Condition Monitoring and Anomaly Detection: Real-World Challenges and Successes

Data science projects in industry come with many challenges – from idea exploration over proof-of-concept implementation to deployment. This paper shows along the use case of detecting anomalies in LED drivers how to successfully approach such a project. Focus is put on the anomaly detection using machine learning methods, namely one-class SVMs, isolation forests, and LSTM-based autoencoders. The algorithms show promising results; all detected anomalies can be linked to an abnormality in the data. These anomalies will be analysed by domain experts to optimize the product design and the production process. Furthermore, the successful proof-of-concept implementation justifies the investment into a global deployment of the anomaly detection in other development and production sites.