How to choose a maintenance solution for your plant ?

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How to choose a maintenance solution for your
plant ?
Revolutions are synonymous with disruptions.
Industry 4.0 is nothing different. It demands new
and advanced technologies for manufacturing
plant maintenance and discarding obsolete plant
maintenance processes at a much faster pace. It
sometimes becomes overwhelming to understand and
adopt new technologies as a plant head. So, here,
we have an article to help you choose the right
maintenance solution for your plant in 2022. In
this article, well majorly talk about how you
can choose the right Predictive Maintenance
solution for your plant. But lets start with the
3 basic types of industrial plant maintenance
solutions available in the market. 7 most
important questions to consider before choosing a
Predictive Plant Maintenance solution Predictive
Plant Maintenance Solution comprises equipment
and sensors, gateway, cloud service, and
management to sense, record, and provide
actionable insights on the machines condition.
Artificial intelligence, machine learning, and
IoT always try to yield accurate results. But
before you buy a predictive plant maintenance
solution, consider these 7 critical aspects of
it to decide which predictive maintenance
solution is right for you. Easy-to-Use and
intuitive for everybody The ideal Predictive
Maintenance solution must be easy to use for all,
from onsite plant operators and technicians to
the plant manager plant head. It should be
intuitive and user-friendly to be mainly
accessible to everyone required. If you need a
data scientist every time to decode the insights
provided by this software, then you are set up
for sudden asset failures due to delayed
responses. The right predictive plant maintenance
solution can empower the onsite condition
monitoring/ maintenance teams with the correct
machine data at the right time for successful
plant maintenance assessments with actionable
insights.
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Finding the root cause, not just alerts Some
Predictive Maintenance solutions indicate only
alerts of anomalies, while the others yield
insightful data alerts with what might be causing
them. Those insights can be used to get a 360º
condition of working equipment, and plant
engineers can trace the root cause of the
problems and plan a more effective solution. It
also helps to distinguish the false alerts from
the true ones. For example Just pointing out
an issue with an exhaust fan of a kiln in a
cement plant may lead to 1000 causes, but a
solution that analyzes this further and points to
a loose bearing that may be the cause can lead
to a different level of agility for your
maintenance teams.
Are the outcomes measurable or just
hopeful? Ensure that the maintenance technology
brings you the results in some way or the other.
And the results must be measurable and not
hypothetical, which means you should be able to
calculate the profits that the technology is
bringing against its cost. The average cost per
hour of equipment downtime is 260,000. Look for
a predictive maintenance solution that can save
you the downtime cost and increase profits.
Predictive maintenance can reduce machine
downtime by 30-50 and increase machine life by
20-40. (McKinsey) Usable across assets and
manufacturers A plant usually has various types
of equipment from multiple manufacturers and
suppliers, depending upon the quality and cost.
The Predictive Maintenance solution you are
planning to install must easily integrate and
comply with every piece of equipment in the
plant- regardless of its age, type, and
manufacturer. Having different data collection
mechanisms for different equipment is costly and
leads to entropy silos that obstruct the whole
picture. Technology, along with human
intelligence, functions to streamline complex
processes and increase efficiency, and not the
opposite.
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Experience around process plants Process
manufacturing plants differ from other industries
in various aspects. Predictive maintenance
solutions request historical data to function
reliably, but process plants have limited
historical machine data, making it difficult for
the predictive solution to function properly.
Make sure your vendor has experience working
with process plants to tackle the situation
constructively. Deployment scaling time One of
the most popular hesitation in IoT-driven Plant
maintenance deployments is the time taken to
deploy the solution. If the deployment takes
months, the internal enthusiasm built around the
deployment dies down, and so does the ROI. It
is also essential that the deployment velocity is
maintained when the solution is scaled
up-whether from some machines to the entire plant
or across plants. Look for a predictive
maintenance vendor that can integrate the
solution in your plant and enable working within
a few weeks and not months.
Predictive Maintenance in mining can cause many
benefits direct indirect.
To Know more about How to choose a maintenance
solution https//www.infinite-uptime.com/how-to-c
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