PLC 디지털 제어 신호를 통한 LSTM기반의 이산 생산 공정의 실시간 고장 상태 감지
Real-Time Fault Detection in Discrete Manufacturing Systems Via LSTM Model based on PLC Digital Control Signals
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초록
A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.
Abstract
A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.
참고문헌 (22)
- 천강민, "앙상블 모델 기반의 기계 고장 예측 방법", 한국산업경영시스템학회지, 43(1), pp.123~131 (2020), 한국산업경영시스템학회[DOI]
- 조상제, "LNG FPSO 압축기 고장시간 예측 방안에 관한 연구", 한국산업경영시스템학회지, 37(4), pp.12~23 (2014), 한국산업경영시스템학회
- Fang, W., "Big Data Driven Jobs Remaining Time Prediction in Discrete Manufacturing System : A Deep Learningbased Approach", International Journal of Production Research, 58(9), pp.2751~2766 (2020)
- Ghosh, A., "A Novel Automata and Neural Network based Fault Diagnosis System for PLC Controlled Manufacturing Systems", Computers and Industrial Engineering, 139, pp.1~16 (2020)
- 홍규택, "플랜트 공정 FD FAN 설비의 이상 상태 예측을 위한 센서 데이터 분석", 한국CDE학회 논문집, 23(2), pp.137~143 (2018), 한국CDE학회[DOI]
- 황세윤, "통계적 기계학습법을 적용한 플랜트 공정 장비의 시계열 데이터 분석및 고장 상태 진단", 한국CDE학회 논문집, 23(3), pp.193~201 (2018), 한국CDE학회[DOI]
- 황세웅, "스마트 팩토리를 위한 센서 데이터 분석과 제품 불량 개선 연구", 한국빅데이터학회 학회지, 3(1), pp.95~103 (2018), 사)한국빅데이터학회[DOI]
- Ioannides, M. G., "Design and Implementation of PLCbased Monitoring Control System for Induction Motor", IEEE Transactions on Energy Conversion, 19(3), pp.469~476 (2004)
- 정훈, "기계적 모터 고장진단을 위한 머신러닝 기법", 한국산업경영시스템학회지, 40(1), pp.57~64 (2017), 한국산업경영시스템학회
- 정선웅, "도금공정 실시간 원격 온도 모니터링 시스템", 한국산업경영시스템학회지, 38(4), pp.72~79 (2015), 한국산업경영시스템학회
- Kim, S., "An AR based Monitoring System for Discrete Manufacturing", Proceedings of 2018 Spring Korean Institute of Industrial Engineers, pp.707~712 (2018)
- 권상현, "밀도기반 군집화와 딥러닝을 활용한공정 주기 신호의 이상 탐지 및 분류", 대한산업공학회지, 44(6), pp.475~482 (2018), 대한산업공학회[DOI]
- Lee, K, "PLC Code Generation for the Control of Production System", Proceedings of 2008 Spring Korean Institute of Industrial Engineers, pp.128~134 (2008)
- Miao, Y., "EESEN : Endto-end Speech Recognition Using Deep RNN Models and WFST-Based Decoding", Proceedings of 2015 IEEE Workshop on Automatic Speech Recognition and Understanding, pp.167~174 (2015)
- Murata, T., "A Petri Net-Based Controller for Flexible and Maintainable Sequence Control and its Applications in Factory Automation", IEEE Transactions on Industrial Electronics, IE-33(1), pp.1~8 (1986)
- Saez, M., "Real-Time Manufacturing Machine and System Performance Monitoring Using Internet of Things", IEEE Transactions on Automation Science and Engineering, 15(4), pp.1735~1748 (2018)
- Sameh, M, "Bearing and Rotor Faults detection and diagnosis of Induction Motors using Statistical Neural Networks", Proceedings of 20th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), pp.77~81 (2020)
- Selvin, S, "Stock Price Prediction Using LSTM, RNN and CNN-sliding Window Model", Proceedings of 2017 International Conference on Advances in Computing, Communications and Informatics, pp.1643~1647 (2017)
- 성길영, "PLC 신호의 시간차이를 이용한 자동화 공정의 PLC 프로그램 모니터링", 한국CDE학회 논문집, 14(3), pp.176~185 (2009), 한국CDE학회
- Sherstinsky, A, "Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM)network", Physica D : Nonlinear Phenomena, 404, pp.1~28
- Song, Y.-U., "Image Analysis based Data Pre-Processing of Digital Control Signals for Error Status Detection in Automatic Manufacturing System", Proceedings of The Korean Society of Mechanical Engineers, pp.2075~2080 (2020)
- Vathsala, M.K., "RNN based Machine Translation and Transliteration for Twitter Data", International Journal of Speech Technology, 23, pp.499~504 (2020)