While the term "machine learning" generally relates to understanding structures or patterns in data, it can also refer to a very diverse set of activities and techniques. Most of us have experienced machine learning in our everyday lives with natural language processing (Alexa, Siri), image recognition (Facebook, Pinterest), purchase recommendations (Amazon) and search optimization (Google). These approaches generally use many different types of algorithms (e.g., neural networks, decision trees, clustering, support vector machines, etc.)
Industrial operations, on the other hand, need more specialized approaches that can provide actionable insights to reduce downtime as well as improve throughput, operator safety, and product quality. The increased access to operational data, combined with the spread of computing, connectivity, and storage, has created the perfect environment for transforming industrial operations. Operational machine learning may be the branch that makes AI relevant in your real life. Without the aid of data scientists, the combination of machine learning and predictive analytics allows operations teams, to create a great deal of business value.
How does Operational Machine Learning work?
Machine Learning discovers patterns hidden in existing operations data, applies predictive analytics and provides actionable insights, all without requiring data scientists.
What are the benefits of machine learning in Industrial Operations?
Industrial organizations are moving quickly to implement "ready-to-use" machine learning systems to gain competitive advantages and optimize their operations. The core requirement of any machine-learning system in this context is that itâ??s designed to solve the complex problems facing industrial organizations today.
This approach will maximize the value derived from the operations data and the effectiveness of the machine learning investment. The end result is substantial improvements in asset performance, practitioner safety, manufacturing yield, and product quality.
Operational machine learning leverages underutilized operations data, and provides data and insights that can significantly improve uptime, quality performance and safety. Some of the benefits of industrial operations are:
1. 5-10% material cost savings.
2. 10-20% increase in equipment uptime and availability.
3. 20-50% reduction in maintenance planning time.
4. 5-10% reduction in overall maintenance costs.
Here are some use cases across industries of machine leaning
The core requirement of any machine-learning system is its ability to discover multivariate patterns in real time, and correlate them with events to deliver actionable insight. Operational Machine Learning has helped detect the flaws by detecting pre shutdown patterns and alerts for uncontrolled emissions (real time) for the oil and gas operations. For automotive manufacturing Operation machine learning has helped detect deviations in discrete manufacturing and what is the real time quality estimation of welding, other than this quality estimation of batch process and monitor machine health in the field of chemical manufacturing. Nearly all operational systems produce streams or bursts of time-series data in the form of sensor readings, log entries, or activity traces. The ability to better understand the behaviour of these systems using this type of data is of extremely high value, but requires expertise in machine learning thatâ??s focused on multivariate time-series data.
Industrial practitioners can harness underutilized time-series data using machine learning to provide actionable insights that reduce downtime and improve throughput, operator safety, and product quality.