Browsing by Subject "fault diagnosis"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Diagnosis and Isolation of Air Gap Eccentricities in Closed-loop Controlled Doubly-Fed Induction Generators(2012-07-16) Meenakshi Sundaram, VivekWith the widespread use of doubly-fed induction generators (DFIG) in wind energy conversion systems, condition monitoring is being given importance. Non-intrusive techniques like motor current signature analysis (MCSA), which involves looking for specific frequency components in the current spectrum, are preferred over analysis of magnetic field, temperature, vibrations or acoustic noise which require additional sensors. The major difficulty in MCSA is isolation of the fault, as multiple faults produce similar signatures. Moreover, closed-loop control makes diagnostics more complicated due to inherent compensation by the controller. This thesis presents a method to diagnose static and dynamic air gap eccentricities in doubly-fed induction generators operated for closed-loop stator power control by using a modified control technique to enable detection and isolation of this fault from electrical unbalances in the stator and rotor and load torque oscillations that produce similar effects. The effectiveness of the proposed control is verified using simulations and preliminary experiments performed on a healthy machine.Item Low-cost motor drive embedded fault diagnosis systems(2009-05-15) Akin, BilalElectric motors are used widely in industrial manufacturing plants. Bearing faults, insulation faults, and rotor faults are the major causes of electric motor failures. Based on the line current analysis, this dissertation mainly deals with the low cost incipient fault detection of inverter-fed driven motors. Basically, low order inverter harmonics contributions to fault diagnosis, a motor drive embedded condition monitoring method, analysis of motor fault signatures in noisy line current, and a few specific applications of proposed methods are studied in detail. First, the effects of inverter harmonics on motor current fault signatures are analyzed in detail. The introduced fault signatures due to harmonics provide additional information about the motor faults and enhance the reliability of fault decisions. It is theoretically and experimentally shown that the extended fault signatures caused by the inverter harmonics are similar and comparable to those generated by the fundamental harmonic on the line current. In the next chapter, the reference frame theory is proposed as a powerful toolbox to find the exact magnitude and phase quantities of specific fault signatures in real time. The faulty motors are experimentally tested both offline, using data acquisition system, and online, employing the TMS320F2812 DSP to prove the effectiveness of the proposed tool. In addition to reference frame theory, another digital signal processor (DSP)-based phasesensitive motor fault signature detection is presented in the following chapter. This method has a powerful line current noise suppression capability while detecting the fault signatures. It is experimentally shown that the proposed method can determine the normalized magnitude and phase information of the fault signatures even in the presence of significant noise. Finally, a signal processing based fault diagnosis scheme for on-board diagnosis of rotor asymmetry at start-up and idle mode is presented. It is quite challenging to obtain these regular test conditions for long enough time during daily vehicle operations. In addition, automobile vibrations cause a non-uniform air-gap motor operation which directly affects the inductances of electric motor and results quite noisy current spectrum. The proposed method overcomes the challenges like aforementioned ones simply by testing the rotor asymmetry at zero speed.Item Robust Condition Monitoring and Fault Diagnosis of Variable Speed Induction Motor Drives(2012-02-14) Choi, SeungdeogThe main types of faults studied in the literature are commonly categorized as electrical faults and mechanical faults. In addition to well known faults, the performance of a diagnostic algorithm and its operational reliability in harsh environments has been another concern. In this work, the reliability of an electric motor diagnosis signal processing algorithm itself is studied in detail under harsh industrial conditions. Reliability and robustness of the diagnosis has especially been investigated under 1) potential motor feedback error; 2) noise interference to a diagnosis-relevant system; 3) ease of implementation; and 4) universal application of diagnostic scheme in industry. Low cost and flexible implementation strategies are also presented. 1) Signature-based diagnosis has been performed utilizing the speed feedback information which is used to determine fault characteristic frequency. Therefore, feedback information is required to maintain high accuracy for precise diagnosis which, in fact, is not the case in a practical industrial environment due to industrial noise interferences. In this dissertation, the performance under feedback error is analyzed in detail and error compensation algorithms are proposed. 2) Fault signatures are commonly small where the amplitude is continuously being interfered with motor noise. Even though a decision is based on the signature, the detection error will not be negligible if the signature amplitude is within or close to the noise floor because the boundary noise level non-linearly varies and, hence, is quite ambiguous. In this dissertation, the effect of noise interference is analyzed in detail and a threshold design strategy is presented to discriminate potential noise content in diagnosis. 3) The compensating procedure of speed feedback errors and electrical machine current noise, characteristics which are basically non-stationary random variables, requires an exhaustive tracking effort. In this dissertation, the effective diagnosis implementation strategy is precisely presented for digital signal processor (DSP) system application. 4) Most of the diagnosis algorithms in the literature are developed assuming specific detection conditions which makes application difficult for universal diagnosis purposes. In this dissertation, by assuming a sinusoidal fault signal and its Gaussian noise contents, a general diagnosis algorithm is derived which can be applied to any diagnostic scheme as a basic tool.