José M. Bossio

Separating Broken Rotor Bars and Load Oscillations on IM Fault Diagnosis Through the Instantaneous Active and Reactive Currents

A new method for broken rotor bars and load oscillation diagnosis on induction motors is presented. The proposed strategy is based on the decomposition of the stator currents into their instantaneous active and reactive current components. Such components allow the decoupling of the effects produced by rotor asymmetries from those produced by oscillating loads. This allows not only the proper fault detection but also a correct fault diagnosis. Simulation and experimental results, both from laboratory and industrial cases, are presented to validate the proposal.

Evaluation of harmonic current sidebands for broken bar diagnosis in induction motors

Effects of rotor faults on stator currents in induction motors are studied in the present work. Particularly, the effects of such faults on the sidebands at fundamental and harmonics current components are analyzed. A multiple-coupled circuit model determines the variation of amplitude of these sidebands as a function of the number of broken bars, load and motor-load inertia. The results obtained from this analysis allow improving the rotor fault diagnosis techniques and identifying and separating rotor faults from others. Simulation and experimental results are presented to validate the proposal.

Broken bar detection in single-phase reciprocating compressors

Broken rotor bar detection in induction motors using current signature analysis gives good results when the motor load is almost constant. However, if the load torque is oscillating, or it is a function of the rotor position, the current spectrum will contain spectral components which coincides with the components produced by rotor faults. Thus, rotor fault detection in these loads, such as compressors and other reciprocating loads, is very difficult. A new strategy for broken rotor bar diagnosis in single-phase induction motors is presented in this paper. This strategy is based on the analysis of the auxiliary winding voltage. The proposed strategy allows the diagnostic of rotor faults even with pulsating load torque. Simulation and experimental results are presented.

Angular misalignment in induction motors with flexible coupling

The problem of angular shaft misalignment in motors-load systems coupled through flexible couplings is analyzed in this work. A model for the analysis and diagnosis of angular misalignment in induction motors is presented. It allows studying the angular shaft misalignment effects over the motor torque, instantaneous power and currents through dynamic simulation. Additional effects introduced by the mixed eccentricity produced by the shaft misalignment are also analyzed through experiments. The results show that angular misalignment can be detected from electrical motor variables, but its correct diagnosis is difficult. The study is completed by vibration and thermography analysis.

Self-organizing map approach for classification of mechanical and rotor faults on induction motors

Two neural network-based schemes for fault diagnosis and identification on induction motors are presented in this paper. Fault identification is performed using self-organizing maps neural networks. The first scheme uses the information of the motor phase current for feeding the network, in order to perform the diagnosis of load unbalance and shaft misalignment faults. The network is trained using data generated through the simulation of a motor-load system model, which allows including the effects of load unbalance and shaft misalignment. The second scheme is based on the motor’s active and reactive instantaneous powers, in order to detect and diagnose faults whose characteristic frequencies are very close each other, such as broken rotor bars and oscillating loads. This network is trained using data obtained through the experimental measurements. Additional experimental data are later applied to both networks in order to validate the proposal. It is demonstrated that the proposed strategies are able to correctly identify, both unbalanced and misaligned load, as well as broken bars and low-frequency oscillating loads, thus avoiding the need for an expert to perform the task.

Fault diagnosis on induction motors using Self-Organizing Maps

A scheme for diagnosis and identification of mechanical unbalances and shaft misalignment on machines driven by induction motors is presented in this work. Fault identification is performed using unsupervised artificial neural networks: the so-called Self-Organizing Maps (SOM). The information of the motor phase current is used for feeding the network, in order to perform the fault diagnosis. The network is trained using data generated through the simulation of a motor-load system model. Such model allows including the effects of load unbalance and shaft misalignment. Experimental data are later applied to the SOM in order to validate the proposal. It is demonstrated that the strategy is able to correctly identify both unbalanced and misaligned cases.

Bearing Fault Detection in Wind Turbines with Permanent Magnet Synchronous Machines

A strategy for the detection of bearing faults in variable speed wind turbines with permanent magnet synchronous machines is presented in this paper. The strategy consists on processing the measured vibration signals as well as the machine voltages. From these voltages and using a phase locked loop, the electrical angle is calculated in order to resample vibrations synchronized with rotor position. Then, it is possible to use conventional vibration-based techniques for bearing fault detection even under variable speed operation conditions. Experimental results to validate the proposed strategy are also presented in this work.

Fault detection for variable-speed wind turbines using vibrations and electrical measurements

A technique for detecting faults in variable speed wind turbines with permanent magnet synchronous generators is proposed. A new method for processing vibration signals is proposed based on a resampling of the acquired signals in order to obtain a speed independent vibration spectrum. This new resampling is obtained using a reduced order observer which allows estimating rotor position using voltage and current measurements, without the need of a position sensor. Experimental results including a rotor imbalance fault are presented to validate the proposal.

Fault detection in gear box with induction motors: an experimental study

The failures in reducing speed gearboxes or speed multipliers with gears have traditionally been diagnosed by means of vibration analysis. Recently there has been noticed the possibility of making a diagnosis based on the tracking of the stator current of the motor driving the box or its electromagnetic torque. These variations, unlike vibration studies, have the advantage of being non-invasive. This work aims to evaluate the ability of fault detection and diagnosis from the spectral analysis of the torque. On an actual application, the behavior of the frequencies associated with the fault according to the engine load condition is analyzed. Conclusions about the possibility of detecting failures of different severity over a tooth of a toothed wheel are obtained.

Detección y diagnóstico de problemas de desmagnetización y desbalance mecánico en máquinas síncronas de imanes permanentes

A new strategy for the detection and isolation of asymmetric demagnetization and mechanical imbalance problems in permanent magnet synchronous generators operating at steady-state is proposed in this paper. The strategy is based on measuring the voltage at the midpoint of the phase windings. Particularly, the presence of demagnetization faults is reflected in the midpoint voltage spectrum frequency as sidebands around the fundamental component. On the other hand, in the case of mechanical imbalance faults, these sidebands will be reflected in both, thus allowing the detection and separation from the type of fault present, as well as quantify each.