Project: Climate chamber
In this success story, we would like to show you how we managed to optimize process stability and reduce unplanned downtime in just 14 days.
Our client in the semiconductor industry is known for manufacturing high-quality electronic components and controls for industrial applications. In order to meet the stringent requirements of its customers and deliver the components on time, the end products must be tested in climate chambers before delivery.
Recently, malfunctions in the climatic chambers caused the test process to be aborted and repeated. This resulted in a time loss of 24 to 48 hours per aborted test process.
Summary:
- The accumulation of failures negatively affects the production process.
- The disruptions are identified in detailed, retrospective data analyses for individual cases.
- The automated identification of faults by simple sets of rules such as pressure thresholds fails due to the complexity of the scenarios that occur.
Our solution for the company:
During their research, the company came across IPN and invited us to present our services. After a thorough analysis of the situation and the available data, it was clear to us that our AI-based Anomaly Detection was the perfect solution.
Another concern of our customer was direct information to maintenance in the event of a malfunction. By integrating the Anomaly Detection into our “LiveAlert” system, we can immediately forward detected faults to the maintenance engineer.
How does the Anomaly Detection work:
Our Anomaly Detection aims to learn AI-supported data histories that represent the smooth operation of a plant. The climate chambers studied simulate different climate scenarios, resulting in different data histories that nevertheless represent trouble-free operation. The model learns these scenarios from existing data and considers them when evaluating new data.
The trained Anomaly Detection continuously monitors the deviation between the model and the actual plant data. In case of significant deviations, the Anomaly Detection reports the occurrence of this deviation to the maintenance engineer via “LiveAlert“.
Findings:
- Anomaly Overview: Our AI-based Anomaly Detection enables early detection of building faults, often more than a week before an actual equipment failure.
- Reduction of unplanned down time: This makes it possible to eliminate build-up faults during planned downtime and therefore, to prevent unplanned downtime.
- LiveAlert: By integrating our results into our “LiveAlert” system, the customer receives both advance information about incipient faults and warnings of an impending outage.
The biggest benefit:
Our system enables early detection of faults, reduction of wear and inefficiencies due to defective operation, and prevention of repeated quality checks due to faults.
Key advantages of our AI-based Anomaly Detection are:
- Once trained, the Anomaly Detection can be applied to all identical climatic chambers.
- Only minimal knowledge of systems or malfunctions is required for implementation, which significantly reduces the customer’s project effort.
- Our customer achieved measurable optimization of equipment and processes in just 14 days.
The most important insight for all involved:
This project illustrates that it is possible to implement condition monitoring and forecasting in as little as 14 days and on a limited budget.
After this short period of time, qualified information is available to decide whether this approach should be pursued further or, if necessary, whether optimizations should be made to the data basis. In any case, this is a significant step into the digital future.
You can find more information about “Anomaly Detection” here.
Details about “LiveAlert” can be found here.
You can learn more about our fortnightly project here.
