Automated Risk Detection

Warehouse automation has made logistics processing more efficient and faster, but technical problems and breakdowns in automated systems are inevitable, occurring due to wear and tear on electronic and mechanical components within the system, and a single component can cause significant damage to the entire system. However, the personnel reductions made possible by automation have resulted in a lack of capacity to manage and troubleshoot these issues. Efficient maintenance of automated systems is required.
Dr.LoPAS automatically detects faults and inspects all conveyors, along with their location information, using a single mobile inspection machine.

19 Sensors

In Dr.LoPAS, a data collection device travels along the conveyor, where 19 types of sensors collect various operational data and combine it with location information to build a comprehensive big data set. By accumulating abnormal data generated by early signs of failure such as roller and conveyor belt wear, or abnormal loads and forces and analyzing this data over time, potential failures can be detected at an early detection stage.

Image Analysis

The high-resolution video camera installed in the front center records the entire conveyor during operation. Image recognition technology determines the presence or absence of abnormal parts at the roller level of each conveyor. It also identifies the current location on the operating conveyor from changes in feature points of surrounding images.

Temperature Mesh

Two thermal cameras are installed at the front left and right. Each camera measures the temperature of the entire conveyor using a mesh-based method. Each mesh cell is synchronized with location information on the conveyor, and temperature change histories at each roller position are stored as big data. By analyzing these historical changes, minor failures can be detected early, enabling regular maintenance before an emergency stop occurs.

Location Information

The most important aspect of failure detection is location information. Dr.LoPAS analyzes location information from data collected at the roller level by coordinating seven types of sensors. It stores 19 types of sensor data collected from each sensor in cloud-based big data. By understanding when, where, what happened, and how it occurred, we derive the "why".