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Land Transport

Dedicated Power Supply Health Management System

 

Introduction: Military quality, intelligent escort

In the military industry, the reliability of power equipment directly impacts national security and mission success. Faced with the potential risks of externally sourced and assembled power supplies, a dedicated power supply health management system has emerged. Relying on IoT sensing and digital twin technology, this system achieves full lifecycle health management of power equipment, focusing on precise monitoring, intelligent early warning, and efficient maintenance, providing a solid guarantee for the stable operation of military power supplies.

 

Solution Introduction

Solution Overview

This project is centered around IoT sensing devices, dedicated to collecting data from power equipment. By linking and displaying the collected data with the equipment's twin model, it achieves precise monitoring of power supply health. Its main functions include:

  • Real-time monitoring: Real-time monitoring of various operating parameters (including vibration, generator winding temperature, casing temperature, engine speed, voltage, current, ripple, surge, water temperature, current, power, oil pressure, inlet and return oil flow, coolant flow, intercooler air pressure, etc.).
  • Intelligent diagnosis: Using intelligent algorithms to establish mathematical models for fault prediction and health management of critical components of dedicated power supplies (engine, ECU, generator, controller).
  • 3D visualization: Simultaneously utilizing multi-dimensional visualization technology to display critical components hierarchically, locate fault positions, and guide installation and maintenance.

Applicable Scenarios

  • Military Power Supply Factory Test: Simulating extreme conditions to ensure equipment reliability
  • Equipment Operation and Maintenance Management: Real-time monitoring of operating status to prevent sudden failures
  • Life Prediction and Maintenance Planning: Extending equipment service life and reducing maintenance costs based on big data analysis

Core Functions

  • 100% Collection of 7 Major Categories and 41 Data Items
    The equipment has 41 available data items, including seven categories: vibration, speed, flow, temperature, power, voltage, and current. Among these, vibration, noise, and voltage are high-frequency data, infrared temperature is image data, and others are low-frequency data.
  • High-Frequency Data 4 Major Spectrograms 24 Feature Indicators Multi-dimensional Analysis
    Supports four major types of spectrograms: raw waveform analysis, acceleration spectrum analysis, velocity spectrum analysis, and trend analysis.
    Statistics include 24 feature indicators such as RMS value, peak-to-peak value, margin, kurtosis, and critical frequency peak value.
  • Deployment of 9 Major Model Categories and Over 60 Alarm Rules
    Based on multi-stage limit alarms, it covers three main types: instantaneous alarms, short-term statistical alarms, and long-term trend alarms, supporting the integration of big data models for machine learning and deep learning.
  • 5 Major Dimensions - Tiered - Health Model Solution
    Over 60 alarms cover five major dimensions: engine, generator, controller, cooling device, and other accessories. The score for each dimension is evaluated based on the severity of the alarm, achieving tiered equipment health management.

Technical Advantages

  • Digital Twin Linkage: Real-time synchronization of equipment data with twin models, achieving "virtual-real combination" for precise monitoring.
  • Edge Computing + Cloud Collaboration: Local edge computers process high-frequency data in real-time, while cloud storage and analysis support long-term decision-making.
  • Highly Compatible Hardware Ecosystem: Integrates 7 major categories and 41 data items, covering the entire area of engines, generators, and controllers.

Application Case Studies

Case One: Dedicated Power Supply Health Management System for a Military Unit

Pain Points: During the dedicated power supply testing process, equipment stability was insufficient, and failures were frequent, which could not be effectively controlled even after adopting multiple methods.
Solution: Multi-dimensional sensor sensing + intelligent algorithm models + visualization platform achieved full lifecycle health management of power equipment.
Effect: Dedicated power supply status monitoring accuracy rate over 90%, fault diagnosis accuracy rate over 80%, and remaining life stage prediction accuracy rate over 60%.

 

 

 

 

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Solution Introduction

Application Cases

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