#Edge AI #AWL Corporation #Denso Hokkaido

AWL Partners with Denso Hokkaido on Innovative Edge AI Solution for Factory Safety Management

In a significant leap towards enhancing factory safety management, AWL Corporation, a Hokkaido-based startup recognized by Hokkaido University, is collaborating with Denso Hokkaido. Denso Hokkaido, located in Chitose City, serves as the northernmost production base of the Denso Group, focusing on automotive semiconductor sensors' design and manufacturing. This partnership aims to integrate Denso Hokkaido's environmental monitoring sensor technology with cutting-edge edge AI capabilities to develop a real-time factory monitoring system equipped with a consolidated multi-sensor analysis function.

The Context of Safety Management in Manufacturing

With the increasing complexities of safety management and quality assurance in manufacturing, particularly driven by labor shortages and the need for enhanced production efficiency, the importance of real-time monitoring is becoming paramount. In particular, for precision manufacturing environments, factors such as temperature, humidity, gas concentration, and vibrations have direct implications on safety and product quality. Consequently, the ability to detect hazards like fire and smoke early is not only vital for ensuring employee safety but also crucial for minimizing damage to production equipment, thereby supporting business continuity plans (BCPs).

Traditional cloud-dependent monitoring systems have faced challenges due to communication delays and network congestion, making instant responses difficult. Furthermore, these systems often require human intervention for analysis and decision-making, which contradicts the solution to labor shortages. In response to these challenges, there is a growing focus on edge AI, which allows for local processing of sensor data, enabling both real-time capabilities and precise anomaly detection.

AWL's Contributions to Enhanced Safety Systems

AWL's role in this initiative involves developing an integrated analysis function that collects real-time data from multiple sensors, including temperature and gas concentrations, to facilitate anomaly detection through edge AI. The primary goal is to establish a framework for early detection of fire and smoke hazards, allowing on-site personnel to respond immediately.

To achieve this, the system leverages a low-latency, high-reliability architecture that operates entirely within the factory's local network, eliminating dependency on the cloud. Furthermore, it features a