Monitoring bolt tightness using percussion and machine learning
The systems and methods described herein are for monitoring the tightness of bolts. The systems and methods may be used with a mechanism to apply a percussive tap and with a recording or monitoring device for detecting and recording the acoustic signals that are generated by the percussive tap. The acoustic signals generated by percussive taps applied to bolts in various looseness states are analyzed and a machine learning model is developed that allows for determining bolt looseness based on the acoustic signals.
1. A method for monitoring the tightness of a bolted connection, comprising:
using an impact device to apply at least one percussive tap to a portion of a plurality of known bolted connections, wherein each known bolted connection has a known tightness, to produce a model acoustic signal for each known bolted connection;
recording the model acoustic signals using a recording device to produce model acoustic data;
extracting features from the model acoustic data wherein the features are correlated with the known tightness of each known bolted connection;
using the features and the known tightness of each known bolted connection to generate a decision tree model for predicting tightness of a bolted connection using an acoustic signal;
using the impact device to apply at least one percussive tap to a portion of an unknown bolted connection having an unknown tightness to produce an unknown acoustic signal for the unknown bolted connection;
recording the unknown acoustic signal for the unknown bolted connection using a recording device to produce unknown acoustic data;
extracting the features from the unknown acoustic data;
using the decision tree model to predict the tightness of the unknown bolted connection using the features extracted from the unknown acoustic data;
using the impact device to apply at least one percussive tap to a portion of a plurality of test bolted connections, wherein each test bolted connection has an known tightness, to produce a test acoustic signal for each test bolted connection;
recording the test acoustic signals using a recording device to produce test acoustic data;
extracting features from the test acoustic data;
using the decision tree model to predict the tightness of the test bolted connections using the features extracted from the test acoustic data; and
comparing the tightness of the test bolted connections predicted by the decision tree model to the known tightness of the test bolted connections in order to validate the decision tree model.
2. The method of claim 1 , wherein the impact device is a hammer.
3. The method of claim 1 , wherein the features are power spectrum density (PSD) at selected frequency ranges.
4. The method of claim 3 , wherein the selected frequency ranges are 200-300 Hz, 300-400 Hz, 400-500 Hz, and 500-600 Hz.