED ON INVERTER DRIVEN MOTOR SYSTEMS

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    The use of Variable Speed Drives (VSDā€™s) for the control of AC motor systems in industry is well established and continues to expand year-on-year. The increasing use of VSD's can be found not only in renewable energy applications such as wind turbines and tidal generators but also in transport such as motor vehicles and marine propulsion, as well as industry such as on conveyors, material processing and pumping applications. To ensure safe, reliable and efficient operation of these applications, Condition Monitoring CM is essential. Following a detailed literature review of established research on motor-driven system CM, it has been found that existing research works are more concerned with detection and identification of specific motor faults. There is a lack of research into how certain motor faults contribute to the degradation of the motor driven system efficiency. Whilst the efficiency monitoring of an AC motor system has previously been researched, being able to measure efficiency decreases caused by certain motor faults and on a VSD system operating in different control modes is not an area that has been studied previously. In fact, new European Union EU draft regulations detailing potential future legislation that may become mandatory to define the efficiency of motor driven systems fed by VSDā€™s has been drafted, but does not detail how these efficiencies may be measured or monitored in the field. To overcome the gap in research, this research focuses on the use of efficiency monitoring methods on a VSD-driven motor system to measure any reduction in efficiency at an early stage caused by minor motor faults. The studies are based on model simulation of a basic DOL-operated AC motor followed by experimental work on a laboratory test rig where the model simulation is translated to a VSD operating at a fixed-speed with different loads applied. This research has found that the efficiency reduction of the motor driven system under different faulty conditions can be detected using certain characteristics of the motor signals. Novel findings have also shown that the efficiency of the motor driven system can be improved by selecting different VSD operating modes. The research also shows that different VSD control techniques can help to improve the regulation of motor speed control when the motor is subjected to minor faults. These findings are important as they provide proof-of-concept for users of VSD systems who may wish to implement efficiency monitoring strategies on their VSD-operated motors with the minimum of intervention. Furthermore, simply by changing the operating strategy of the users VSD, there may be immediate efficiency benefits offered to the equipment that do not require any new hardware

    FAULT DIAGNOSIS OF MECHANICAL SYSTEMS BASED ON ELECTRICAL SUPPLY CHARACTERISTICS

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    Induction motors are the main workhorses of industry. Condition monitoring (CM) of motor based systems plays an important role in the early detection of possible defects, averting adverse operational and financial effects caused by unexpected breakdowns. Limited information has been found which explores the diagnostic abilities of voltage and motor current signals from motors with variable speed drives (VSDs), which are increasingly used in industry to obtain better dynamic response, higher efficiency and lower energy consumption. This study addresses the gap identified by carrying out a systematic review of the monitoring of mechanical systems using induction motors with sensorless VSDs. Specifically, existing techniques often prove ineffective for common internal and external faults that develop in Induction motors. The primary aim is to extract accurate diagnostic information from the power supply parameters of a VSD to monitor IM driven systems for early diagnosis of both mechanical and electrical faults. This thesis examines the effectiveness of both motor current and voltage signals using spectrum analysis for detecting broken rotor bar(s) and/or shaft misalignment and gear oil viscosity changes with different degrees of severities under sensorless control (close) modes. The results are obtained from common spectrum analysis applied to signals from a laboratory experimental setup operating under different speeds and loads. Evaluation of the results shows that the faults cause an increase in sideband amplitudes, which can be observed in both the current and voltage signals under the sensorless control mode. In addition, combined faults cause an additional increase in the sideband amplitudes and this increase can be observed in both the current and voltage signals. The voltage signals show greater change compared with the current signals because the VSD adapts the voltage supply source to compensate for changes in the system dynamics. Furthermore, this study also presents a model of an induction motorfed by a variable speed drive (VSD), as an approach to simulate broken rotor bars and shaft misalignments to give an in-depth understanding of fault signatures. The model was validated with experimental results in both current and voltage signals, with good agreement. The model confirmed that BRB causes a shift and increase in the amplitudes of the sidebands with the amplitudes of the rotor frequency components increased due to shaft misalignment

    Induction Motors

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    AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis

