Instrumentation & Measurement Magazine 24-2 - 102

Application of Machine Learning;
the Overall Picture
The process of ML application starts from capturing a signal
that can be either 1D or 2D signal. Usually the collected signal
suffers from noise and/or low magnitude, so it will need preprocessing before applying any ML algorithms. Once the signal
is prepared, different features need to be extracted. These features are usually large in number and need to be reduced to
minimize the computational complexity of the proposed ML
algorithm. Once the input features have been minimized, they
will be used to train and test certain ML algorithms. The machine learning overall picture is depicted in Fig. 1. The details of
each step are provided in the following subsection.

Collecting Data
Different sensors have been used to inspect outdoor insulators.
They can be generally classified into intrusive and non-intrusive sensors. Application of intrusive sensors either requires the
shutdown of power or the ability to physically touch the insulator. An online voltage measurement electrode is an example
of an intrusive sensor. On the other hand, non-intrusive sensors
can collect the data without meeting these two conditions.
Due to their field practicality and safety, the focus of this
paper will be only about non-intrusive sensors. They can be
primarily categorized into the following types:
◗◗ Visual sensors: that include regular, infra-red (IR) and
ultraviolet (UV) cameras.
◗◗ Radiation measurement-based sensors: that include
acoustic emission (AE) sensors and radio frequency (RF)
antenna.

Table 2 - Types of defects that can be
detected by different non-intrusive sensors
Defect type

Sensor Type

Broken (ceramic) or eroded
(non-ceramic) insulator

Regular camera

Damage or corrosion of
hardware

Regular camera

Internal voids

AE sensor, HFCT and RF
Antenna

External cracks

AE sensor, Antenna, HFCT,
regular and IR and UV
camera

Pollution

AE sensors, Antenna, LFCT,
regular, UV and IR camera

◗◗ Current measurement sensors: that are low and high
frequency (LFCT and HFCT) clamp type current
transformers.
The potential problems that can be inspected using these
sensors are summarized in Table 2. While it is evident that
more than one sensor can detect the same problem, the mechanism behind the sensor's ability to detect the defect is different
from one sensor to another. For example, an external crack may
initiate partial discharge that can emit either sound waves, electromagnetic waves, ultraviolet light or heat that can be detected
by AE sensor, RF antenna, UV camera and IR camera, respectively. Moreover, if the crack is visible, a regular camera may be
able to detect it. Nevertheless, the sensitivity of these sensors to
detect the external defect in
the form of partial discharge
depends on different factors
like severity of the defect,
the distance between the
sensor and the defect and
the pollution condition of
the insulator surface. There
is a lack of research in the literature that compares the
efficiency of these sensors to
detect the same defect.
It is important to mention that for the 1D sensor,
compatibility between the
sensor frequency response
and the data acquisition
frequency bandwidth is a
must.

Data Pre-processing

Fig. 1. Steps for application of machine learning in the context of outdoor insulators inspection [1] (© 2020, IEEE,
reprinted with permission).
102	

IEEE Instrumentation & Measurement Magazine	

In many applications, especially field measurement,
collected data suffers from
noise, corrupted or irrelevant signals that may
April 2021



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