An Introduction to AI Anomaly Detection

An Introduction to AI Anomaly Detection | SPONSORED

In the evolving landscape of industrial IoT, the ability to identify anomalies is not just a luxury but a necessity. This blog post shows how AI-driven anomaly detection is transforming how industries operate and setting new standards in efficiency and security.

As we delve deeper, you’ll discover the unique advantages AI brings to the table, differing significantly from traditional methods. We’ll guide you through the initial steps to implement AI anomaly detection in your business, utilizing cutting-edge tools like InfluxDB, Quix, and HiveMQ. By the end of this read, you’ll grasp the essence of AI in anomaly detection and be equipped with the knowledge to leverage its potential for your industrial IoT needs.

What is AI anomaly detection?

AI anomaly detection identifies irregularities in data that deviate from the norm using artificial intelligence and machine learning algorithms. Unlike traditional methods, AI anomaly detection doesn’t rely solely on predefined thresholds or simple statistical techniques. Instead, it leverages complex models that can learn from data over time, adapting to new patterns.

The process typically involves data ingestion, feature engineering, model training, and continuous monitoring. Machine learning models, such as neural networks or clustering algorithms, train on historical data to recognize standard patterns. When new data comes in, these models can detect anomalies by identifying data points that significantly differ from established patterns.

How is AI anomaly detection different from traditional anomaly detection?

Traditional methods often rely on rule-based systems that trigger alerts when data points exceed certain thresholds. These systems require setting thresholds manually and don’t adapt to changes in data patterns without experts fine-tuning them, making them less flexible to changing underlying conditions. These models also become less accurate in situations where many different variables can impact the anomaly being detected. In contrast, AI-powered anomaly detection can dynamically adapt to new data, identify subtle and complex anomalies, and continuously improve over time with more training data.

Anomaly detection benefits

Anomaly detection offers several benefits to businesses. Let’s look at a few real-world benefits and use cases for anomaly detection.

● Improved Security – Anomaly detection improves the overall security of your business. By collecting and monitoring network traffic, you can use anomaly detection to create alerts when unusual events occur that could signal data breaches or unauthorized access.
● Enhanced operational efficiency – You can improve operational efficiency by using anomaly detection to monitor industrial processes. When operational data deviates from a certain threshold, your system can take immediate action to resolve the issue. An example would be a food processing plant recalibrating a machine to improve packaging speed.
● Cost reduction – Detecting problems with anomaly detection can save costs by resolving issues before they escalate. One example is wind turbine companies monitoring blade rotation speed, which allows them to know when to make a minor repair before a major failure occurs.
● Scalability – Anomaly detection can help with scaling systems as they grow or change. Users can update models to use or introduce new variables and help identify performance anomalies in complex systems.

How to build an AI anomaly detection pipeline

Let’s take a look at the key things you will need to get started with using AI for anomaly detection:
1. Data collection — The first step requires a way to efficiently collect the data generated by your hardware. A tool like Telegraf is useful for IoT use cases where devices may be communicating over multiple different protocols. Telegraf also allows you to process and manipulate data before storage, which can simplify your architecture by removing the need for an additional data processing component. MQTT is a common protocol used in IoT environments because of its reliability and efficiency. A tool like HiveMQ can simplify working with MQTT.
2. Data storage — Once you have established instrumentation for collecting data, you will need a storage solution. For IoT workloads, a time series database like InfluxDB is a good option.
3. AI model — To make predictions or determine if a data point qualifies as an anomaly, you need to train an AI model. You can build a model from scratch or fine-tune an existing model with your own data to improve accuracy. Hugging Face is a platform that hosts models you create or pre-made models.

Check out this webinar for a walkthrough tutorial on creating your anomaly detection pipeline. This webinar shows you how to set up an MQTT broker for transferring data from devices, uses Quix to process and InfluxDB to store that data, and finally, uses a model hosted on Hugging Face to make predictions on the data.

Real-world examples

Let’s look at a few examples of companies using anomaly detection in production to create value for their businesses.

Gotion

Gotion is an electric vehicle battery manufacturer that uses AI for anomaly detection and alerting to enable predictive maintenance. They collect millions of metrics for data like voltage, current, and temperature in their batteries and store them using InfluxDB. This data can then be used for analytics and training their AI models.

Robinhood

Robinhood is a financial services and trading platform. Robinhood built their real-time monitoring and anomaly detection platform using their Faust stream processing tool backed by InfluxDB for storage.

Veritas technologies

Veritas is a company that specializes in data management and is used by approximately 80% of Fortune 500 companies. Veritas built machine learning models to improve forecasting, detect anomalies, and generate alerts based on sudden increases in backup storage usage.

Wrapping up

AI anomaly detection represents a significant advancement in monitoring and maintaining industrial IoT systems. Its ability to learn from data, adapt to new patterns, and detect subtle anomalies makes it a powerful tool for enhancing operational efficiency, security, and predictive maintenance. By following the outlined steps and utilizing tools like InfluxDB, Quix, HiveMQ, and Hugging Face, businesses can effectively implement AI-driven anomaly detection systems, unlocking new levels of insight and control in their industrial processes.

Sponsored by InfluxData