Machine Learning: The catalyst to unlock the power of IIoT
The Industrial Internet of Things has already started changing the way businesses have been handling their operations or at least has made them comprehend its potential, showing what it can do for them. But we are yet to realize the real value of IIoT, which can be gained by blending it with machine learning.
Looking at the way Internet of Things is advancing and impacting various verticals, the maxim “experience makes a man perfect” seems to be true for machines as well. Like humans, machines too can develop abilities to operate efficiently by taking decisions based on their experiences; this realization has led to the invention of machine learning. As machines work in varying conditions, they collect data from different experiences. Machine learning is all about providing machines with human-like decision making capabilities by using the collected data and analyzing it to make machines self-regulating and productive.
We all had our hands on machine learning in one or the other way, from the ecommerce sites that recommend products that “we might like” based on our past purchases, to banks that detect fraudulent activities based on our spending patterns. Now is the time when machine learning can demonstrate its real value by amalgamating with the Industrial Internet of Things.
Some of the ways in which machine learning can provide value with the Industrial Internet of Things are:
Predictive maintenance
IoT will not function as expected if not combined with intelligence and machine learning. Predictive maintenance (PdM) is one of the most applicable areas in which machine learning can be applied to give more power to the industrial sector. PdM is basically a failure inspection strategy that makes use of existing data and models to predict the behavior of any connected equipment, allowing companies to map a better maintenance strategy.
The most fitting maintenance plan is to use condition-based monitoring to detect any abnormal conditions by analyzing real-time data that is collected through a range of sensors. Once you have a condition monitoring system in place, machine learning plays a huge role in predicting machine failures. This can be done after parsing through a series of data points that help in identifying the factors that are directly or indirectly affecting the machine status.
Demand forecasting
When dealing with ‘Big Data’, a lot of people wonder if machine learning techniques can be used to create larger forecasting models. Organizations can predict demand on any day or any time in the future, by deriving insights using historical demand data, weather patterns, regions, events, population, and other relevant information.
Looking into the specific use case of demand forecasting, machine learning algorithms and techniques can be used in complex scenarios, making planners capable of forecasting difficult situations with higher accuracy. Machine learning takes a broad range of data, leverages all the knowledge, skills, and experience that planners and experts have to offer, and then provide a forecast on the upcoming consumer demands.
Anomaly detection
Anomaly detection is used by organizations to detect events or items that do not match with other items in the dataset or do not conform to an expected pattern. Using machine learning, these organizations can significantly enhance the speed of anomaly detection, thus improving the overall efficiency, equipment active hours, and reducing costs.
Machine learning can solve intrusions, by keeping a close watch on the perturbations of normal behavior that may indicate a presence of defects, induced attacks, or faults. Considering the power machine learning brings to IIoT, it translates into smoother operations while minimizing machine downtimes.
The Bottom Line
Data is the new oil, and with this in mind, analyzing it to gain valuable insights is what businesses are focused on. Because IoT and machine learning rely on data flows and sophisticated approaches to deliver business value, organizations that are not willing to adopt them will be relegated to low-value and lower revenue.