Method followed in the project

  

Methodology

The proposed SITF diagnosis scheme are:

a)      Gathering of data

The collection of data is for the three-phase current signatures for the Healthy and Faulty IM which contains SITF. The data records for the faulty IM at different loads with different severity level. Furthermore, data samples to be analyzed are extracted from simulation.

b)      Data pre-processing

Data cleaning and smoothing is done at this stage which is necessary to ensure that the dataset is free of any unlogged values.




Figure 1: Data Pre-Processing block scheme

c)      Exploratory analysis

Data cleaning, exploration and dimensionality reduction of datasets is done in this stage. It is the way of reducing higher dimensions dataset into lesser dimensions dataset which ensures that is provides similar information. Principle component analysis (PCA), and pareto charts techniques will be used to visualize the obtained datasets.

d)      Model development

MATLAB and Python shall be utilized to obtain the desired models. Also, training of the data is done in this stage, on non-neural and neural classifier.

e)      Classification of faults

From the dimensionality reduction techniques mentioned in (c), the best classification tools, non-neural and neural based will then be chosen to analyze the faults of SITF. Different state of algorithms will be used for comparison. Furthermore, the results will then be compared to view the model performance.   

 

Figure 2: Proposed scheme for STIF


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