Using anomaly detection to support classification of fast running (packaging) processes
This is an incremental solution for machine operators in the food packaging industry to identify and address production breakdowns more efficiently.
The paper tackles the problem of labeling high-speed data from fast-running packaging processes by using anomaly detection to assist manual classification, enabling the training of machine learning models for identifying breakdown causes, though no concrete numbers are provided.
In this paper we propose a new method to assist in labeling data arriving from fast running processes using anomaly detection. A result is the possibility to manually classify data arriving at a high rates to train machine learning models. To circumvent the problem of not having a real ground truth we propose specific metrics for model selection and validation of the results. The use case is taken from the food packaging industry, where processes are affected by regular but short breakdowns causing interruptions in the production process. Fast production rates make it hard for machine operators to identify the source and thus the cause of the breakdown. Self learning assistance systems can help them finding the root cause of the problem and assist the machine operator in applying lasting solutions. These learning systems need to be trained to identify reoccurring problems using data analytics. Training is not easy as the process is too fast to be manually monitored to add specific classifications on the single data points.