Edge AI for Predictive Maintenance in High Value Manufacturing

机器故障会在生产和效率方面造成巨大损失。预测它们是具有挑战性的。传统的非基于人工智能的解决方案可能会导致以下部分/全部问题：1.计划外停机和生产力下降2\。严重事故和机器寿命缩短3\。财务损失人工智能有可能解决上述问题。然而，目前的解决方案依赖于物联网/云计算，存在以下缺点：1.耗电和开支2\。数据隐私问题3\。碳排放量增加量化计算，一台云AI相机每年消耗325公斤二氧化碳。这是因为摄像头需要大量的数据传输。该项目旨在创建嵌入式系统解决方案，使用EDGE AI来预测和预防机器故障。该解决方案分析收集的传感器数据来监控机器，降低故障风险。解决方案为：1低成本和Robust2。环境友好型3.数据安全(内部)量化，边缘AI摄像头每年仅消耗4公斤二氧化碳

Machine breakdowns cause massive losses in production and efficiency. Predicting them is challenging.Traditional non-AI-based solutions may cause some/all of the following problems:1\. Unplanned downtime & Reduction in productivity2\. Severe accidents & Reduced lifetime of machines3\. Financial lossesAI has the potential to solve the aforementioned problems. However, current solutions rely on IoT/cloud computing, and suffer from the following drawbacks:1\. Power-hungry & Expensive2\. Data privacy issues3\. Increased carbon footprintTo quantify, a Cloud AI camera consumes 325 kg CO2 /year. This is because of the large amount of data transmission required for the camera.This project aims to create embedded system solutions that use edge AI to predict and prevent machine breakdowns.The solution analyses collected sensor data to monitor the machines and reduce the risk of a breakdown.The solution is:1\. Low-cost & Robust2\. Environment-friendly3\. Data secure (In-house)To quantify, an edge AI Camera consumes only 4 kg CO2 /year