Predicting Safety Solutions via an Artificial Neural Network

Abstract

Abstract Considering the extensive data sets and statistical techniques, Industry 4.0 embodies a branch of machine learning that has a constantly increasing impact on machine safety. We propose an preliminary study based on application of multi-layer feed-forward neural networks in machine safety solutions; the approach is expected to simplify the user choice of suitable measures and safety functions. The prediction method and factors influencing the success rate of the procedure are indicated in a safety parameter scale reflecting industrial experience with classic methods. The multilayer perceptron, a mainstream classification algorithm from the WEKA machine learning workbench, was employed in our primary dataset as a class of the feed-forward artificial neural network. Our initial experimental data were collected from various experts within the industry. The overall proportion of individual safety solutions was correctly assigned by using the training-evaluated test mode, and its prediction accuracy was 100%; further, when assessing the 5-fold cross-validation test mode, we obtained the success rate of 40%. These statistical tools could be used to assess safety PLC traceability systems, and they exhibit the potential to assist managers in decision-making as safety devices. We demonstrate that machine learning is widely usable by the expert community and might bring multiple advantages, such as reduction of the safety solution design time, major cost cutback, and engineering tool availability.