Instrumentation & Measurement Magazine 24-2 - 103

Fig. 2. Different pre-processing techniques. (a) Changing the image of silicone rubber insulators of different hydrophobicity classes to the grey scale [2] (© 2013,
IEEE, reprinted with permission); (b) Denoising a PD RF signal using wavelet based denoising algorithm, based on [3].

render the ability of the ML algorithm to extract any useful information from the captured signals. Denoising algorithms,
image enhancement techniques and data anomaly rejection
are examples of data pre-processing techniques that are usually applied before any further use of the data. Two different
examples that show two different pre-processing techniques
are depicted in Fig. 2. In the first example, regular images are
transformed to a gray scale where an edge detection algorithm will be applied. In the second example, a Wavelet based
denoising algorithm is applied to remove noise from the RF
partial discharge signal. To make sure that all the features
have the same significance, before any feature selection, it is
very important that the scales of all features remain the same.
Hence, each feature individually is converted into a number
between zero and one. This is done by min-max scaling, that
is, for each reading the minimum value is subtracted, and the
result is divided by the maximum value of that feature vector.

Feature Extraction
Since the raw captured signals cannot directly be fed to the ML
algorithm, some information needs to be extracted from these
signals. Recently, the application of deep learning does not require this step as the images are fed directly to the classifier, and
the deep learning algorithm like convolutional neural network
(CNN) can directly extract the distinct features. However, the
focus of this paper is about application of ML and not deep learning algorithms. The features can be classified by the following:
◗◗ Statistical features: in addition to the mean and variance,
among the most used statistical parameters are kurtosis
and skewness. Statistical features can be either extracted
from raw signals or certain frequency bands.
◗◗ Time domain features: when the captured signal is in the
form of pulses like partial discharge signals, different
time domain features like rise time, pulse width and pulse
repetition rate are utilized.
◗◗ Spectral features: this is the most common used features
that include Fourier transform, short Fourier transform
and Wavelet transform.
April 2021	

Prior to the application of the ML algorithm, it is usually not
known which features are best to be used in the ML algorithm,
and hence, a trial and error procedure is usually required. Usually, it is a good practice to start with the simple features like
the statistical ones before moving to more complicated features like frequency bands extracted from Wavelet transform.
Moreover, other features that are not contained in the measured signals may also be required. Examples of these features
may include the distance between the sensor and the insulator,
the temperature and humidity.

Feature Reduction
Usually the number of features extracted from the previous
step is extremely large. Some of these features are redundant
and some are not useful, which will impact the efficiency and
prediction accuracy of the ML algorithm. So, it is of paramount
importance to reduce the size of the feature vector without influencing the prediction accuracy of the used ML algorithms.
Different features reduction techniques have been used like
stepwise regression and principal component analysis (PCA).
Stepwise regression is a common feature extraction method
that selects the input feature vector depending on their statistical significance. Stepwise regression can be of great value
when redundant input features of the prediction model occur,
which can cause a reduction in the prediction efficiency. Applying stepwise regression analysis requires measuring the
statistical significance of the model using the partial F-statistic parameter. On the other hand, PCA projects the data on a
lower dimensional feature space by using an orthogonal transformation based on maximization of variance. The resulting
dimensions are reduced in number with respect to the total
number of features and are also orthogonal (have no overlap)
to each other.

Application of Machine Learning Algorithm
Application of ML can be used to solve either a regression or
a classification problem. In the regression problem, the objective is to predict the actual value of the measured phenomena

IEEE Instrumentation & Measurement Magazine	103



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