Analytical Model of Induction Machines with Multiple Cage Faults Using the Winding Tensor Approach

Area of Science:

• Electrical Engineering
• Mechanical Engineering
• Industrial Maintenance

Background:

• Induction machines (IMs), particularly squirrel cage IMs (SCIMs), are vital for industrial power but prone to cage faults causing costly breakdowns.
• Current diagnostic techniques require accurate and fast IM models, but traditional methods like finite elements are computationally expensive, and analytical models struggle with asymmetry.

Purpose of the Study:

• To develop a novel analytical modeling method for IMs with asymmetrical cage windings.
• To simplify the complexity of modeling multiple cage faults in IMs.
• To provide a foundation for faster and more accurate fault diagnosis in condition-based maintenance (CBM) systems.

Main Methods:

• A tensor-based approach is proposed for analytical modeling of IMs.
• Routine tensor algebra is applied to derive parameters of faulty IM models from healthy ones.
• The method's theoretical explanation is validated through the diagnosis of a commercial IM with multiple cage faults.

Main Results:

• The tensor-based approach significantly reduces the complexity of modeling asymmetrical cage windings in IMs.
• The method allows for the efficient derivation of faulty IM model parameters from healthy models.
• Validation confirmed the effectiveness of the approach in diagnosing multiple cage faults.

Conclusions:

• The proposed winding tensor approach offers a computationally efficient and accurate method for modeling IMs with cage faults.
• This facilitates the development of improved on-line fault diagnosis systems for industrial CBM.
• The technique enhances the reliability and reduces downtime of critical industrial machinery.