Since the first use of advanced software in asset-intensive industries such as utilities, airports, ports, road, rail and mining more than four decades ago, manufacturers have been on a journey to transform their businesses and create added value for stakeholders.
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Today, a fresh generation of technologies, fueled by advances in based on , is opening up new opportunities to reassess the upper bounds of operational excellence across these sectors.
To stay one step ahead of the pack, businesses not only need to understand the complexities of
After all, the latest
Anomaly detection
“They can alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences. The software constructs are autonomous and self-learning. They demonstrate a capability known as unsupervised
Moreover, he said that it can be used to understand ‘normal’ operational behavior, based on signals from sensors on and around machines, and once the behavioral patterns are learned, analysis of new data can help detect deviations from the norm, called anomalies, highlighting mechanical issues and process changes that affect specific pieces of equipment.
However, he said the downside is that anomaly detection based on
“It is good at detecting correlations but less effective at working out causation. Unaided
Correlation is not the same as causation
When unsupervised
A human must take a look at the machine and decide which of the options is correct, he said, but such manual intervention can then help
After all, he added that correlation is not the same as causation, so
For example, he said that voice recognition technologies use
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