Anomaly Detection Using Artificial Intelligence and Machine Learning for Quality Assurance
Artificial Intelligence
Silpa Sasidharan October 17, 2023

You might have noticed that artificial intelligence (AI) is taking over every aspect of everyday life. Indeed, this ingenious technology enables us to enhance every facet of our lives! From automating tasks to recommending customized solutions, AI is here to stay! Also, one of the effective methods for assessing product quality is anomaly detection.

Automation with the help of artificial intelligence ensures quality assurance and eliminates the risks of manual errors. Today, companies are more concerned about the quality of their products and services. 

In this article, we shall discuss the key aspects of anomaly detection, time series anomalies, and the different types of time series anomalies. Without further ado, let us go to the topic downright.

What is Anomaly?

Many of you might have heard about the term ‘anomaly’. Before breaking down anomaly detection, we must understand the word ‘anomaly’. Typically, it refers to ‘deviation’ or a rare occurrence where an event does not match the regular pattern. As a result, it becomes suspicious.

For example, you may notice:

  • A sudden increase or decrease in quantity
  • Or any changes that are different from the expected standards

Some of the most common reasons for such anomalies include

  • Corruption of data,
  • Human error during data entry,
  • Unstable data collection methods
  • System malfunctions

Anomaly Detection Using Artificial Intelligence and ML

What is Anomaly Detection?

As you understand what anomaly implies, we shall move on to describe anomaly detection. So, it is the technique of identifying the anomalies in the data pattern. Identifying deviations from the standard data patterns addresses product quality issues.

Read More: Artificial Intelligence as a Service (AIaaS): What Is It and How Does It Solve Business Problems?

What is the Need for Machine Learning in Anomaly Detection?

Anomaly detection powered by artificial intelligence and machine learning (ML) ensures quality assurance in several industrial sectors. With the help of artificial intelligence and machine learning, businesses can identify unusual patterns in the data.

Anomaly detection helps find the odd man. For example, those that do not fit into the standard data pattern. Let us see how anomaly detection with machine learning helps quality assurance.

Suppose we take the case of the production and manufacturing sector. Any anomaly points to defects and malfunctions in the manufacturing process. As a result, it helps stop production or undertake corrective measures to prevent the sale of damaged and defective products to customers.

So, in this case, how does the ML algorithm behave? ML algorithms analyze production data from the sensors and use product specifications in real time to detect anomalies. 

Early detection of such deviations helps companies take sudden action. Therefore, it prevents organizations from manufacturing defective goods and ensures absolute quality.

What is Anomaly Detection in Time Series Data?

Time series data consists of data collected over a certain time. Today, companies leverage time series data to make better decisions. Anomaly detection is critical for time series data because it helps identify deviations that may not be easily spotted by looking through the raw/unstructured data. It can either be in the form of increased or decreased data or any unusual change in the pattern.

What are the Different Types Of Time Series Anomalies?

Generally, three significant types of anomalies come under time series anomalies.

Artificial Intelligence and Anomaly Detection

  • Point Anomaly
  • Contextual Anomaly, and 
  • Collective Anomaly

Let us go through each of the anomalies in detail.

Point Anomaly

Point Anomaly refers to a single data point that deviates from the remaining data set. For example, events happen in an order or a series of unexpected combinations.

Contextual Anomaly

The algorithms searching for contextual anomalies identify deviations in those datasets that appear normal.

Collective Anomaly

In collective anomaly, the single data points appear normal when looked at individually. However, you can see unexpected patterns or results in a collection of data points.

What is the role of Artificial Intelligence in Anomaly Detection?

It takes a lot of effort to manually process large amounts of complex data to generate useful insights. Unstructured data comprises images, audio files, videos, emails, and so on.

Therefore, relying on artificial intelligence techniques helps organizations process large and complex raw data for anomaly detection. They can also combine machine learning algorithms to get the best results.

Why Does Your Organization Need Anomaly Detection Using Artificial Intelligence and Machine Learning?

