These days, more than a few manufacturers are incorporating AI technology into their worksites . In this article, we will summarize “machine learning”, which is the cornerstone of such AI. In particular, with the use of AI in anomaly detection systems in the manufacturing industry in mind, we explain the types, characteristics, and advantages of typical machine learning. Please refer to it if you are a manufacturing business manager or a person in charge of a site.
What is machine learning that has become popular in recent years?
In recent years, AI has evolved further through machine learning, which has been realized through the use of big data. Especially in the manufacturing industry, using AI equipped with machine learning for anomaly detection systems has produced great results. By grasping the characteristics of machine learning and introducing it appropriately, it will be possible to modernize the efficiency of the manufacturing industry.
Machine learning is a mechanism in which AI, which is fed with a large amount of data, will be able to find the essential patterns behind such data on its own. Specifically, it is used as follows.
For example, the manufacturing industry produces large quantities of the same product. Strictly speaking, there is a very small amount of error between these products. “Products whose errors are too large to ignore” are called defective products. In machine learning, information about a large number of products including such defective products is read into AI as “teaching materials”. Then, AI automatically learns, “If the product has a large error, it should be rejected from the line as a defective product.”
In this way, the mechanism that enables AI to automatically distinguish between normal products and defective products is called machine learning. By introducing such learned AI to manufacturing sites, it is possible to free human labor from pre-shipment checks of products and status checks on production lines. This will greatly reduce the burden on employees. As a result, it is expected that they will be able to focus on the work that is impossible without humans, and that the overall flow of work will become smoother.
Benefits of machine learning anomaly detection
Let’s take a concrete look at the benefits that the manufacturing industry will benefit from the realization of an anomaly detection system based on machine learning as described above.
can eliminate individuality
In the anomaly detection performed by human employees, the ability to spot defective products with the naked eye and skin sensation is extremely important. Such detection skills, along with the use of specific instruments and equipment, can be viewed as an individual’s accumulated ability, like a kind of craftsmanship. In other words, it is very difficult to find and train employees who can handle such detection tasks, other than those who have cultivated the skills over many years. As a result, it is easy to fall into a situation where “specific detection work can only be done by a very small number of specific individuals.”
If machine learning can be used to detect anomalies, it will be possible to eliminate such dependency on the job site. The search for new personnel and training costs will also be reduced.
Business efficiency can be improved
Employees will be able to perform anomaly detection work with a minimum of work in an environment that eliminates individuality.
AI performs anomaly detection tasks according to visual and numerical criteria. Also, as an effect of machine learning, AI can quickly perform precise detection that is impossible for humans. Employees can carry out precise detection work by assisting such clear AI movements. It would be a big psychological burden to perform such detection with your own senses such as your own eyes. The introduction of AI will reduce the detection time itself while relieving employees of such a burden.
Leads to reduced running costs
Machine learning will allow detection work to be carried out automatically without the need for human intervention. Therefore, it is possible to maintain a high level of detection without hiring or training personnel dedicated to detection. In other words, the running costs related to anomaly detection can be greatly reduced.
These reduced costs can be used in a variety of ways, such as further improving the working environment, trying to develop new business opportunities, and so on.
Types of machine learning anomaly detection
There are several classifications of mechanisms commonly referred to as “machine learning”. Here, we introduce representative types.
Supervised and unsupervised learning
In particular, machine learning can be broadly classified as “supervised/unsupervised” learning.
In the case of supervised, AI is first given correct data, that is, normal product data. AI learns patterns from that data and guides analytical models. By using this analysis model as the axis, even if you don’t know whether the product data is normal or defective, you will be able to judge by yourself. Since the criteria for correct answers are clear in advance, learning accuracy is high, and learning can be completed relatively quickly if a large amount of high-quality correct answer data is available.
Since this method is often referred to simply as “machine learning” today, it is safe to understand it as “the basic learning format of AI.”
In the unsupervised case, a large amount of unknown data is given to AI without determining in advance whether the product is normal or defective. AI analyzes the structure and characteristics common to those data by itself and divides them into groups. As a result, it becomes possible to determine whether the product data newly imported from the next time should be assigned to the normal group or should be rejected as a defective product.
The advantage of unsupervised learning is that humans can start learning without setting a standard for “correct” answers. It is also a highly useful method in situations where the correct answer is unknown. For example, by comparing the data of experimental products with existing products to create patterns, it will show its true value even when searching for the ideal state.
Abnormal part detection
Next, we will introduce three typical detection methods. The first is called anomaly detection. Detects anomalies in certain data.
When AI observes specific data, it compares it with a large amount of other data and compares it with expected states and patterns. Even in such a situation, if we find “a place that clearly deviates from the expected state” in specific data, we will extract it as an abnormal part and report it.
Not only is the product rejected as a defective product, but it is also useful for future improvements because it visualizes “what is abnormal” compared to normal products.
Change point detection/outlier detection
Change-point detection is a method of detecting where some abrupt change occurs in the data. For example, when analyzing specific data along the time axis, if there is a point where the numerical value changes sharply in a specific period, it will be detected while specifying the point in time that triggered it.
Outlier detection is a method of detecting when unusual changes occur in the data. Detect when there is a “larger change than expected” or “a change to a different trend than expected” when comparing a large amount of data.
With these, when an irregular situation occurs, it will be possible to identify the time and period that triggered it and confirm the abnormality.
Image-based detection is also effective in the manufacturing industry
Recently, advanced machine learning called “deep learning” has also become popular. As a result, anomaly detection using images has come to be realized on site. A typical example is a system that uses cameras to monitor products that are being manufactured one after another on a line, and uses the images as materials to detect anomalies. The use of such image data in the various learning methods and detection methods mentioned above has enabled the introduction of AI at manufacturing sites today.
summary
With the evolution of machine learning technology today, AI is being actively introduced at manufacturing sites. As a representative example, this article introduced an anomaly detection system.
By making good use of machine learning, including systems that use images as material data, employees will be able to perform more accurate anomaly detection work with less work. If you are interested in labor shortages in the field and avoiding personalization, please consider introducing machine learning and AI once.
