Detecting Unsafe Conditions of a Lathe using an Artificial Neural Network with Three-axis Acceleration Data

Detecting unsafe conditions of a lathe is critical to prevent hazards in a workplace. This study proposed an artificial neural network (ANN) model to classify the state of a lathe into one of the nine conditions (two normal conditions and seven unsafe conditions) based on three-axis acceleration data. The two normal conditions were (1) idle and (2) normal processing. The seven unsafe conditions included unsafe states of a lathe (i.e., eccentric rotation, chipping, improper workpiece fixation, and base looseness) and a worker (i.e., glove contact, hair contact, and necklace contact). The acceleration data for each condition were measured for 30 s using a small lathe and smoothed with the moving average. The datasets were randomly divided into three different sets for training (70%), validation (15%), and testing (15%). The ANN model was trained using the training and validation sets and its performance was evaluated using the testing set. The testing results showed that the classification accuracy of the ANN model proposed in this study (100%) was better than that of a multiclass linear support vector machine model (68%). The procedure and the ANN model established in this study can be utilized to detect unsafe conditions of a lathe and other industrial machines.