Early Anomaly Detection in Electrical Bushings Manufacturing at Hitachi Energy

Abstract: The manufacturing of electrical bushings for high voltages is complicated and highly demanding technology-wise. This process has more than 10 steps where a single mistake in the chain could cause a complete failure of the final product. A faulty bushing represents high costs to the company both economically and in terms of public image. Nowadays, fault detection is corrective-oriented, which means that there is low traceability on where the problem happens, and it is only detected once the final product is tested. This thesis aims to test a machine learning tool from Imagimob® to determine if is possible to detect faults during the manufacturing process using the existing captured data. To perform the test, a sample from 2019 was taken where the production of the bushings reached a 60% scrap rate. A deep-learning neural network with a 2D convolutional layer was implemented. The outcome of the system showed an efficiency of 80%. However, due to the complexity of the bushing manufacturing process, the few data samples, and the addition of different factors that can result in a faulty bushing, a range of probability is set depending on the number of anomalies detected. With such validation, the tool can label 18% of the bushings as surely faulty, and 27% as most likely faulty. The limitation of the tool is that the information must be analyzed after each step is done, and not continuously. Hence further research should be carried out on implementing a real-time tool.