Most businesses look for ways to improve performance and enhance efficiency in offering service. So, with too much data, companies need help to track each item. Manually tracking these data is almost impossible. Therefore, they rely on data mining to discover trends, ensuring every operation works perfectly. Anomaly detection helps organizations understand potential security problems and address issues in quality control.

How Does Anomaly Detection Work?

Typically, there are three kinds of approaches based on artificial intelligence and machine learning techniques.

Artificial Intelligence and Machine Learning for Anomaly Detection

Supervised Anomaly Detection

In Supervised Anomaly Detection, you can train machine-learning models with normal and deviated datasets. It is useful for detecting deviations that form part of the training dataset but fails when there is a new anomaly. Supervised learning needs manual expertise and effort and, hence the name.

Unsupervised Anomaly Detection

In this case, you do not need manual data labeling. Here, a few data points deviate from the normal datasets. Unlike the above approach, an unsupervised detection system identifies new anomalies unseen during training.

Semi-supervised Anomaly Detection

It consists of labeled and unlabeled data sets. Further, this model can learn continuously after deployment and identify anomalies not seen during training. This model works with raw data as well.

Read More: Unlock Business Opportunities With Artificial Intelligence (AI): Interview With Nicolas Babin

Design Principles for Anomaly Detection Using Artificial Intelligence

Organizations can either purchase ready-made systems or develop their own. Regardless of the choices, they should consider the business size, resources, applications, and requirements.

1. Time

Does detecting data breaches and security issues take a lot of time? So, do you want to leave an anomaly without addressing it, particularly one directly impacting your operations? Therefore, consider the time needed; for example, it requires batch processing if you need it within a few days. Or else, if you need it immediately, it needs real-time data analysis.

 2. Scalability

Scalability typically considers the size of an organization, a factor that varies between organizations. Do you need to analyze a large number of records? Do you think there will be an increase in the volume of data soon? Therefore, based on your choice, you can consider either batch processing or real-time analysis of data.

3. Rate of Variation 

Do you think your data changes very fast or slow? Is it consistent for a certain time and then changes after a few while? Understand the speed at which your data changes. If a change in data happens slowly, it is easier to find anomalies. Therefore, understand the speed at which data changes occur and then develop and deploy an anomaly detection system. In fact, it varies between industries.

4. Conciseness

Artificial intelligence and machine learning systems can identify several anomalies in a single notification. Further, it can provide useful insights into the source of an issue. Therefore, investing in a system that informs you about the different anomalies rather than unrelated alerts seems better. 

Frequently Asked Questions

What is anomaly detection? 

Anomaly detection identifies rare, suspicious occurrences in data as they vary from the standard pattern.

What is the process of anomaly detection?

It finds patterns that deviate from the normal standard data. Further, it indicated potential issues that require in-depth analysis. It is used in industrial process control to provide useful insights. 

What are the key benefits of anomaly detection?

Anomaly detection enhances the performance of software applications. Additionally, it assists in detecting fraud and security issues and drives growth opportunities.

Final Word

Machine learning technology helps with anomaly detection, which helps organizations understand deviations from the usual course of pattern. Anomaly detection backed by artificial intelligence, deep learning, and machine learning technologies helps us precisely identify deviations from normal standards. Therefore, it ensures comprehensive quality assurance throughout the production process.

Conventional tests that form part of manual quality assurance are prone to errors. On the other hand, anomaly detection using artificial intelligence and machine learning techniques hardly shows errors and locates anomalies accurately. Further, it does not require much time or effort and is very useful for monitoring the development of products.

Do you want to step toward a less laborious and more result-oriented quality assurance process? Select ThinkPalm’s AI development services to kick-start your quality assurance efforts using artificial intelligence and machine learning techniques. Our AI experts lift your business by deploying the most reliable anomaly detection system. So what are you waiting for? Get in touch with us today and be ready to ride the wave of success!

Anomaly Detection System

Author Bio

Silpa Sasidharan is a content writer and social media copywriting expert working at ThinkPalm Technologies, who aspires to create marketing texts for topics spanning from technology, automation and digital business solutions.