🏭 AI 智慧工廠 — 預測性維護決策平台 (PoC)

最近這段時間，我開始將過去研究的「預測性維護」概念，實際落地為一個可運行的系統。

不過這次加入 AI 預測模型 + 3D Model，讓整個系統更接近我當初構想的樣子。

🏭 AI 智慧工廠 — 預測性維護決策平台 (PoC)

透過 Three.js 建立 4 種不同設備的 3D Model。

結合 AI 健康度預測，不僅能預測設備故障風險，也能模擬異常行為並驅動自動化決策：

✦ 異常預警 → 自動降速，降低設備損耗風險

✦ AI 信心區間 → 95% CI fan chart 量化預測不確定性

✦ 自動派工 → 預警觸發後直接推送 CMMS 工單

當然目前的功能還遠遠不夠，庫存連動、供應鏈整合、領料自動化這些真實工廠需要的能力，都還在規劃中。

畢竟現實的工業場景遠比 PoC 複雜，這也是接下來想持續探索的方向。

👉 最終目標是減少非計劃性停機時間，並實現真正主動的維護決策。

請期待下一次更新，謝謝 🙏

#PredictiveMaintenance #Industry40 #SmartManufacturing 

#DigitalTransformation #AI #工業4_0 #預測性維護 #智慧製造

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Recently, I've been working on turning my previous research onPredictive Maintenance into a fully functional system.

This time, I incorporated an AI prediction model along with 3D modeling, bringing the system closer to my original vision.

🏭 AI Smart Factory — Predictive Maintenance Decision Platform (PoC)

Built with Three.js, the system includes 3D models of four different equipment types.

By integrating AI-based health prediction, it not only forecasts potential equipment failures but also simulates fault conditions and drives automated decision-making:

✦ Anomaly Detection → Automatically slows down operations to reduce wear and risk

✦ Uncertainty Quantification → 95% CI fan chart to represent prediction confidence

✦ Automated Work Orders → Triggers and pushes CMMS work orders upon detection

The system is currently at an early PoC stage.

Key capabilities required in real factory environments—such as inventory integration, supply chain coordination, and automated material handling—are part of the next phase.

Industrial scenarios are far more complex than any PoC can capture,

and bridging that gap is exactly the direction I aim to keep exploring.

👉 Ultimately, the goal is to reduce unplanned downtime and enable truly proactive maintenance decisions.

Stay tuned for the next update. Thank you 🙏

#PredictiveMaintenance #PdM #Industry40 #SmartManufacturing

#DigitalTransformation #AI

#工業4_0 #預測性維護 #智慧製造