Collaborating Authors

Striking a balance between supervised & unsupervised machine learning


Since the first use of advanced software in asset-intensive industries 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, fuelled by advances in artificial intelligence based on machine learning, is opening 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 machine learning complexities but 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 then alert operators and even prescribe solutions to avoid the impending failure, or at least mitigate the consequences.

The Danger in "Just Data"


When allied with domain knowledge, analytics can be key in finding the sources of uptime losses and margin leakage. However, results can prove sensitive to the context of the data, and sometimes data analysis can produce faulty outcomes. I would like to be able to tell you, (as a CTO of a startup machine learning company once told me), "just give me the data and I will sort out the problems." Unfortunately, it does not work like that. Data analysis techniques including machine learning are portable across industries, domain knowledge is not – and you need both to succeed.

Driving reliability and improving maintenance outcomes with machine learning


In 2017, McKinsey conducted a study on productivity gains driven by technology transformations, such as the steam engine, early robotic technology and advances in information technology. McKinsey sees manufacturing on the brink of the next generation of industrial automation revolution with unprecedented annual productivity growth of between 0.8 – 1.4% in the decades ahead. Advances in robotics, artificial intelligence and machine learning will match or outperform humans in a range of work activities involving fast, precise, repetitive action and cognitive capabilities. To remain competitive, complex industries need to deploy industrial automation more than ever, as intense global competition drives process industries to increase efficiency through reduced operating costs, increased production, higher quality and lower inventories. The highest priority should be to eliminate production losses caused by unplanned downtime and address a $20 billion a year problem for the process industries.

Improving maintenance outcomes with machine learning


Mike Brooks proposes the use of machine learning software to improve plant reliability and to reduce unplanned downtime. There is a significant need to carry out failure prevention using data-driven truths instead of guesstimates, evidenced by the fact that a combination of mechanical and process induced breakdowns account for up to 10% of the worldwide $1.4 trillion manufacturing market, according to a 2012 report from The McKinsey Global Institute. While companies have spent millions trying to address this issue and ultimately avoid unplanned downtime, only recently have they been able to address wear and age-based failures. Current techniques are not able to detect problems early enough and lack insight into the reasons behind the seemingly random failures that cause over 80% of unplanned downtime. This is where using machine learning software to cast a'wider net' around machines can capture process induced failures.



In this special guest feature, Mike Brooks, Senior Business Consultant at AspenTech, discusses how companies can no longer rely solely on traditional equipment maintenance practices but must also incorporate operational behaviors in deploying data-driven solutions using machine learning. For example, a North American energy company was losing up to a million dollars in repairs and lost revenue from repeat breakdowns of electric submersible pumps. In another case, a leading railway freight firm operating across 23 states in the US used Machine Learning to address perennial locomotive engine failures costing millions in repairs, fines, and lost revenue. Companies can no longer rely solely on traditional maintenance practices but must also incorporate operational behaviors in deploying data-driven solutions.