Introduction: Military Quality, Intelligent Escort

In the military field, the reliability of power supply equipment is directly related to national security and mission success. Facing the potential risks of externally purchased and assembled power supplies, a dedicated power supply health management system has emerged. Relying on IoT perception and digital twin technology, this system realizes the whole life cycle health management of power supply equipment, with precise monitoring, intelligent early warning, and efficient maintenance as the core, providing a solid guarantee for the stable operation of military power supplies.

Solution Introduction

Solution Overview

This project focuses on IoT sensing equipment and is committed to collecting data from power supply equipment. By linking the collected data with the twin model of the equipment for display, it achieves precise monitoring of the power supply's health status. Its main functions include:

• Real-time monitoring: Real-time monitoring of its operating parameters (including vibration, generator winding temperature, case temperature, engine speed, voltage, current, ripple, surge, water temperature, current, power, oil pressure, inlet and return oil flow, coolant flow, intercooler pressure, etc.).
• Intelligent Diagnosis: Using intelligent algorithms to establish mathematical models for fault prediction and health management of key components of dedicated power supplies (engines, ECUs, generators, controllers).
• Three-dimensional visualization: At the same time, using multi-dimensional visualization technology, it performs hierarchical display of its key components, locates fault locations, and guides installation and maintenance.

Applicable Scenarios

• Military power supply factory testing: Simulate 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: Based on big data analysis, extend equipment life and reduce maintenance costs

Core Functions

• 100% Collection of 41 Data Items in 7 Categories
  There are 41 items of available equipment data, including seven categories: vibration, speed, flow, temperature, power, voltage, and current. Among them, vibration, noise, and voltage are high-frequency data, infrared temperature is image data, and others are low-frequency data.
• High-Frequency Data: Multi-Dimensional Analysis of 4 Major Spectrums and 24 Feature Indicators
  Supports four major types of spectrum analysis: original 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 key frequency peak.
• Deployment of Over 60 Alarm Rules in 9 Categories of Models
  Based on multi-stage limit alarms, it covers three major types: instantaneous alarms, short-term statistical alarms, and long-term trend alarms, and supports the access of big data models for machine learning and deep learning.
• 5 Dimensions—Step-Type—Health Model Solution
  Over 60 alarms cover five dimensions: engine, generator, controller, cooling device, and other accessories. The score of each dimension is evaluated according to the severity of the alarm to realize step-type equipment health management.

Technical Advantages

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

Application Case

Case 1: Health Management System for Dedicated Power Supplies in a Military Unit

Pain points: During the dedicated power supply test, the equipment was not stable enough and failures occurred frequently, and it could not be effectively controlled after using various methods.
Solution: Multi-dimensional sensor perception + intelligent algorithm model + visualization platform realize the whole life cycle health management of power supply equipment.
Effect: The accuracy rate of dedicated power supply status monitoring is over 90%, the accuracy rate of fault diagnosis is over 80%, and the accuracy rate of remaining life stage prediction is over 60%.