Project Background
The client is a top 10 logistics enterprise in China, with over 50 sorting centers nationwide and equipment assets exceeding RMB 1 billion. The traditional reactive maintenance approach resulted in frequent equipment failures, causing losses of over RMB 20 million annually due to equipment downtime.
Core Pain Points
Solution
IoT Data Acquisition Layer
Vibration, temperature, and current sensors were deployed on critical equipment (conveyor motors, sorters, elevators), supporting multi-protocol access via Modbus, OPC-UA, and MQTT. Edge gateways perform data preprocessing and feature extraction to reduce cloud transmission costs.
AI Predictive Engine
Using a time-series anomaly detection model based on LSTM and Transformer, equipment failures are predicted 7 days in advance with 92% accuracy. The model automatically identifies failure types (bearing wear, imbalance, misalignment, etc.) and provides recommended maintenance windows and priorities.
Visual Operations Dashboard + Automated Work Orders
Real-time display of all equipment health status (green/yellow/red triage), with a failure prediction timeline that helps the O&M team schedule shifts in advance. When AI detects anomalies, maintenance work orders are automatically generated, the most suitable repair engineer is assigned, and when spare parts inventory is insufficient, a procurement request is automatically triggered.
Performance Data
| Indicator | Before | After | Improvement |
|---|---|---|---|
| Failure Downtime Rate | 12% | 1.5% | ↓87.5% |
| Annual Maintenance Cost | 8 million | 4.4 million | ↓45% |
| Average Repair Time | 4 hours | 1.5 hours | ↓62.5% |
| Spare Parts Inventory Turnover | 90 days | 45 days | ↓50% |
| Unplanned Downtime Count | 36/year | 5/year | ↓86% |
> Quantitative summary: Failure downtime rate reduced by 87.5% to 1.5%, annual maintenance cost reduced by 45% to 4.4 million, unplanned downtime count reduced by 86% to 5 per year, with 7-day advance failure warnings at 92% accuracy.
Technology Stack
FAQ
How far in advance can IoT+AI predictive maintenance warn of a failure?
Using the LSTM+Transformer time-series anomaly detection model, equipment failures are typically warned 7 days in advance with 92% accuracy. Vibration, temperature, and current sensors are deployed on critical equipment (conveyor motors, sorters, etc.), and data is preprocessed by edge gateways before being uploaded to the cloud for analysis.
What is the payback period for predictive maintenance?
In this case, annual maintenance cost dropped from 8 million to 4.4 million, saving 3.6 million per year. The system deployment investment was approximately 1.5 million, with a payback period of about 5 months. Additionally, unplanned downtime losses (originally 20 million per year) were significantly reduced.
Can existing PLC/SCADA systems be directly integrated?
Yes. We integrate with existing PLC/SCADA systems via standard industrial protocols such as Modbus, OPC-UA, and MQTT, without replacing existing equipment. Edge gateways collect data locally and support store-and-forward during network interruptions, ensuring stable operation in complex industrial field network environments.
