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 artificial intelligence based on machine learning, 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 machine learning but also be prepared to act on it and take advantage.
After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behavior by recognizing complex data patterns and uncovering the precise signatures of degradation and failure.
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 machine learning, a specific method of learning patterns of performance or behavior using clustering techniques,” said Mike Brooks, Senior Director at Massachusetts-based asset optimization software company AspenTech.
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 unsupervised learning may be fraught with errors and always requires human intervention.
“It is good at detecting correlations but less effective at working out causation. Unaided machine learning may find correlations that can be complete nonsense, such as the meaningless but the true correlation between reduced highway deaths in the US and the number of tonnes of lemons the country imports from Mexico,” he said.
Correlation is not the same as causation
When unsupervised machine learning detects an anomaly, Brooks said the change in behavior patterns could be just a new operating mode, or it could be an impending failure.
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 machine learning learn and adapt, effectively ensuring that moving forward it always provides analysis the business can trust.
After all, he added that correlation is not the same as causation, so machine learning needs human guidance to learn properly.
For example, he said that voice recognition technologies use machine learning, but cannot learn without help. The technology assessments need to be highly-stewarded by humans, who intercept unresolved phrases and apply translations to assist learning techniques.[…]
read more – copyright by www.techradar.com
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.
copyright by www.techradar.com
To stay one step ahead of the pack, businesses not only need to understand the complexities of machine learning but also be prepared to act on it and take advantage.
After all, the latest machine learning solutions can determine weeks in advance if and when assets are likely to degrade or fail, distinguishing between normal and abnormal equipment and process behavior by recognizing complex data patterns and uncovering the precise signatures of degradation and failure.
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 machine learning, a specific method of learning patterns of performance or behavior using clustering techniques,” said Mike Brooks, Senior Director at Massachusetts-based asset optimization software company AspenTech.
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 unsupervised learning may be fraught with errors and always requires human intervention.
“It is good at detecting correlations but less effective at working out causation. Unaided machine learning may find correlations that can be complete nonsense, such as the meaningless but the true correlation between reduced highway deaths in the US and the number of tonnes of lemons the country imports from Mexico,” he said.
Correlation is not the same as causation
When unsupervised machine learning detects an anomaly, Brooks said the change in behavior patterns could be just a new operating mode, or it could be an impending failure.
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 machine learning learn and adapt, effectively ensuring that moving forward it always provides analysis the business can trust.
After all, he added that correlation is not the same as causation, so machine learning needs human guidance to learn properly.
For example, he said that voice recognition technologies use machine learning, but cannot learn without help. The technology assessments need to be highly-stewarded by humans, who intercept unresolved phrases and apply translations to assist learning techniques.[…]
read more – copyright by www.techradar.com
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