Edge AI for Predictive Maintenance in High Value Manufacturing

机器故障对生产和效率造成巨大损失。预测它们是具有挑战性的。传统的非人工智能解决方案可能会导致以下部分/全部问题：计划外停机时间和生产力降低2\。严重事故和机器寿命缩短3\。经济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，并且存在以下缺点：耗电又贵。数据隐私问题3\增加的碳足迹为了量化，一台Cloud AI相机每年消耗325公斤的二氧化碳。这是因为相机需要大量的数据传输。该项目旨在创建使用边缘人工智能来预测和防止机器故障的嵌入式系统解决方案。该解决方案分析收集的传感器数据来监控机器并降低故障风险。解是：1\。低成本和鲁棒性。Environment-friendly3 \。数据安全（内部）为了量化，一台边缘AI摄像头每年仅消耗4千克二氧化碳

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