Diagnostics of eccentricities and bar/end-ring connector breakages in polyphase induction motors through a combination of time-series data mining and time-stepping coupled fe~state-space techniques

Abstract

-This paper develops the foundations of a technique for detection and categorization of dynamic/static eccentricities and bar/end-ring connector breakages in squirrelcage induction motors that is not based on the traditional Fourier transform frequency domain spectral analysis concepts. Hence, this approach can distinguish between the "fault signatures" of each of the following faults: eccentricities, broken bars, and broken end-ring connectors in such induction motors. Furthermore, the techniques presented here can extensively and economically predict and characterize faults from the induction machine adjustable speed drive design data without the need to have had actual fault data from field experience. This is done through the development of dual-track studies of fault simulations and hence simulated fault signature data. These studies are performed using our proven Time Stepping Coupled Finite Element-State Space method to generate fault case performance data, which contain phase current waveforms and time-domain torque profiles. Then from this data, the fault cases are classified by their inherent characteristics, so called "signatures" or "fingerprints". These fault signatures are extracted or "mined" here from the fault case data using our novel Time Series Data Mining technique. The dual-track of generating fault data and mining fault signatures was tested here on dynamic and static eccentricities of 10% and 30% percent of airgap height as well as cases of 1, 3, 6, and 9 broken bars and 3, 6, and 9 broken end-ring connectors. These cases were studied for proof-of-principle in a 208-volt, 60-Hz, 4-pole, 1.2-hp, squirrel cage 3-phase induction motor. The paper presents faulty and healthy performance characteristics and their corresponding so-called phase space diagnoses that show distinct fault signatures of each of the above mentioned motor faults.

Electric motor defects diagnosis based on kernel density estimation and Kullback–Leibler divergence in quality control scenario

“…The benchmark uses a Time Stepping Coupled Finite ElementState Space modelling approach to generate current signals for induction motors as described in Bangura et al (2003). The simulation dataset consists of twenty-one different motor conditions, which are one related to healthy motor, ten related to broken bars and ten related to broken connectors.…”

Determination of Number of Broken Rotor Bars and Static Eccentricity Degree in Induction Motor under Mixed Fault

“…Therefore, it seems that transient analysis of a faulty induction motor using the winding function is invalid . In Elkasabgy et al (1992), Bangura and Demerdash (1999), Bangura et al (2003), Thomson (1998), Salon et al (1992), Tenhunen (2005), and Faiz et al (2008), the finite element method (FEM) has been used to model the broken rotor bar and eccentricity in induction motor. In Fiser (2001) and Elkasabgy et al (1992), the time-stepping finite element (TSFE) method has been used to model the broken rotor bar induction motor in which broken rotor bar current has been taken to be zero; this means a considerable increase of the current in the adjacent bars.…”

“…Such an assumption is not true due to inter-bar current. In Bangura and Demerdash (1999) and Bangura et al (2003), the TSFE coupled state-space (TSFE-SS) method has been employed to model a broken rotor bar and induction motor and motor under static and dynamic eccentricity. However, convergence of the TSFE-SS solution depends on the suitable estimate of the initial values of the current and rotor angular position.…”

“…In Fiser (2001) and , magnetic field analysis is used to detect the broken rotor bars and mixed fault. In Bangura and Demerdash (1999) and Bangura et al (2003), the developed torque of the motor has been utilized for fault detection. In Faiz et al (2008), the faulty induction motor has been analyzed through current, torque, and speed signals.…”

“…However, in the use of the indices introduced in Fiser (2001) and , the influence of disturbance, noise, and electromagnetic interference (EMI) waves must be taken into account. In Bangura and Demerdash (1999) and Bangura et al (2003), the gyration radius has been employed for diagnosis of broken rotor bars and eccentricity faults, and the index used is usable only for torque signal that has rapid changes versus time. On the other hand, the torque for fault diagnosis is considered as an invasive technique.…”