It is true that the Industrial Internet of Things will change the world someday. So far, it is the abundance of data that makes the world spin faster.
Copyright by medium.com
Piled in sometimes unmanageable datasets, big data turned from the Holy Grail into a problem pushing businesses and organizations to make faster decisions in real-time. One way to process data faster and more efficiently is to detect abnormal events, changes or shifts in datasets. Thus, anomaly detection, a technology that relies on Artificial Intelligence to identify abnormal behavior within the pool of collected data, has become one of the main objectives of the Industrial IoT. Anomaly detection refers to identification of items or events that do not conform to an expected pattern or to other items in a dataset that are usually undetectable by a human expert. Such anomalies can usually be translated into problems such as structural defects, errors or frauds.
Examples of potential anomalies:
- A leaking connection pipe that leads to the shutting down of the entire production line;
- Multiple failed login attempts indicating the possibility of fishy cyber activity;
- Fraud detection in financial transactions.
Why is it important?
Modern businesses are beginning to understand the importance of interconnected operations to get the full picture of their business. Besides, they need to respond to fast-moving changes in data promptly, especially in case of cybersecurity threats. Anomaly detection can be a key for solving such intrusions, as while detecting anomalies, perturbations of normal behavior indicate a presence of intended or unintended induced attacks, defects, faults, and such. Unfortunately, there is no effective way to handle and analyze constantly growing datasets manually. With the dynamic systems having numerous components in perpetual motion where the “normal” behavior is constantly redefined, a new proactive approach to identify anomalous behavior is needed.
Statistical Process Control
Statistical Process Control, or SPC, is a gold-standard methodology for measuring and controlling quality in the course of manufacturing. Quality data in the form of product or process measurements are obtained in real-time during the manufacturing process and plotted on a graph with predetermined control limits that reflect the capability of the process. Data that falls within the control limits indicates that everything is operating as expected. Any variation within the control limits is likely due to a common cause — the natural variation that is expected as part of the process. If data falls outside of the control limits, this indicates that an assignable cause might be the source of the product variation, and something within the process needs to be addressed and changed to fix the issue before defects occur. In this way, SPC is an effective method to drive continuous improvement. By monitoring and controlling a process, we can assure that it operates at its fullest potential and detect anomalies at early stages. Introduced in 1924, the method is likely to stay in the heart of industrial quality assurance forever. However, its integration with Artificial Intelligence techniques will be able to make it more accurate and precise and give more insights into the manufacturing process and the nature of anomalies.
Tasks for Artificial Intelligence
When human resources are not enough to handle the elastic environment of cloud infrastructure, microservices and containers, Artificial Intelligence comes in, offering help in many aspects:
Automation: -driven anomaly detection algorithms can automatically analyze datasets, dynamically fine-tune the parameters of normal behavior and identify breaches in the patterns.
Real-time analysis: solutions can interpret data activity in real time. The moment a pattern isn’t recognized by the system, it sends a signal.
Scrupulousness: Anomaly detection platforms provide end-to-end gap-free monitoring to go through minutiae of data and identify smallest anomalies that would go unnoticed by humans.
Accuracy: enhances the accuracy of anomaly detection avoiding nuisance alerts and false positives/negatives triggered by static thresholds.
Self-learning: -driven algorithms constitute the core of self-learning systems that are able to learn from data patterns and deliver predictions or answers as required.
Learning Process of Systems
One of the best things about systems and -based solutions is that they can learn on the go and deliver better and more precise results with every iteration. The pipeline of the learning process is pretty much the same for every system and comprises the following automatic and human-assisted stages:
- Datasets are fed to an system
- Data models are developed based on the datasets
- A potential anomaly is raised each time a transaction deviates from the model
- A domain expert approves the deviation as an anomaly
- The system learns from the action and builds upon the data model for future predictions
- The system continues to accumulate patterns based on the preset conditions
As elsewhere in -powered solutions, the algorithms to detect anomalies are built on supervised or unsupervised techniques.