Pedro Rodriguez

Detection of stator winding fault in induction motor using fuzzy logic

The online monitoring of induction motors is becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose induction motor faults. This work presents a reliable method for the detection of stator winding faults (which make up 38% of induction motor failures) based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. The fuzzy system is based on knowledge expressed in rules and membership functions, which describe the behaviour of the stator winding. The finite element method (FEM) is utilised to generate virtual data that support the construction of the membership functions and give the possibility to online test the proposed system. The layout has been implemented in MATLAB/SIMULINK, with both data from a FEM motor simulation program and real measurements. The proposed method is simple and has the ability to work with variable speed drives. The fuzzy system is able to identify the motor stator condition with high accuracy. This work is an example of the fusion between soft and hard computing.

Patterns of Guillain-Barré syndrome in children Results from a Mexican population

Background: Guillain-Barré syndrome (GBS) is an acute, immune-mediated flaccid paralysis frequently associated with Campylobacter infection. Of two predominant GBS subtypes, a demyelinating subtype (acute inflammatory demyelinative polyneuropathy [AIDP]) predominates in the United States and Europe, and axonal subtype (acute motor axonal neuropathy [AMAN]) is the predominant form in China. Previous clinical studies suggested that AMAN also occurs in Mexican children. The purpose of this study was to describe the subtypes of GBS in children from Mexico City. Methods: We prospectively studied 121 children admitted to two pediatric hospitals in Mexico City from 1996 to 2002. Clinical histories were obtained, electrophysiologic studies were performed to determine GBS subtype, and microbiologic studies were performed. Results: Of the 121 children, 46 had AMAN and 32 had AIDP. The male to female ratio was 1.3 for AMAN cases (mean age = 6.3) and 3.0 for AIDP cases (mean age = 7.0). There was a strong seasonal distribution of AMAN cases in July to September. Children with AMAN, but not AIDP, had worsening of illness during hospitalization as judged by peak severity scores. Vomiting was more likely in AIDP (28.1%) vs AMAN (6.5%) (p = 0.012) and diarrhea was more common in AMAN (32.6%) than AIDP (12.5%) (p = 0.06). IgG anti-GM1 antibody titers were higher in patients with AMAN vs AIDP (p = 0.067). Anti-GD1a antibodies were equally present in both groups. Anti GQ1b titers were higher in AMAN vs AIDP (p = 0.009). Campylobacter antibody responses were positive in 44.1% of patients with AMAN and 37.0% of patients with AIDP. Twenty patients (14 = AMAN, 6 = AIDP) had positive stool cultures for C jejuni. Two serotypes, HS:19 and HS:41, accounted for 6 of 10 Campylobacter isolates available for serotyping from these cases. Conclusions: This study confirms that acute motor axonal neuropathy is an important Guillain-Barré syndrome subtype in Mexican children, is associated with diarrhea, and occurs seasonally. GLOSSARY: AIDP = acute inflammatory demyelinative polyneuropathy; AMAN = acute motor axonal neuropathy; AMSAN = acute motor sensory axonal neuropathy; GBS = Guillain-Barré syndrome; HS = heat stable; INP = Instituto Nacional de Pediatria; HPL = Hospital Legaria.

Comparative investigation of fault indicators for synchronous machine failures

Ideal condition of a synchronous machine and subsequent fault models representing rotor eccentricity of different types as well as inter-turn short circuits in stator and rotor are created with finite element method simulation for a specially designed SM. This machine has provisions of recreating the same faults made in the simulations by suitable changeover of customized end-shields and sleeves in the shaft assembly for various eccentricity faults or introducing short circuits through accessible terminals of the stator and rotor windings. The fault indicators for characterizing such failures are identified and are measured through experiments. Clear difference obtained between separate fault signatures help identifying and discriminating rotor and stator related failures. With fair understanding of geometry, physical dimensions and the ability to model the complex portions of healthy as well faulty conditions, such a concept helps to create a know-how to detect different failures, well before a catastrophic event takes place.

Signatures of Electromechanical Faults in Stress Distribution and Vibration of Induction Motors

This paper develops a method for determining the signatures of electromechanical faults in the airgap stress distribution and stator vibration of induction machines. Two types of induction motors are analyzed; a 2-pole pairs, star connected and an 1-pole pair, delta connected motor. The radial electromagnetic stress distribution along the airgap is calculated and developed into double Fourier series in space and time. The computations show the existence of low frequency and low order stress distributions acting on the stator of the electrical machine when it is working under faulty conditions. These stress waves are able to produce forced vibration in the stator surface. The simulation results are corroborated by vibration measurements. Modal testing is also carried out to determine the natural frequencies. The measurements and simulations show that low frequency components of the vibrations can be used as identifiable signatures for condition monitoring of induction motors. The patterns presented by the faults allow to discriminate between them.

Field current signature analysis for fault detection in synchronous motors

Often encountered driving critical applications, synchronous machines (SM) require reliable condition monitoring systems. In this paper, the advantages of using the field winding current of a synchronous motor as an early indicator of machine faults are demonstrated. Analysis is carried out, both through simulation, via the use of finite element methods (FEM), and through experimentation, using a specially-designed synchronous machine which allows various fault conditions to be investigated such as eccentricities and short-circuits. It is established from the comparison of the spectra, between healthy and faulty field current data, that the field winding current is well suited for fault detection as well as discrimination among the common faults in SMs. Further, it is shown that field winding current is quite sensitive to any problem of the stator, permitting the detection of stator short-circuits at a very early stage. Thus, it is concluded that an improved condition monitoring and protection system for SMs may be achieved if the field current is effectively monitored.