Automatic Component Identification Based on Time Series Classification for Intelligent Devices

Advances in manufacturing technology have made it possible to replace some components anywhere to meet the needs of different functions with multi-function devices. However, remote servers are typically not informed when workers replace device components. In these scenarios, there is a mismatch in the data models between the device components and the servers. To this end, this study proposes an automatic component identification method based on time series classification for intelligent devices. First, the component identification problem is transformed into a time series classification problem. Then, some classifiers are trained based on the time series data generated by the models. In the next step, the classifiers match the collected data with the component models. Finally, the components are identified by a fusion decision, respectively. The experimental results show that the proposed method has a high component identification accuracy.