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.
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.
“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.
Human involvement needed
Similarly, Brooks said that credit card fraud detection needs help to learn to recognize spending behavior. The credit card company might ask “Are you attempting to purchase an air ticket in Paris?” The credit card issuer uses machine learning to understand your normal spending patterns and now recognizes an abnormal event.
“A simple input of yes or no characterizes the detected anomaly and ensures that the technology learns to recognize any future spending as normal or fraud,” he said.
Brooks said that supervised machine learning, similarly, needs human involvement to work effectively and it requires an individual to declare an event and the time and date it occurred.
Then, he said the technology must learn the signature of the precise patterns that lead to that event, which in the asset-intensive industries could, for example, be a machine failure due to an exact cause such as bearing failure.
“The technology learns and calibrates the precise degradation and failure pattern and then tests new incoming data streams to find exact pattern recurrences, to then alert well before the failure occurs, allowing the action to avoid the failure or provide time to arrange a timely repair before major damage occurs. The results are much lower maintenance costs and more up-time producing valuable products,” he said.
Morris also urged business owners who run the day-to-day operations of the company across asset-intensive industries to start taking advantage of the many benefits that machine learning can already bring in terms of running their facilities more efficiently and optimizing asset performance.
In today’s crowded marketplaces, he said there is a window of opportunity to use machine learning to predict asset performance, drive business advantage and develop a competitive edge.
“Unsupervised machine learning can take businesses part of the way by helping to identify anomalies in an asset or plant performance, but organisations must be aware that to perform at optimum levels, machine learning needs human guidance and intelligence. It still needs guide-rails to find and solve the right problems,” he said.
Asset-intensive businesses need to act on this understanding now, he said and added that those that do will be best placed to take advantage of the new era of machine learning and ensure that by measuring actual patterns of asset behavior and extracting real insight and automatically developing foresight, they can optimize asset performance across their entire operations.