0.5×← Perlahan · Cepat →
💬Tekan Play Demo untuk lihat bagaimana Machine Alarm Intelligence Agent mengesan pola alarm.
Demo Complete — All Stages Generated
01 WhatsApp Command
02 Requirement Analysis
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03 Python Script Generation
1
📥
Ambil log alarmFetch alarm logs from PLC
2
🔗
Korelasi alarmCorrelate by timestamp
3
🧠
Cari punca utamaIdentify root cause patterns
4
📊
Laporan & cadanganGenerate report with recommendations
import pandas as pd
from datetime import datetime
def load_alarms(csv_file):
df = pd.read_csv(csv_file)
return df.sort_values('timestamp')
def correlate_alarms(df, window=5):
df['window'] = df.groupby('machine')['timestamp'].diff().abs().le(window)
return df[df['window']]
alarms = load_alarms('alarms.csv')
correlated = correlate_alarms(alarms)
root_causes = correlated[correlated['severity'] == 'critical']
print(json.dumps({'total':len(alarms), 'correlated':len(correlated), 'root_causes': len(root_causes)}))
04 Block Diagram
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05 System Schematic
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⚠️ Alarm correlation confidence depends on accurate PLC timestamps; validate before action.
06 Simulasi Alarm Intelligence
Mula Analisis
07 Alarm Intelligence Report
Terminal Log
[SISTEM] Machine Alarm Intelligence Agent sedia
System Architecture
🤖 Agent Layer
Alarm fetcher
Correlation engine
Root cause analyzer
⚙️ Manufacturing Layer
PLC alarm logs
Machine DB
Alert system
Reports
Safety Notes
- Do not act on alarm correlations without verifying PLC timestamps.
- Configure escalation for critical unresolved alarms.
- MTTR targets must align with production SLAs.
- Browser demo uses simulated data only.