Recently, due to the expansion of the logistics industry, demand for logistics automation equipment is increasing. The modern logistics industry is a high-tech industry that combines various technologies. In general, as various technologies are grafted, the complexity of the system increases, and the occurrence rate of defects and failures also increases. As such, it is time for a predictive maintenance model specialized for logistics automation equipment. In this paper, in order to secure the operational safety and reliability of the parcel loading system, a predictive maintenance platform was implemented based on the Naive Bayes-LSTM(Long Short Term Memory) model. The predictive maintenance platform presented in this paper works by collecting data and receiving data based on a RabbitMQ, loading data in an InMemory method using a Redis, and managing snapshot DB in real time. Also, in this paper, as a verification of the Naive Bayes-LSTM predictive maintenance platform, the function of measuring the time for data collection/storage/processing and determining outliers/normal values was confirmed. The predictive maintenance platform can contribute to securing reliability and safety by identifying potential failures and defects that may occur in the operation of the parcel loading system in the future.

Door to door service is a typical method for individual parcel deliveries. In the current delivery information system, a delivery person manually inputs the information of many delivery results, which causes inefficiency and difficulties of tracking deliveries. This study suggests an enhanced delivery information system which has the following two characteristics. Firstly, a tagged RFID(Radio Frequency Identification) transmits the delivery result information to the main server by just collecting RFID at a place of destination. Secondly, with the characteristics of rewritable method, the collected RFID is to be input new information and tagged to other parcels.