It’s frustrating to see your business suffer from regulations or equipment failures that cost a lot of money and effort. Often, these failures are not attributed to any one person, but rather a system failure. Unfortunately, it’s hard to identify these problems, especially when there are missed signals along the way. Condition monitoring is a process that can help prevent equipment failures. By measuring various parameters of machines, like vibration, and sound, we can spot errors quickly. Traditional methods of monitoring are not equipped to handle the massive amounts of data we have today. It’s like finding a needle in a haystack to identify a single deviation. To improve productivity and prevent potential issues, traditional monitoring needs machine learning. The abundance of data available today makes a strong case for using these techniques in industrial environments. A study by McKinsey estimated that data-driven techniques can reduce machine downtime by 30- 50% and increase machine life by 20-40 %.

What is Anomaly Detection?

Anomaly detection is an application of Machine Learning that involves identifying events in data that deviate from the expected behaviour. By spotting anomalies early on, we can prevent potential issues and improve safety, security, and efficiency in a wide range of scenarios. In particular, anomaly detection is central to meeting regulatory standards in manufacturing. Anomalies in manufacturing can often mean major bottlenecks, leading to reduced throughput, increased downtime, and decreased product quality.

The Problem: Anomaly Detection in Sound

Anomaly detection in sound poses several unique challenges compared to other types of data analysis. For one, sound data is inherently complex and high-dimensional, as it involves a continuous stream of audio signals that can vary in frequency, amplitude, and duration. Then there are background noises that make it difficult to isolate anomalies. Another challenge with manual anomaly detection in sound is human perception. Understanding of sound can be highly subjective and vary from person to person. Even trained experts may struggle to recognise subtle changes in sound patterns, such as slight decibel changes or unusual harmonics. This subjectivity can make it difficult to establish consistent criteria for what constitutes an anomaly in sound data.

The Solution: APIs for Anomaly Detection in Audio

By training machine learning algorithms on large datasets of sound data, AI models can learn to recognise patterns and anomalies in sound that may be difficult for humans to perceive. These models can be highly accurate and consistent, allowing for early detection and proactive intervention in cases where anomalies are identified. Early detection of anomalies can prevent further damage or harm, reducing the cost and impact of incidents. More benefits of automated anomaly detection include:

Quality Control

AI-powered anomaly detection can help manufacturers detect faulty products during production. For instance, in an automotive factory, detecting unusual sounds during the assembly process can indicate a faulty part. Anomaly detection can alert workers to stop production, effectively reducing faulty products.

Predictive Maintenance

Anomaly detection can be used to predict equipment failures before they occur. In a manufacturing plant, detecting changes in sound patterns could signal a malfunction. This can help maintenance teams to schedule repairs and maintenance before equipment fails, reducing downtime and production losses. For example, an anomaly detection system that can detect changes in the sound of the pump may indicate that the pump is about to fail. By detecting the anomaly early, maintenance teams can schedule repairs before the pump fails completely.

Environmental Monitoring

Detecting unusual sounds or noise levels can signal issues such as leaks, emissions, or hazardous conditions. Early detection can help prevent accidents, reduce environmental impact, and improve safety.

Security Monitoring

Anomaly detection in audio can be used for security monitoring in public places or commercial buildings. For example, detecting unusual sounds or noise levels could indicate a security breach or unauthorised access. This can help security personnel to respond quickly.

Cost Savings

By identifying and addressing issues early, manufacturers can reduce the cost of defects, waste, and equipment downtime. Anomaly detection can also help companies optimise their production processes, reducing the cost of production and increasing efficiency.

Our API: Anomaly Detection using Datasets for Manufacturing

At Gemmo we have a model zoo consisting of a wide range of pre-trained machine-learning models, built on diverse datasets. We have specifically designed one to detect anomalies in manufacturing sounds. This model is an invaluable tool for manufacturers as it helps them identify potential issues in their production processes before they become major problems. Each dataset contains thousands of audio recordings that capture the sound of specific industrial equipment or processes. For example, our Pump dataset contains audio recordings of pumps in different states, including normal operation, clogged pumps and pumps with damaged impellers. Similarly, the Valve dataset contains audio recordings of valves in different states, including open and closed valves, as well as valves with different levels of wear and tear. By leveraging these pre-trained models, manufacturers can quickly and easily integrate anomaly detection capabilities into their production processes.

Final Thoughts

Anomaly detection in sound is a critical capability for many manufacturing operations as it can help prevent bottlenecks, reduce downtime, and ensure product quality. However, detecting anomalies in sound patterns can be difficult and time-consuming for humans, making AI-powered solutions such as Gemmo’s Anomaly Detection API invaluable. By Johanna Walsh