Clearance Size Detection based on Deep Neural Networks without Feature Extraction

Abstract

Clearance is an important phenomenon in mechanisms that stems from manufacturing imperfections and wear and tear. Undetected clearance can compromise machine operations, negatively impacting its performance, and cause premature damage that requires maintenance actions. Monitoring clearance growth is useful to improve maintenance plans and helps reduce the number of unwanted interruptions during machine operation. In this work, a prediction method that targets the determination of the clearance size based on minimal, raw sensor data and machine learning is proposed. The data in this study are generated using multibody dynamics simulations based on planar mechanisms and used to train and compare several types of neural networks in terms of their ability to assess clearance size. Results show that clearance parameters can be reliably estimated with appropriate combinations of sensor locations and type of neural network. The developed method offers reliable clearance detection based on measurements that can be obtained from physical systems and used to monitor the state of their clearance defects.