BigData_ass6
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School
Southern New Hampshire University *
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Course
260
Subject
Industrial Engineering
Date
Dec 6, 2023
Type
docx
Pages
3
Uploaded by jfoll
Katie Letson
DAT-260
Module 6 assignment
10/6/2023
I chose the category predictive maintenance.
I picked a heavy equipment company that
would use a prognostics algorithm to predict the machines (RUL) remaining useful life or time
-to-failure prediction by analyzing the machines current state. Unsupervised learning algorithms
use clustering and anomaly detection as well.
Machine learning possess the ability to handle
multivariate, high dimensional data and can extract hidden relationships within data in complex,
dynamic, and chaotic environments[ CITATION Zek20 \l 1033 ]. Machine learning and
modeling are used by prognostics to predict the future value of condition indicators.
Doing this
enables this company to be alerted to diagnosed faults and equipment failure estimations of
occurrence.
This allows the company to plan maintenance before a problem occurs, cutting
down on expensive equipment or part replacement.
The company will be able to reduce
downtime, increase efficiency of the operation, and manage inventory more accurately.
It will
also keep the company from costly stand-still time because they are waiting for parts, equipment,
or for a mechanic to fix broken equipment.
This can change a company that is failing or struggling to survive into a thriving
company that has a competitive edge.
Customers will notice less incidents where timing is of
importance.
They will want to come back to this company repeatedly for this service because it
will gain a reputation for reliability, little wait time, and maximum efficiency.
Using smart
sensors, your understanding of the industries interworking’s, actuators, radio frequency tags, and
many more can
enhance industry processes and in turn elevate customers satisfaction and
security[ CITATION Pri23 \l 1033 ].
All these aforementioned components are networked
together to collect data, exchange, and analyze.
They will merge historical performance data,
engineering specs, and real-time analytics[ CITATION Mic201 \l 1033 ].
Then based on this
specific to the user alarms based on conditions and alerts are set up to allow the user to fix this
problem long before it ever occurs if it even does.
There are many tools that AI may use such as ultra sonic analysis, infrared analysis, oil
analysis, motor circuit analysis, vibration analysis, or laser shaft alignment.
Usually a program
will be set up
by identifying critical assets, creating a data base with historical data, analyzing
failure mode, and then the failure predictions are made.
Then you will deploy the predictive
maintenance technology to pilot equipment groups to validate the program.
In the past people had very little idea when a machine was going to have an issue or
breakdown and it left companies with costly downtime.
It also caused customers to have to wait
for mechanical work, adjustments in time frame for job to be completed, and changes in cost
from original estimate.
Theses are only a few examples and there are many more.
Now with ML
and IoT being utilized in companies to predict maintenance needed before an event or occurrence
of breakdown ever takes place it has helped many businesses to optimize their company
performances, cut cost, better utilize time and employee’s workload, and see a rise in repeat
customers and customer satisfaction.
There are so many benefits to utilizing machine learning
because it can identify clusters, patterns, and trends using historical data and sensors that indicate
many helpful, efficiency boosting, and cost cutting processes or maintenance is needed.
This
eliminates the guess work and cuts out downtime and wait-time keeping a company running at
maximum production efficiency.
It is a revolutionary tool that has changed the industry
completely to optimize operations and I foresee this developing more and being used much more
in the years to come.
References
Apple, S. (2019). Smarter, healthier, more productive: how Uptake is revolutionizing heavy industries
with AI+IoT.
Havard Business School
.
Michael Horrell, L. R. (2020). Data Science in Heavy Industry and the Internet of Things.
Havard Data
Science Review, 2
(2).
Sinha, P. (2023). Introduction To Industrial Digitalization.
Evalueserve
.
Zeki Murat Çınar, A. A. (2020). Machine Learning in Predictive Maintenance towards Sustainable Smart
Manufacturing in Industry 4.0.
Sustainability, 8211
, 1-42. doi:10.3390/su12198211
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