Anomaly detection is indispensable in various business scenes. In particular, anomaly detection technology that utilizes machine learning has been attracting attention recently, and its accuracy is improving yearly. Perhaps some of you reading this article would like to introduce an anomaly detection system.
However, it is not possible to introduce a system without knowing what kind of method is used to detect anomalies. Therefore, this time, we will explain the procedures and advantages of anomaly detection systems for those who want to know about anomaly detection systems.
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Anomaly Detection Method
There are three main methods of anomaly detection:
- Outlier detection
- Abnormal site detection
- Change point detection
Outlier detection is a method that detects when the input data deviates from the standard state. Standard state means that the characteristics of the data are not far from the region in which most of the data accumulated in the past are contained.
For example, when an abnormal noise occurs in a car engine, it can be detected by an outlier detection.
Abnormal Site Detection
Abnormal part detection is a method of detecting time-series parts when abnormal patterns significantly differ from normal conditions in time-series data continuously acquired by sensing.
Outlier detection evaluates anomalies based on the characteristics of the data, while anomaly detection extracts only anomalies hidden in continuous data, so it is used for failures in equipment.
Change Point Detection
Change-point detection is a technique to detect abrupt changes in the predicted model pattern. For example, if the number of accesses to owned media drops after a certain day, it will be used to detect when it started.
Advantages Of An Anomaly Detection System Using AI
AI-based anomaly detection systems have the following three advantages.
- Improve operational efficiency
- Can prevent human error
- Prevents work from becoming dependent on individual skills
Improve Operational Efficiency
The anomaly detection system replaces the work performed by humans, leading to improved work efficiency. For example, there is a product inspection task in the manufacturing industry. Normally, workers visually check each product one by one to check for defects.
On the other hand, by letting the anomaly detection system perform the inspection work on your behalf, you will be able to use human resource time for other complicated tasks, and you will be able to improve your work efficiency.
Can Prevent Human Error
Introducing an anomaly detection system can automate work and decisions, preventing human errors. If the inspection work explained earlier is done manually, the quality will vary depending on the worker’s condition on that day.
However, introducing an anomaly detection system makes it possible to make decisions according to algorithms so that inspections can be performed under certain rules.
Prevents Work From Becoming Dependent On Individual Skills
By introducing an anomaly detection system, it is possible to prevent work from becoming dependent on the individual. In work performed by humans, such as inspection work, the workers accumulate the know-how, so there is a large difference in the work level between young workers and veterans.
For this reason, if veteran workers fail to pass on their skills, problems may occur, such as young workers being unable to perform their duties after retirement.
Therefore, by having the anomaly detection system learn from the experience of experienced workers, highly accurate inspection work becomes possible and leads to the prevention of dependence on individual skills.
Precautions For Anomaly Detection Systems
When introducing an anomaly detection system, the following points should be noted.
- Requires huge amounts of data
- System understanding required
Requires Huge Amounts Of Data
Since the anomaly detection system requires machine learning by AI, a large amount of data for education must be prepared. Machine learning is good at finding patterns in large amounts of data and making judgments based on them. With a small amount of data, without careful devising, the learning will be biased, and it will not be possible to deal with various patterns that can occur in reality.
System Understanding Required
Machine learning can be used effectively if users understand its features and how to use it correctly. When introducing anomaly detection, it is important to understand what kind of data it learned from and what decisions it made as a result of learning.