Real-time Equipment State Monitoring and Cycle Time Calculation Method Based on DTW-KNN

Effective real-time monitoring of cycle time has important significance for manufacturing workshops to achieve production improvement and increase enterprise competitiveness. In this study, based on industrial big data, a workshop equipment state monitoring and cycle time calculation method based on DTW-KNN algorithm is proposed. Firstly, the characteristics of historical current data of equipment are analyzed, and the current waveform data in different equipment states are extracted and labeled with tags. Then, the DTW-KNN algorithm is used to construct the state model. Finally, based on the Spark framework, real-time current waveform is collected and pushed into the state model to monitor the state and calculate the cycle time. The application verification from the automotive engine valve production line shows that the DTW-KNN algorithm has significantly improved the efficiency of cycle time monitoring.