Machines and manufacturing plants constantly generate data. Companies that successfully transform this data into innovation gain a decisive competitive advantage. Weidmüller is applying artificial intelligence approaches to machine manufacturers and production companies using easy-to-use software.
To analyze machine and process data, industrial analytics uses sophisticated models capable of detecting anomalies and even predicting future machine behavior. By employing artificial intelligence ( AI ) methods and machine learning ( ML ), relationships between previously unknown measurements are revealed using features derived from raw data.
Requires comprehensive specialized knowledge
Almost all companies have access to the necessary information. However, mid-sized companies, in particular, often rely on external data scientists to develop meaningful analytical models. Weidmuller has developed a groundbreaking solution that eliminates the need for data scientists in mid-sized companies. Through close collaboration with end-users, data experts identify correlations in measurements and train an initial model. Once the initial model is successfully implemented, new data is repeatedly fed into it, further developing the model throughout its lifecycle. Over time, this improves the quality of information.
Learning Machine Learning
Many machine manufacturers and production companies cannot yet use existing machine learning tools independently because these tools are already optimized for data-driven activities by analytics experts. Companies can invest heavily in training existing staff or hire their own data scientists. This creates a limiting factor that slows down the adoption of artificial intelligence in industry.
Another approach is to develop easy-to-use software solutions that users can understand and generate analytical models even without any statistical training. Weidmuller's Industrial Analytics business unit has put this idea into practice with automated machine learning software. The application's name implies that the models are largely developed automatically.
“ Similar applications are currently widely used in fintech, banking, and marketing. However, existing solutions are not suitable for machines and factories because they do not support the relevant data types in the automation industry. These solutions always require an ideal database, ” explained Dr. Carlos Paiz Gatica, Product Manager of the Industrial Analytics Business Unit . “ Furthermore, these solutions cannot integrate the user’s domain knowledge, which is crucial for industrial applications. ”
For automated machine learning software, Weidmuller's analytics experts combine domain expert data with algorithms to automatically generate suitable models. The following steps describe the model generation process (using anomaly detection as an example):
1. Select training data
Domain experts determine which datasets should be used to learn the normal behavior of machines or factories. To do this, a raw data overview is first generated to support user evaluation of the data's informational content. The preparation of measurements is entirely automated.
2. Feature Engineering
If the raw data is insufficient, additional information can be generated based on it. Users can use their domain knowledge to create new features. For example, these features can describe the process of temperature change, rather than just showing individual conditions. Using these features often allows for a better assessment of machine condition than using the raw data.
3. Mark machine behavior
Users label areas of normal behavior (green) or unwanted behavior (red) in the data. This allows users to add information to the training data using their domain knowledge. The assistance system supports the labeling process by directly highlighting similar situations in the dataset.
4. Model Training
The labeled dataset is transformed into models and trained using various machine learning methods. This fully automated process generates a list of alternative models that provides information related to result quality, execution time, and training duration. An anomaly score plot directly displays the model's results, allowing experts to directly compare model performance. If the desired model performance is not achieved, the user can edit the model's features and labels again. The model can then be directly integrated into the target system's architecture.
Expanding AI Applications
Paiz stated, " With automated machine learning software, machine manufacturers and production companies don't need to be data experts to independently develop and benefit from artificial intelligence and machine learning . " " Common applications allow users to generate initial models and further develop them. This way, companies no longer rely on data scientists or need to share their processes and machine knowledge with external partners. "
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Regarding Weidmüller
As a seasoned expert in industrial connectivity, Weidmüller provides products, solutions, and services to customers and partners worldwide in industrial environments encompassing power, signaling, and data processing. Rooted in these industries and markets, Weidmüller is confident in its ability to meet future technological challenges.
Weidmüller is committed to development, providing innovative, sustainable, and efficient solutions to meet the diverse needs of its customers. Therefore, we have jointly set standards in the field of industrial connectivity.
Currently, the Weidmüller Group has manufacturing plants, sales companies, and liaison offices in more than 80 countries around the world.
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