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.

1. 서 론
2. 이산생산공정 및 디지털 제어 신호
    2.1 이산 생산 공정의 디지털 제어 신호 구성
    2.2 H대 레이저 마킹 공정
3. 고장 상태 감지를 위한 LSTM 구축 및 실험 결과
    3.1 RNN 및 LSTM
    3.2 LSTM 모델 구축 및 시간 공정 상태 감지 결과
4. 결 론
References

• Yong-Uk Song(한밭대학교 산업경영공학과) | 송용욱
• Sujeong Baek(한밭대학교 산업경영공학과) | 백수정 Corresponding Author