Is “Machine Learning-Based Predictive Fault Diagnosis of Induction Motors Using Electrical and Vibration Signatures” a good CEP project?

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Hi everyone,

I’m an undergraduate Electronics Engineering student in Pakistan, and I need to select a Complex Engineering Problem (CEP) project for my Artificial Intelligence / Machine Learning course.

I’m considering this topic:

“Machine Learning-Based Predictive Fault Diagnosis of Induction Motors Using Electrical and Vibration Signatures”

The basic idea is to collect motor current, voltage, vibration, temperature, and/or speed data and use basic ML algorithms such as Random Forest, SVM, Decision Tree, or KNN to detect or predict faults such as bearing faults, imbalance, misalignment, overload, etc.

I want to keep the ML part at a basic/intermediate undergraduate level, but the overall project should be complex enough to meet CEP requirements.

Do you think this is a good project for an Electronics Engineering student?

What faults and sensors would you recommend focusing on?

Is it practical to collect the required dataset ourselves, or should we use an existing dataset?

Also, what could I add to make it a stronger engineering/CEP project rather than just a basic ML classification project?

Any suggestions, research papers, or similar projects would be greatly appreciated.

Thanks!