[7A5] A hybrid physics-data model for end-of-line-test failure prediction
Amirreza Baghbanpourasl and Christian Eitzinger Profactor GmbH, Im Stadtgut A2, 4407 Steyr-Gleink, Austria
For the purpose of quality control in some production lines each product undergoes an end of line test. Early detection of the products that will fail during or at the end of the test can save time and material. Besides, it prevents hazards and damages to the test equipment and the product, that can be caused by operating defective products. Utilizing data analysis for prediction of such anomalies can be challenging due to the non-stationary and multi-phased nature of the measurement process.
Here, we present a hybrid model (using welding machine test data), where instead of fully data driven anomaly detection analysis, the analysis starts with approximate modeling of temperature evolution at the location of temperature sensors which is governed by physical process of heat transfer, originated from any unspecified heat source. This approach allows decision making about the test result just at the beginning of the test. In addition, since this method reduces the dimensionality of the input space, the machine learning model can be trained with much less number of samples, which is an enabler for many cases where access to training data is limited.
Here, we present a hybrid model (using welding machine test data), where instead of fully data driven anomaly detection analysis, the analysis starts with approximate modeling of temperature evolution at the location of temperature sensors which is governed by physical process of heat transfer, originated from any unspecified heat source. This approach allows decision making about the test result just at the beginning of the test. In addition, since this method reduces the dimensionality of the input space, the machine learning model can be trained with much less number of samples, which is an enabler for many cases where access to training data is limited.
