We are your experts for artificial intelligence and machine learning in digital production. Data generated by production equipment is used to predict trends or failures, react automatically and thus increase efficiency.
Outliers and anomalies are rare and unusual patterns that can occur in any data stream e.g. images, video and sensor signals. A challenge for previous AI models is the provision of so-called labeled data, i.e. data sets labeled as good/bad in advance. In a productive environment, these are difficult or almost impossible to obtain, since anomalies by their nature rarely occur. Therefore, we use artificial intelligence and machine learning techniques that learn the normal state and can thus detect abnormal conditions at an early stage.
AI models support the detection of downtimes before they occur. The goal is to predictively maintain machines and equipment before faults occur and minimize downtime, as well as to increase machine efficiency by maximizing the utilization of wear parts. This is done using artificial intelligence and machine learning techniques that learn the normal state of process data in a production cycle and can thus detect abnormal conditions at an early stage. You thus reduce your maintenance costs without risk and increase plant availability at the same time.
AI-based defect detection in automated image processing makes it possible to support users in decision-making, thereby reducing time, costs, and safety risks. Using examples, the system learns to distinguish specific features and anomalies such as missing components, misalignments, etc. The advantage of an AI-based system is reliability and high accuracy.
We use state-of-the-art technologies to develop individual reliable AI solutions and components that meet the needs of users and optimally map your processes. These can be connected or integrated into your existing system landscape and software systems.
We are your experts for artificial intelligence and machine learning in production. KItelligence offers you consulting and individual AI solutions for outlier detection and predictive maintenance.
Would you like to know how you can effectively use Artificial Intelligence and Machine Learning in your company? Do you need help finding the right model and making your processes more efficient through Big Data use and machine learning? We can help you find the right approach for your company.
Our incentive is constant engagement with current research on the topics of artificial intelligence and machine learning. We examine the results with regard to suitability and use in an industrial environment and therefore see ourselves as a link between research in e.g. university institutions and industry.
Are you looking for a trusted partner to develop a robust solution tailored to your specific needs and requirements and connect to your existing system landscape and software systems? You have found it! Do you have a functioning team, but lack resources or special know-how? Our team is used to working closely with our customers on new solutions and supporting your employees through knowledge sharing.
We are your experts for artificial intelligence and machine learning in production.
With KItelligence, we realize our idea of innovation - integrating technology into processes and business models in a targeted manner with a clear focus on real added value on the market and for people.
We help our customers grow and achieve positive change for the future by increasing their productivity and performance. To do this, we use our diverse expertise every day to develop innovative solutions for the industry.
News, use cases and examples
Are you tired of manually sifting through mountains of data to identify potential issues or anomalies? Look no further! Anomaly detection is a powerful technique that can save you time and effort by automatically identifying unusual events or behaviors in your data. In this blog post, we'll introduce you to the world of anomaly detection and explain how it can benefit you as an engineer.
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Every day, we rely on a variety of machines. But the truth is that every machine will eventually fail if it is not maintained. In this blog post, we'll show you the different maintenance strategies companies are pursuing to increase operational reliability and reduce costs. Predictive maintenance can be used to estimate the time to failure and help you identify which parts need repair.
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Now let's walk through the steps to build a predictive maintenance system. The first step is to collect a large amount of sensor data that represents normal and faulty operation. Modern machines and systems are often already equipped with a large number of sensors for direct control. By recording the sensor data accordingly, you can now use it sensibly for predictive maintenance.
Read moreYou need information material or have a question about our solutions? Our team will be happy to help you! Just give us a call, send an email to info@KItelligence.de or use the contact form below.
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