Abstract: A method for predicting a clamp force using a convolutional neural network includes: generating a cepstrum image from a signal processing analysis apparatus; extracting a characteristic image by multiplying a predetermined weight value to pixels of the generated cepstrum image through artificial intelligence learning; extracting, as a representative image, the largest pixel from the extracted characteristic image; synthesizing an image by synthesizing the extracted representative image information; and predicting a clamp force by comparing the synthesized image with a predetermined value.

Abstract: An axial force measurement apparatus measuring an axial force of a bolt member by measuring a clamping force, of a nut member threaded onto the bolt member, which is applied to fasten a structure, includes: a sensor that applies vibration to the structure through one side of the tightened nut member and obtains, on an opposite side of the nut member, a signal by the vibration that propagates through the structure and passes through the nut member; and an analyzer that analyzes the signal received from the sensor and determines the axial force of the bolt member using a difference in propagation velocity of the signal by the vibration according to the clamping force of the nut member.

Abstract: An axial force measurement apparatus measuring an axial force of a bolt member by measuring a clamping force, of a nut member threaded onto the bolt member, which is applied to fasten a structure, includes: a sensor that applies vibration to the structure through one side of the tightened nut member and obtains, on an opposite side of the nut member, a signal by the vibration that propagates through the structure and passes through the nut member; and an analyzer that analyzes the signal received from the sensor and determines the axial force of the bolt member using a difference in propagation velocity of the signal by the vibration according to the clamping force of the nut member.

Abstract: A method of estimating clamp force of a bolt may include converting and analyzing, by a signal processing and analyzing device, a learning signal for the clamp force of the bolt, the learning signal being acquired by a data acquirement device; comparing the learning signal classified into groups according to signal characteristics by the converting and analyzing, with a discriminant signal desired to be estimated; and estimating, as clamp force of the discriminant signal, clamp force corresponding to a signal of a most similar group among the learning signal classified into the groups by the comparing.

Abstract: A method for predicting a clamp force using a convolutional neutral network method includes: generating a cepstrum image from a signal processing analysis apparatus; extracting a characteristic image by multiplying a predetermined weight value to pixels of the generated cepstrum image through artificial intelligence learning; extracting, as a representative image, the largest pixel from the extracted characteristic image; synthesizing an image by synthesizing the extracted representative image information; and predicting a clamp force by comparing the synthesized image with a predetermined value.

Abstract: A method of measuring an axial force of a bolt may include fastening a joint using a fastener, receiving a vibration signal by a detector, transforming the received vibration signal into frequency domain data having a plurality of frames, analyzing the signal transformed into the frequency domain data, and predicting an axial force and indicating a predictive value thereof.

Abstract: A method of measuring an axial force of a bolt may include fastening a joint using a fastener, receiving a vibration signal by a detector, transforming the received vibration signal into frequency domain data having a plurality of frames, analyzing the signal transformed into the frequency domain data, and predicting an axial force and indicating a predictive value thereof.