Akwai IEE-Business na ƙwarewa da kula da alaƙar daidaito da tushen gida
Ⅰ. Gwandar kaɗi da Ƙauna
Idan kungiyoyin gagarwa ta zama suka fi yawa da tushen jirgin sama, modelon ingancin karatu na tsawo suna da abu a gina yaɗu kan buƙata kan mafi girman gagarwa:
• Lokaci na Ƙarin Yadda Ake Jira Farkon Ƙauna: Tana iya gano ƙarin yadda ake jira farkon ƙauna ko kuma ƙarin yadda ake cika ƙauna a baya ba
• Maiyawa mai Yawan Inganci: Inganci mai yawa take taka ƙaramin mutane, amma inganci mai lili take taka wani lokacin da aka yi ƙauna
• Tsarin Bincike mai Yawan Al'amari: Al'adu daga DGA (Bincike Gas Mai Tsakiyar), binciken ƙarin yadda ake jara, kuma wasu babban al'adun da suka ɗauki tsarin bincike mai yawan al'amari
II. Tashar Sistem da Tattalin Kudin
(1) Tashar Nemanin Mai Yawan Zanani
Yana kafa masu nemanin IoT na manyan kafin:
graph LR
A[Winding Fiber Optic Temp] --> D[Central Analytics Platform]
B[DGA Sensor] --> D
C[Vibration/Noise Monitor] --> D
E[Core Grounding Current Detector] --> D
(2) AI Analytics Engine
|
Module |
Core Tech |
Function |
|
Condition Assessment |
DBN (Deep Belief Network) |
Integrates SCADA/online data to generate health indices |
|
Fault Warning |
LSTM Time-Series Analysis |
Predicts hotspot trends based on temperature/load rates |
|
Life Prediction |
Weibull Distribution |
Quantifies insulation paper degradation curves |
(3) Predictive Maintenance Platform
• 3D Dashboard: Real-time display of transformer load rates, hotspot temps, and risk levels
• Maintenance Decision Tree: Auto-generates work orders based on risk ratings
(e.g., C₂H₂>5μL/L & CO/CO₂>0.3 → Triggers bushing looseness inspection)
III. Tabelin Kudin Kudin
|
Function |
Technical Implementation |
O&M Value |
|
Panoramic Monitoring |
Edge-computing gateways (10ms data acquisition) |
100% device status visualization |
|
Smart Diagnostics |
IEEE C57.104 + AI correction |
92% fault identification accuracy |
|
Predictive Maintenance |
RUL prediction via degradation modeling |
25% lower maintenance costs |
|
Knowledge Retention |
Self-iterating fault case database |
60% faster new staff training |
IV. Tattalin Kudin
- Multi-physics Coupling Analysis:
EM-thermal-stress simulation data fed into AI models for early winding deformation alerts (±0.5mm precision) - Blockchain Certification:
O&M records and test data stored on-chain for ISO 55000 compliance - AR-assisted Repair:
Hololens overlays 3D fault-point positioning → 40% faster critical repairs
V. Abubuwan Da Suka Samu (Case 1,000MW Plant)
|
Metric |
Pre-upgrade |
Post-upgrade |
Improvement |
|
Unplanned Outages |
3.2/yr |
0.4/yr |
↓87.5% |
|
Avg. Repair Time |
72 hrs |
45 hrs |
↓37.5% |
|
Life Prediction Error |
±18 months |
±6 months |
↑67% accuracy |
