세탁기 생산 공정 내 결함 검출을 위한 소음 특성 분석과 AI 기반 결함진단 모델 개발 = Development of AI-based Diagnosis Model for On-line Fault Detection for Washing Machines

다국어 초록 (Multilingual Abstract)

Quality inspection in the production lines of washing machines is very important since even minor defects inside a washing machine can escalate into major issues such as leaks and loud noises. Previous studies have explored various methods for fault detection in washing machines, including vibration signal analysis. In addition, artificial intelligence (AI) diagnostic models have been widely adopted in many industry fields, and significant research is being conducted to improve the performance of these models. In this study, we propose the use of AI models and their enhancements for fault detection in washing machines for quality assurance in the manufacturing stage. We extract damage-sensitive features from sound recordings of these machines on the manufacturing line and utilize supervised learning to train an AI model to detect faults. Performance enhancement via feature selection is also performed. The proposed methods are validated by applying and comparing the performance of various AI models on the data obtained from the manufacturing line.

국문 초록 (Abstract)

세탁기 생산라인에서 품질검사는 세탁기 내부의 작은 결함이라도 누수와 소음 등의 문제로 확대될 수 있으므로 신속하고 정확한 결함 검출이 요구되는 중요한 과정이다. 기존에는 진동 신호 분석을 통한 세탁기 고장 탐지 연구와 세탁기의 소음 특성 연구가 진행되었다. 또한 인공지능(AI) 기반의 진단기법이 많은 산업 분야에서 널리 채택되고 있으며 실제 양산에 적용하기 위한 진단 모델의 성능과 신뢰성에 관한 연구들이 지속해서 진행되고 있다. 본 연구에서는 세탁기 제조 공정 내 품질검사를 위한 AI 기반의 결함진단 모델과 성능 개선 방식에 대해 제안한다. 생산라인에서 측정된 세탁기의 탈수 소음에서 결함에 민감한 특징을 추출하고, 통계학적 분석에 기반하여 특성인자를 선정한 후 진단 모델에 적용하였다. 제안된 기법은 실제 생산라인에서 측정한 데이터에 적용되어, 진단 모델의 검출 성능 비교와 검증을 진행하였고 우수한 특성인자 조합을 통해 성능이 향상됨을 확인하였다.

참고문헌 (Reference)

1 김승일 ; 노유정 ; 강영진 ; 박선화 ; 안병하, "진동 데이터의 시간영역 특징 추출에 기반한 고장 분류 모델" 한국전산구조공학회 34 (34): 25-33, 2021

2 이명준 ; 전준영 ; 강토 ; 한순우 ; 박규해, "압축센싱과 통계학적 기법을 적용한 회전체 시스템의 상태진단" 한국소음진동공학회 26 (26): 651-659, 2016

3 김지만 ; 정병규 ; 허소정 ; 안세진 ; 정의봉, "드럼세탁기 방사소음의 소스 및 기여도 분석" 한국소음진동공학회 24 (24): 628-635, 2014

4 R. X. Gao, "Wavelets" Springer 2011

5 S. Arfaoui, "Wavelet Analysis : Basic concepts and applications" CRC Press 2021

6 R. Stamminger, "Towards a durability test for washing-machines" 131 : 206-215, 2018

7 T. G. Dietterich, "The handbook of brain theory and neural networks" 2 (2): 110-125, 2002

8 F. Y. Osisanwo, "Supervised machine learning algorithms : classification and comparison" 48 (48): 128-138, 2017

9 C. T. Yiakopoulos, "Rolling element bearing fault detection in industrial environments based on a K-means clustering approach" 38 (38): 2888-2911, 2011

10 P. Henriquez, "Review of Automatic Fault Diagnosis Systems Using Audio and Vibration Signals" 44 (44): 642-652, 2014

11 R. X. Gao, "Non-stationary signal processing for bearing health monitoring" 1 (1): 18-40, 2006

12 R. Yan, "Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis" 42 (42): 293-302, 2009

13 A. Malekloo, "Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights" 21 (21): 1906-1955, 2022

14 S. Goumas, "Intelligent on-line quality control of washing machines using discrete wavelet analysis features and likelihood classification" 14 (14): 655-666, 2001

15 A. Singh, "Gearbox fault diagnosis under non-stationary conditions with independent angular re-sampling technique applied to vibration and sound emission signals" 144 : 11-22, 2019

16 J. Tang, "Feature selection for classification : A review" 2014

17 S. Lu, "Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals" 385 : 16-32, 2016

18 R. Polikar, "Ensemble machine learning:Methods and applications" Springer 1-34, 2012

19 S. Bae, "Dynamic analysis of an automatic washing machine with a hydraulic balancer" 257 (257): 3-18, 2002

20 Hee-Tae Lim ; 정의봉 ; Keun-Joo Kim, "Dynamic Modeling and Analysis of Drum-type Washing Machine" 한국정밀공학회 11 (11): 407-417, 2010

21 U. C. Jeong, "Development of a sound quality index for the wash cycle process of front-loading washing machines considering the impacts of individual noise sources" 87 : 183-189, 2015

22 S. K. Goumas, "Classification of washing machines vibration signals using discrete wavelet analysis for feature extraction" 51 (51): 497-508, 2002

23 A. Bommert, "Benchmark for filter methods for feature selection in high-dimensional classification data" 143 : 106839-, 2020

24 Z. K. Peng, "Application of the wavelet transform in machine condition monitoring and fault diagnostics : A review with bibliography" 18 (18): 199-221, 2004

25 A. Singh, "A review of supervised machine learning algorithms" 2016

26 A. Jović, "A review of feature selection methods with applications" 2015

27 A. Mondal, "A novel feature extraction technique for pulmonary sound analysis based on EMD" 159 : 199-209, 2018