Simulations have become an integral part of each and every domain today. From product

design to strategy development, simulations give the user an end-to-end understanding of

how each scenario would play out. The insight that simulations offer can help kickstart and

reinforce future iterations of the product or strategy.

As the types of simulations are as vast as the applications themselves, we will be talking

about computer simulations. This century has been a good one for computers. The past

decade has seen massive improvements in the processing power of computers. This

enables industries across the board to run complex simulations without having to invest

heavily in a capable system. Simulation software allows users to design, visualize and

interact with a digital version of the product. The user can also constrain the model and test

it’s limits without ever manufacturing a single component. This not only saves time and

resources, but also ensures a quality product from the get-go.

Simulations are just as important in Condition Monitoring. Condition monitoring is a practice

through which data from various machines is captured using sensors and analysed to

generate insights about the machine’s health. This data is key to not only understanding a

machine’s health, but also the health of the entire manufacturing plant.

So how exactly do simulations tie into condition monitoring? A digital model of the machine

or plant that is being monitored can be subject to different scenarios just as any other

product would. A digital version of the machine can draw its operating parameters from the

sensors that capture data from the machine and behave like it. This behaviour can be

expanded to see how each component is interacting with the other, how each component

functions under different conditions and also how it approaches the end of its life cycle.

Simulations can help mimic the behaviour of any machine thereby making it the base in

predicting how a machine will behave. Simulations combined with software algorithms can

give users the ability to not just understand their machines and their plant, but also to predict

if and when a machine is going to fail. Knowing when a machine is about to fail also presents

opportunities to simulate different load conditions and configurations to determine if the

machine’s life cycle can be extended. It can help determine when a machine will fail if it is

operated in its present condition, what might happen if a maintenance cycle is skipped, how

the failure of each and every component will affect its overall performance.

As the software algorithms evolve over time and the simulation models increase in their

fidelity, the overall productivity of a plant will peak.

Condition monitoring systems backed by simulations can drastically reduce that potential

unplanned downtime that an industry may face due to a catastrophic failure.

Internet of Things(IoT) or Industrial Internet of Things (IIoT) is just a simple but ingenious

combination of simulations and software development to not just connect machines to a user

but to give them the answers to the how, what, when, why and where of the plant at any

given time.

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