딥러닝 알고리즘 훈련을 위해서 충분한 양의 데이터 확보가 필수적이다. 그러나, 공학시스템에서 데이터 취득은 매우 어렵거나, 상황에 따라 불가능한 경우가 존재한다. 이러한 데이터 부족 문제는 딥러닝 알고리즘 개발에 큰 걸림돌이 되고 있다. 본 논문은 구조물 건전성 진단을 위한 딥러닝 알고리즘 개발에서 발생하는 데이터 부족 문제 해결을 시도하였다. 깊은 생성 모델을 구축하고 딥러닝 학습을 위한 훈련 데이터를 생성하는 방법을 제안한다. 제안된 방법의 성능을 검증하기 위해 수상 양식장 어망 데이터를 바탕으로 사례 연구를 진행하였다. 본 연구는 제안된 심층 생성 모델을 통해 데이터를 직접 만들어 냄으로써 구조물 건전성 진단에서 발생되는 데이터 부족 문제 해결에 기여할 것으로 기대된다.

Degraded electrodes in a resistance spot welding system should be replaced to ensure that weld quality is maintained. Welding electrodes are subjected to different environmental and operational loading conditions during use. When they are replaced with a fixed interval, replacement may occur too early (raising maintenance costs) or too late (leading to quality issues). This motivates condition monitoring strategies for resistance spot welding electrode tips. Thus, this paper proposes a modified recurrence plot (RP) for robust condition monitoring of welding electrode tips in resistance spot welding systems. The overall procedure for the proposed condition monitoring approach consists of three steps: (1) transformation of a one-dimensional signal to a two-dimensional image, (2) unsupervised feature extraction with LeNet architecture-based convolutional neural networks, and (3) health indicator calculation. RP methods convert dynamic resistance waveforms to RPs. The original RP method provides an image with binary-colored pixels (i.e., black or white) that makes this method insensitive to the change of the waveform signal. The proposed RP method is devised to be sensitive to the change of the waveform signal, while enhancing robustness to external noise. The performance of the proposed RP method is evaluated by examining simulated aperiodic waveform signals with and without external noise. A case study is presented to examine the proposed method’s ability to monitor the condition of resistance spot welding electrodes. The results show that the proposed method outperforms handcrafted, feature-based condition monitoring methods. This study can be used to accurately determine the lifetime of welding electrodes in real time during the spot welding process.