Slow response to LED display screen failures often leads to business losses and operational inefficiencies. However, the AI prognostic system, through real-time data collection and machine learning algorithms, can reduce fault location time to under 30 minutes. The following is a comprehensive guide to the system integration process, from hardware deployment to algorithm training, achieving end-to-end optimization.

Perception Layer Hardware Deployment

1. Multi-Dimensional Sensor Array

 Temperature sensor (±0.5°C accuracy): One sensor is deployed per 10 square meters of screen area to monitor the temperature of LED lamp beads (normally 60-85°C) and power modules (≤70°C).

 Vibration sensor (sampling rate 1000Hz): Installed on the fan and driver board, it captures early vibration signals indicating bearing wear (initiating an early warning when the amplitude exceeds 0.1mm/s).

 Current transformer (ratio 100:1): Connected in series with the power supply circuit, it monitors abnormal inrush current (fluctuations exceeding ±15% trigger an early warning).

After deploying 86 sensors on a stadium screen, a power module overheating warning was detected four hours in advance, preventing a black screen during a match. Edge Computing Layer Configuration

1. Pre-diagnosis Gateway Deployment

 Hardware: Equipped with NVIDIA Jetson AGX Orin (275TOPS computing power), supporting concurrent processing of 128 sensor data channels

 Software: Pre-installed with Ubuntu 20.04, configured to receive sensor data using the MQTT protocol (latency <50ms)

1. Feature Engineering Template

 Establish a Fault Feature Library: Collect over 200 characteristic parameters for 10 typical fault types (e.g., abnormal pixel current curves corresponding to dead spots)

 Real-time Feature Extraction: Perform wavelet transform denoising on temperature curves and extract temperature rise rates (>5°C/min is considered abnormal)

 Cloud AI Platform Training

1. Algorithm Selection and Training

 Utilize an LSTM+CNN hybrid model: LSTM processes time series data (temperature trend prediction), CNN identifies image faults (dead spot detection)

 Training Dataset: Collect over 3,000 fault cases (with a 1:1 ratio of normal to abnormal samples), split into training/validation/testing data in a 7:2:1 ratio. Test Set

Optimization Goal: Fault Classification Accuracy > 95%, Warning Lead Time ≥ 2 Hours

1. Knowledge Base Construction

Fault Tree Model: Establish a three-level mapping of "Phenomenon-Cause-Solution" (e.g., localized dark area → LED open circuit → module replacement)

Maintenance Strategy Library: Generate dispatch priorities based on fault urgency (Level 1 faults: 1-hour response, Level 3 faults: 24-hour response)

Business System Integration

1. API Interface Definition

Fault Warning Interface: Returns warning information in JSON format (including fault type, location, and confidence level)

Historical Data Interface: Supports querying sensor data curves for the past 30 days (resolution 1 minute/point)

1. Work Order System Integration

Automatic Dispatching: Directly route Level 1 faults to the nearest maintenance team (within a radius of ≤ 5 km)

Spare Parts Dispatch: Prepare modules in advance based on pre-diagnosis results (increases inventory turnover by 30%)

After integrating the AI pre-diagnosis system into a smart city project, the average fault response time was reduced from 12 hours to 2.1 hours, improving maintenance efficiency. 83%, and labor costs were reduced by 40%. It is recommended that embedded sensors be deployed during the construction phase of new screens. For the renovation of old screens, it is important to focus on adding power and temperature monitoring points to ensure the maximum effectiveness of the AI pre-diagnosis system.