    THE ANALYSIS OF POWER SUPPLY SIGNALS BY INCLUDING PHASE EFFECTS FOR MACHINE FAULT DIAGNOSIS

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    Substantial efforts have been devoted to developing Condition Monitoring techniques to provide timely preventative measures for ensuring a safe and cost-effective operation of electromechanical systems. High investment of installation and implementation in using conventional condition monitoring techniques such as vibration based monitoring makes it difficult to be used in most industries such as petrochemical processing, food and drinking processes, paper mills and so on where large number of motor drives are used but operational profits are very limited. To overcome the shortages of vibration based monitoring, this project focuses on developing condition monitoring techniques based on electrical signal analysis which can offer great savings as electric signatures that can monitor a large system are generally available in most motor drives. However, fault signatures in electrical signatures such as instantaneous current and voltage signals are very weak and contaminated by noise. To enhance the signatures, this study has focused on using two more advanced signal processing approaches: 1) Modulation signal bispectrum analysis, which enhances the modulation and suppresses random noise by including phase linkages. 2) Instantaneous phase quantities including conventional instantaneous power factor and a novel instantaneous phase of voltage and current which highlights instantaneous phase changes through a summation of instantaneous phases in current and voltage signals. It has the ability of enhancing the phase components that are of the same phases in both voltage and current signals, and also cancel out any random components to a great extent, producing more diagnostic information. These two approaches emphasis the use of phase information along with that of amplitudes and frequency in a signal that is based on in most previous methods in the condition monitoring fields. Based on a general electromechanical system comprising of a AC motor, a gearbox and a DC generator, it firstly explored the characteristics of the signatures by modelling and simulation studies, which lead to that faults in a sensorless Variable speed drive system can produce combined amplitude and frequency modulation effects in both current and voltage signals fed to the AC motor. Moreover, the modulating frequencies and levels are closely associated with the rotational frequencies of the gearbox and fault severity respectively, which become more significant at higher load conditions. Experimental evaluations have found that these two proposed methods allow common faults in the downstream gearbox including gear tooth breakage, oil shortage and excessive bearing clearances to be detected and diagnosed under high load conditions, showing the effectiveness and accuracy of these two new approaches. Furthermore, the results show that the electrical signature analysis is capable of detecting and diagnosing different faults in sensorless variable speed drive systems. Instantaneous phase of voltage and current has been shown to provide more consistent and accurate separation between the three different faults under different loads. The use of the modulation signal bispectrum analysis succeed to provide an improved, accurate and reliable diagnostic with the power signal providing the best means of detecting and determining fault severity with good separation between fault levels

    Fault detection and diagnosis method for three-phase induction motor

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    Induction motors (IM) are critical components in many industrial processes. There is a continually increasing interest in the IMsā€™ fault diagnosis. The scope of this thesis involves condition monitoring and fault detection of three phase IMs. Different monitoring techniques have been used for fault detection on IMs. Vibration and stator current monitoring have gained privilege in literature and in the industry for fault diagnosis. The performance of the vibration and stator current setups was compared and evaluated. In that perspective, a number of data were captured from different faulty and healthy IMs by vibration and current sensors. The Principal Component Analysis (PCA) was utilized for feature extraction to monitor and classify collected data for finding the faults in IMs. A new method was proposed with the combined use of vibration and current setups for fault detection. It consists of two steps: firstly, the training part with the aim of giving acceleration property (nature of vibration data) to the current features, and secondly the testing part with the aim of excluding the vibration setup from the fault detection algorithm, while the output data have the property of vibration features. The 0-1 loss function was applied to show the accuracy of vibration, current and proposed fault detection method. The PCA classified results showed mixed and unseparated features for the current setup. The vibration setup and the proposed method resulted in substantial classified features. The 0-1 loss function results showed that the vibration setup and the developed method can provide a good level of accuracy. The vibration setup attained the highest accuracy of 98.2% in training and 92% in testing. The proposed method performed well with accuracies of 96.5% in training and 84% in testing. The current setup, however, attained the lowest level of accuracy (66.7% in training and 52% in testing). To assess the performance of the proposed method, the Confusion matrix of classification in NN was utilized. The Confusion matrix showed an accuracy of 95.1% of accuracy and negligible incorrect responses (4.9%), meaning that the proposed fault detection method is reliable with minimum possible errors. These vibration, current and proposed fault detection methods were also evaluated in terms of cost. The proposed method provided an affordable fault detection technique with a high accuracy applicable in various industrial fields
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