Today, when the industrial environment is changing at a rapid pace, the IoT ( Internet of things) has emerged as one of the most important & advanced technology trends of the era. In order to develop a robust industrial environment, business leaders have started realigning their technology initiatives, in order to be future-ready & process efficient to stay ahead in the race of Industry 4.0.
An Industry 4.0 survey, which was conducted among 361 top-level executives across 11 countries, showed that 94% of these executives reported Digital transformation as the Top Strategic Initiative of their respective organizations.
Industry 4.0 is all about IoT devices, transmitting data and information through complex systems, on a real-time basis, in order to achieve process excellence. With such a huge amount of data being poured in constantly, the monitoring of this data becomes a challenge, that too in an environment when actionable insights are a must for the decision-makers. This can be achieved by developing a robust and reliable Industrial IIoT environment.
In order to achieve reliability and robustness in the IIoT environment, Anomaly detection plays a huge role. Anomaly detection in IoT, refers to identifying the data points or observations that deviate from the predefined behavior of these datasets. The biggest advantage of anomaly detection in IoT is that it prevents potential technical glitches, using the machine learning techniques in order to achieve desired outcomes of the IoT devices, especially in the case of Industrial devices.
Why is IIoT Anomaly Detection Important?
Industrial IIoT has evolved a lot in the previous years. With the emergence of 5G , Advanced sensors, AI Tools & technologies, the Industries have seen enormous growth and potential in terms of Automation & predictive maintenance. The traditional business approaches have come a long way after the introduction of sensor-based technologies in the industries, where the interconnected devices provide real time actionable insights to the relevant stakeholders.
With so much information and data at stake, the next big thing for the Key Decision Makers is to understand the need of the Anomaly detection. Industrial IoT anomaly detection not only can help with the preventive maintenance of industrial devices, but can also make a significant impact on the quality & performance of the devices.
By detecting the patterns, which generally can not be tracked by human observations, the anomaly detection techniques can help predict the early signs of failure in the Industrial machinery systems such as potential equipment failure which will lead to carry out maintenance accordingly. This approach not only helps increase productivity, but also helps in reducing the cost which could occur due to the issues and failures related to data, sensors or machineries.
A survey by McKinsey and company shows that predictive maintenance technology adoption can reduce the maintenance cost by 20%, and unplanned outages by 50% along with an increased life span of the machinery of these industries.
How Can You Detect An Anomaly?
The use of Anomaly detection in IIoT has become significantly important due to its ability to reduce disastrous occurrences along with Cost Saving and ensuring predictive maintenance.
Today when the manufacturing industry is scaling up to newer heights and the organizations are in a race of improving their process efficiency, the stakeholders, irrespective of their roles, should be well versed with the basic understanding of the IIoT terminologies.
Hence, in order to proactively detect the anomalies, the stakeholders must give a thought to the challenges that are needed to be tackled in advance:
- Identifying the exact location and the time of the Anomaly behavior.
- Exact time & location along with the equipment where Anomaly has occurred or will occur.
- Predictive maintenance of the tools/equipment in order to achieve optimum performance.
Thanks to the latest tools & Technologies, Real time data sensors and Industrial IoT devices, most of these challenges can be tackled in advance, with minimal human intervention, giving the manufacturers an edge in the IIoT universe.
Speaking of the Anomaly detection techniques, it can broadly be categorized into two parts:
- Supervised Anomaly Detection: The supervised Anomaly detection model classifies future data models by constructing Machine Learning based, Predictive model by passing both labeled and anomalous sample data sets. Support Vector Machine Learning, K Nearest neighbor are some of the commonly used algorithms for this kind of technique.
- Unsupervised Anomaly Detection: Unlike supervised Anomaly detection, this technique involves an unlabeled data set, which means, it assumes that already the data points in the unlabeled data are normal. Hence, only the data points that differ from normal data points are considered as Anomaly. Typically, the percentage of anomalies in the data set is very small in this case.
Since the Industrial IoT consists of multiple sensor devices, transmitting huge amounts of data through multiple sources, it becomes important not only to store and monitor the data, but also to extract meaningful insights on a real time basis. The organizations can choose basis the need and current capabilities, which of these techniques suit them the best.
At Valiance, we have helped an Industrial Giant in Remote Monitoring for Power Plant Monitoring, with our industrial expertise, by deploying an IoT based platform with the help of multiple sensors, which not only resulted in real time analysis of data, but also helped reduce the anomalies upto a great extent using machine learning based web application.
Industrial Anomaly Detection- The Road Ahead:
As the new Business dimensions are shaping up, it has become crucial for the manufacturers to take the steps into the right direction, and be future ready with the Industrial IoT solutions. The ways of doing business have seen a rapid shift and hence being technologically sound is going to be a game changer in the years to come, especially for the industries.
If you want to know more about the best Anomaly Detection techniques considering your infrastructure or looking to fine-tune your IoT infrastructure, you can always get in touch with our experts.