An experimental anomaly detection framework for a conveyor motor system using recurrent neural network and dendritic gated neural network

Abstract

Machine breakdowns are alarming threats to factories. They can substantially decrease productivity, cause financial losses, and create unsafe work environments for operators. Early detection of system anomalies is crucial to prevent and fix machine threats before they become fatalities. With the advent of digitalisation and smart manufacturing, various artificial intelligence (AI) and machine learning (ML) techniques contribute to implementing efficient anomaly detection systems with more accurate results. In this research, the design of an experimental anomaly detection platform (ADP) was suggested for a conveyor motor system. The ADP analyses time-series conveyor motor parameters and accurately classifies whether they would cause a faulty system. The authors build a classification ML model using dendritic gated neural networks (DGNN) to achieve better accuracy. Dendritic Neural Networks are highly immune to forgetting, contributing to better performance than regular artificial neural networks (ANNs) using backpropagation. The ADP also includes a fault detection platform section for the conveyor motors' time-series parameters with recurrent neural networks (RNN) ML regression models to predict motor sensor values. When training ML classification models, the predicted time-series parameters can also serve data augmentation purposes. This regression section contributes to a more robust and double-layered ADP, preventing threats from the time-series inputs to the output classification level. The ADP solution suits small traditional factories with limited historical data records. The experimental results show the benefits of using our ADP built on the DGNN ML model over several classification models such as ANN, convolutional neural network (CNN), and support vector machine (SVM).