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

سال انتشار: 1399
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 89

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شناسه ملی سند علمی:

JR_IJRRS-3-1_003

تاریخ نمایه سازی: 16 شهریور 1400

چکیده مقاله:

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 (۱) idle and (۲) 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 ۳۰ s using a small lathe and smoothed with the moving average. The datasets were randomly divided into three different sets for training (۷۰%), validation (۱۵%), and testing (۱۵%). 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 (۱۰۰%) was better than that of a multiclass linear support vector machine model (۶۸%). The procedure and the ANN model established in this study can be utilized to detect unsafe conditions of a lathe and other industrial machines.

نویسندگان

Sung Hyun Park

School of Industrial Engineering, University of Ulsan, Korea

Amir Tjolleng

School of Industrial Engineering, University of Ulsan, Korea

Jeong Won Park

School of Industrial Engineering, University of Ulsan, Korea

Kihyo Jung

School of Industrial Engineering, University of Ulsan, Korea