This paper studies the application of independent component analysis (ICA) and support vector machines (SVMs) to detect and diagnose of induction motor faults. The ICA is used for feature extraction and data reduction from original features. The principal components analysis is also applied in feature extraction process for comparison with ICA does. In this paper, the training of the SVMs is carried out using the sequential minimal optimization algorithm and the strategy of multi-class SVMs-based classification is applied to perform the faults identification. Also, the performance of classification process due to the choice of kernel function is presented to show the excellent of characteristic of kernel function. Various scenarios are examined using data sets of vibration and stator current signals from experiments, and the results are compared to get the best performance of classification process.

Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors

Vibration energy harvesting in automotive suspension system: A detailed review

The presence of electrical and mechanical faults in the induction motors (IMs) can be detected by analysis of the stator current spectrum. However, when an IM is fed by a frequency converter, the spectral analysis of stator current signal becomes difficult. For this reason, the monitoring must depend on multiple signatures in order to reduce the effect of harmonic disturbance on the motor-phase current. The aim of this paper is the description of a new approach for fault detection and diagnosis of IMs using signal-based method. It is based on signal processing and an unsupervised classification technique called the artificial ant clustering. The proposed approach is tested on a squirrel-cage IM of 5.5 kW in order to detect broken rotor bars and bearing failure at different load levels. The experimental results prove the efficiency of our approach compared with supervised classification methods in condition monitoring of electrical machines.

Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique

Investigation on thermal management performance of PCM-fin structure for Li-ion battery module in high-temperature environment

Development of an e-maintenance system integrating advanced techniques

This paper will present the regenerative braking quantification, design control, and simulation of a hybrid energy storage system (HESS) for an electric vehicle (EV) in extreme conditions. The EV is driven by two 30-kW permanent magnet synchronous motors. The HESS contains a Li-Ion battery and ultracapacitor (UC) storage element sources as well as a dissipative resistor. The UC will be mainly involved in braking and traction modes. The role of the resistor is to protect the dc bus and the battery according to the voltage and current constraints. The sizing of the elements takes into consideration the extreme braking conditions of the vehicle while respecting the Economic Commission for Europe Regulation No. 13 Harmonized. Controllers are being used in order to regulate the various electrical variables of the overall system. A sequential logic controller is also being introduced. The role of the sequential logic controller is to activate the different existing regulation controllers and to ensure the switching between the storage elements depending on the system states. Simulation tests will be performed covering a wide operation range for variable braking load, according to vehicle speed and road type, and extreme braking conditions.

Regenerative Braking Modeling, Control, and Simulation of a Hybrid Energy Storage System for an Electric Vehicle in Extreme Conditions

Comparative analysis of two hybrid energy storage systems used in a two front wheel driven electric vehicle during extreme start-up and regenerative braking operations

Comparison between two braking control methods integrating energy recovery for a two-wheel front driven electric vehicle

In this article a new method is presented allowing the diagnostic of bar or ring rupture of a cage of asynchronous machine. It is shown that a modelling of the asynchronous machine taking into account each bar and each portion of ring of the cage allows to consider the influence of a rupture upon the expression to the instantaneous electromagnetic torque. It has been put up an instrumentation permitting the measurement of this torque. The spectral analysis of the parameter leads to the diagnostic of a cage rupture by discriminating this fault from other faults, particularly mechanical ones. It is also observed that ruptures influence greatly the electromagnetic torque. The peak variation of the significant spectral lines is so important that it allows the detection of a first rupture without the need of a very sophisticated instrumentation.

Asynchronous motor cage fault detection through electromagnetic torque measurement

In this paper, we will represent the optimal traction and regenerative braking current synthesis for an IPMSM using three combined torque control methods for an Electric Vehicle (EV) using Matlab/Simulink®. In order to impose torque at higher speed, the flux should be weakened by injecting negative current in the d- axis. Several methods of reference current generation will be described according to the operating speed, torque reference, voltage and current constraints. The torque/speed characteristics will be decomposed into five zones of operation delimited by three methods of control: Maximum Torque per Ampere, Voltage Current Limited Maximum Torque and Constant Power Region.

Optimal traction and regenerative braking reference current synthesis for an IPMSM motor using three combined torque control methods for an Electric Vehicle

Ahmad Jammal

Papers

Regenerative Braking Modeling, Control, and Simulation of a Hybrid Energy Storage System for an Electric Vehicle in Extreme Conditions

Comparative analysis of two hybrid energy storage systems used in a two front wheel driven electric vehicle during extreme start-up and regenerative braking operations

Comparison between two braking control methods integrating energy recovery for a two-wheel front driven electric vehicle

Asynchronous motor cage fault detection through electromagnetic torque measurement

Optimal traction and regenerative braking reference current synthesis for an IPMSM motor using three combined torque control methods for an Electric Vehicle