### Use of Smart Maintenance Model in Electric Utility Industry for Power Grids-Part II

Random asset breakdowns take the business
by surprise and makes it suffer with hefty erosion of capital as a significant
amount of Industry capital is in the form of assets. The only way to prevent is
to gauge the pattern of asset breakdowns and predict it before the damage is
done.

* Fig 1: SMART
MAINTENANCE MODEL*

After the data
is assimilated by means of real time monitoring of assets through SCADA, DFR,
IEDs and historical data secured through EAM tool, the next phase is to analyze
the data so as to pick up a trend or pattern of assets breakdowns. The ultimate
goal is the move from reactive (scheduled, break-fix) and preventive
(condition-based, preventive) to predictive maintenance. The data collected so
far by means of real time monitoring and Asset management tool will be analyzed
to spot repetitive patterns and trends based on which predictive actions to safeguard
assets will be taken. Linear Regression analysis is used for predicting the
unknown value of a variable from the known value of another variable. The
variable whose value is to be predicted is known as the dependent variable and
the one whose known value is used for prediction is known as the independent variable. The
model uses the linear equation:

**y = a +bx**

Where y is dependent variable and x is
independent variable. This is also known as the 'Line of best fit'.

A base year is taken ahead of
which the asset breakdown data is plotted to arrive at a scatter diagram. A
hypothetical example will be say to take year 1990 as the base year, so all the
asset data collected since 1990 till date will be used to predict the future
asset breakdown trend. Here we are going to plot the graph taking X as the 'Number
of days from the base date' and Y being the 'Number of times the asset has
broken down'. The value on Y axis will be cumulative and keeps on adding. Say
from base year to 180 days, asset has
broken down 2 times, 360 days from base year asset has broken down 9(2+7) times
and so forth. After plotting the data, a line is drawn in such a way so as the
distance between the line and the coordinates on the graph is minimum, this is
done using Sum of Squared Errors (SSE). This is the 'Line of Best Fit'. With
the help of this line we will predict the probable future asset breakdown time
window. Using the equation arrived at(**y
= 0.0375x - 7.5333**), we can give the value of x (number of days from base date)
and based on the value of x we can find out the value of y which will be the
predicted value. The data plotted should be at least for 5 years so as to pick
a trend. The more the quantity of data in terms of the number of years, the more
reliable and accurate the predictions will be.

The diagram mentioned below shows
the asset breakdown data plotted on a graph called as Scatter Diagram. A line of
Best Fit is arrived using Linear Regression Equation.

** **

Smart Maintenance Model using
Predictive Maintenance by way of Linear Regression is a boon for the electric
utilities where the entire industry is reliant on the proper functioning of
asset. The collection of data using both real time monitoring and historical
data leaves no stone unturned for optimizing asset performance. This gives management
a leading edge ahead of its competitors to manage asset lifecycle costs in a
better way and make informed and judicious decisions.