Erik Leandro Bonaldi

Detection of Localized Bearing Faults in Induction Machines by Spectral Kurtosis and Envelope Analysis of Stator Current

Early detection of faults in electrical machines, particularly in induction motors, has become necessary and critical in reducing costs by avoiding unexpected and unnecessary maintenance and outages in industrial applications. Additionally, most of these faults are due to problems in bearings. Thus, in this paper, experimental bearing fault detection of a three-phase induction motor is performed by analyzing the squared envelope spectrum of the stator current. Spectral kurtosis-based algorithms, namely, the fast kurtogram and the wavelet kurtogram, are also applied to improve the envelope analysis. Experimental tests are performed, considering outer bearing faults at different stages, and the results are promising.

Using rough sets techniques as a fault diagnosis classifier for induction motors

This paper describes the ongoing research on rough sets based classifier applied to induction motors fault diagnosis through motor current signature analysis (MCSA). The results of mechanical failures detection and how a rough sets based classifier is used as a monitoring system using current signature analysis in predictive maintenance are described in this paper.

Predictive Maintenance by Electrical Signature Analysis to Induction Motors

Industries always try to increase the reliability of their productive process. In this context, predictive maintenance performs a fundamental role in order to reach high availability and reliability concerning their pieces of equipment. Predictive maintenance can be understood as the action on the equipment, system or installations based on the previous knowledge about the operation condition or performance, obtained by means of parameters previously determined (Bonaldi et al, 2007).

Induction Motor Efficiency Evaluation Using a New Concept of Stator Resistance

This paper presents an air-gap torque (AGT)-based method for efficiency estimation of induction motors. A new concept of stator resistance that includes the mechanical losses effect is proposed. This new stator resistance is estimated through a particle swarm optimization approach based on the stator flux equations and minimization of torque error at the rated operation point. Then, the obtained stator resistance is used in the AGT equations to estimate the shaft torque and then the efficiency. Moreover, the rotor speed is estimated using induction motor current signature analysis. Thus, the proposed methodology for induction motor efficiency estimation relies only on line currents, line voltages, and nameplate data, being appropriate for in-service applications. Finally, the simulation and experimental results are presented to validate the proposed method at different load conditions.

Real-time determination of the best interval of calculation for moving averages used for DC value extraction in active power filter control methods

In many cases, active power filters based on the i/sub d/-i/sub q/ and p-q control methods can have their dynamic response improved and their computational effort reduced by using moving averages instead of filters for the DC value extraction. An optimal dynamic performance, however, can not be achieved without the knowledge of the shortest calculation interval for the moving averages. The best interval depends on the harmonic content of the load currents and/or supply voltages that will be compensated. This paper presents a method for the instantaneous determination of the shortest calculation interval of the moving averages. The paper describes the strategy and presents the simulations.

A stator flux synthesis approach for torque estimation of induction motors using a modified stator resistance considering the losses effect

This work proposes a methodology for induction motor torque estimation based on the stator flux model. The presented methodology just relies on line voltages, line currents and nameplate data; and it adopts a modified stator resistance, which already comprises the mechanical losses effect. This modified stator resistance is estimated through a Particle Swarm Optimization (PSO) algorithm, which is a kind of artificial intelligence applied to optimization problems. The PSO presented objective function aims to minimize the torque error at the rated operation point. Simulation and experimental results validate the effectiveness of the presented methodology.

Removing the fundamental component in MCSA using the synchronous reference frame approach

The predictive maintenance applied to three-phase-induction motors through the motor current signature analysis is the highlight of the moment. Removing the fundamental component of the stator current signal is an important task in this approach. With this component extracted it is possible to improve the dynamic range of the A/D converter in order to get a more precise digitized signal and improve the failure detection. In general, notch filters are utilized to accomplish this task, but they present problems such as attenuation of some important frequencies located very close to the fundamental component which have information about a specific failure. This paper introduces the synchronous reference frame approach, generally applied to active power filters, to solve the presented problem.

A system for turbogenerator predictive maintenance based on Electrical Signature Analysis

This paper proposes a prototype to detect incipient faults in non-salient pole synchronous generators based on the Electrical Signature Analysis (ESA) technique. The methodology consists in taking measurements of the machine stator voltages and currents and processing the acquired signals. By analyzing the voltages and currents spectra, it is possible to distinguish a faulty condition from a healthy one and to infer the failure type of the generator. This is summarized in a set of ESA failure patterns, which were proved through tests conducted in a custom made scale model laboratory. The custom laboratory setup consists of a small fault injection capable two-pole synchronous generator driven by an inverter-fed induction motor. The proposed prototype is operating in four turbogenerators of a Brazilian power plant. The main advantages of the presented system are the low intrusiveness, the ease of installation and it is economically viable. Moreover, the ESA failure patterns are based on defined frequencies and the structural features of the machine.

Differential Evolution based Air-Gap Torque method approach for induction motor efficiency estimation

This paper proposes a methodology for torque and efficiency estimation of in-service induction motors based on the Air-Gap Torque (AGT) method. The proposed methodology only relies on induction motor stator voltages, currents and nameplate data. A modified stator resistance is estimated including the mechanical losses effect. This modified stator resistance is estimated by using a Differential Evolution (DE) based algorithm. The DE procedure estimates the modified stator resistance by using the stator flux equations, minimizing the torque error at the rated operation point. The estimated stator resistance is applied in the AGT method equations to compute the induction motor torque and efficiency. Simulation tests were performed to validate the proposed methodology.

An air-gap torque based method for efficiency evaluation using pso to estimate a new concept of stator resistance including the losses effect

This work proposes an air-gap torque (AGT) based method for efficiency and torque estimation of induction motors. A new concept of stator resistance including the mechanical losses effect is presented. This modified stator resistance is estimated through a Particle Swarm Optimization (PSO) approach. The proposed methodology only relies on line currents, line voltages and nameplate data, which is appropriate for in-service applications. Finally, simulation and experimental tests were conducted to validate the proposed method at different load conditions.