Case Study
Driving and defining change with expertise, compassion and persistence.
Case Study
Driving and defining change with expertise, compassion and persistence.
Case Study
Driving and defining change with expertise, compassion and persistence.
Automatic Anomaly Detection in Smart Manufacturing for A Semiconductor Company
Automatic Anomaly Detection in Smart Manufacturing for A Semiconductor Company
Automatic Anomaly Detection in Smart Manufacturing for A Semiconductor Company
Background
Unplanned downtime caused by unexpected machine faults are extremely costly.
The company has hired engineers to monitor the conditions of the machines, but aim to replace them with an AI system.
Reduce unplanned downtime by detecting subtle anomalies using the AI system.
Unplanned downtime caused by unexpected machine faults are extremely costly.
The company has hired engineers to monitor the conditions of the machines, but aim to replace them with an AI system.
Reduce unplanned downtime by detecting subtle anomalies using the AI system.
Design
Anomalies are rare, so focus on unsupervised anomaly detection.
Anomalies are rare, so focus on unsupervised anomaly detection.
Anomalies are rare, so focus on unsupervised anomaly detection.
Aim to apply the AI system on any new machines, so no domain expertise is required.
Aim to apply the AI system on any new machines, so no domain expertise is required.
Aim to apply the AI system on any new machines, so no domain expertise is required.
Encompass all sorts of sensor signals from electricity to chemical concentrations to accelerometer.
Encompass all sorts of sensor signals from electricity to chemical concentrations to accelerometer.
Encompass all sorts of sensor signals from electricity to chemical concentrations to accelerometer.
Objective
Installed sensors on the machines to collect data.
Installed sensors on the machines to collect data.
Installed sensors on the machines to collect data.
Use the collect data to train an AI system.
Use the collect data to train an AI system.
Use the collect data to train an AI system.
Compared to human engineers, the AI system should achieve lower cost, higher accuracy, and and zero latency
Compared to human engineers, the AI system should achieve lower cost, higher accuracy, and and zero latency
Compared to human engineers, the AI system should achieve lower cost, higher accuracy, and and zero latency
Methodology
Flexible preprocessing for different kinds of sensor signals
Flexible preprocessing for different kinds of sensor signals
Flexible preprocessing for different kinds of sensor signals
Includes both Autoencoder and Forecasting neural network models as part of the AI system to enhance its capabilities for different kinds of anomaly.
Includes both Autoencoder and Forecasting neural network models as part of the AI system to enhance its capabilities for different kinds of anomaly.
Includes both Autoencoder and Forecasting neural network models as part of the AI system to enhance its capabilities for different kinds of anomaly.
Proposed a regularization framework that increases confident to deploy it in real-world.
Proposed a regularization framework that increases confident to deploy it in real-world.
Proposed a regularization framework that increases confident to deploy it in real-world.
Result & Impact
Less Unplanned Downtime
The company react to anomalies much more promptly and thus reduce unplanned downtime.
Higher Efficiency with Less Human Involvement
The AI system can monitor anomalies 24/7 with higher accuracy than human engineers.
Design AI solutions that create more efficiency and impact in the world
© 2024 Gaialabs. All rights reserved.
Design AI solutions that create more efficiency and impact in the world
© 2024 Gaialabs. All rights reserved.
Design AI solutions that create more efficiency and impact in the world
© 2024 Gaialabs. All rights reserved.