| |
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| | import pandas as pd
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| | import plotly.graph_objects as go
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| | from plotly.subplots import make_subplots
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| | import base64
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| | import json
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| | from langchain_core.messages import HumanMessage
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| | import streamlit as st
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| |
|
| | def get_defect_prompt(data_str):
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| | return f"""
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| | System Role: Principal DCRM & Kinematic Analyst
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| | Role:
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| | You are an expert High-Voltage Circuit Breaker Diagnostician. Your task is to interpret Dynamic Contact Resistance (DCRM) traces to detect specific electrical and mechanical faults.
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| |
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| | Critical "Anti-Overfitting" Directive:
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| | You must distinguish between Systematic Defects and Artifacts.
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| | True Degradation: Flag issues only when the visual signature is statistically significant and exceeds the "noise floor."
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| |
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| | Capability:
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| | Identify Multiple Concurrent Issues if present. (e.g., A breaker can have both misalignment and contact wear).
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| | there will mostly be 3 line charts in the input
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| | green resistance profile
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| | blue current profile
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| | red travel profile
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| |
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| | Data (Sampled): {data_str}
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| |
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| | 1. Diagnostic Heuristics & Defect Taxonomy
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| | Map the visual DCRM trace to ONLY the following defect types. Use the specific Visual Heuristics to confirm detection.
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| |
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| | Defect Type | Visual Heuristic (The "Hint") | Mechanical Significance (Root Cause)
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| | --- | --- | ---
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| | Main Contact Issue (Corrosion/Oxidation) | "The Significant Grass"<br>In the fully closed plateau, look for pronounced, erratic instability. <br>• Ignore: Uniform, low-amplitude fuzz (sensor noise).<br>• Flag: Jagged, irregular peaks/valleys with significant amplitude (e.g., > 15–20 μΩ variance). The trace looks like a "rough rocky road," not just a "gravel path." | Surface Pathology: The Silver (Ag) plating is compromised (fretting corrosion) or heavy oxidation has occurred. The current path is constantly shifting through microscopic non-conductive spots.
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| | Arcing Contact Wear | "Big Spikes & Short Wipe"<br>Resistance spikes are frequent and significantly large (high amplitude). Crucially, the duration of the arcing zone (the time between first touch and main contact touch) is noticeably shorter than expected. | Ablation: The Tungsten-Copper (W-Cu) tips are heavily eroded. The contact length has physically diminished, risking failure to commutate current during opening.
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| | Misalignment (Main) | "The Struggle to Settle"<br>There are significant, high-amplitude peaks just before the trace tries to settle into the stable plateau. These are not bounces; they are "struggles" to mate that persist longer than 3-5ms. | Mechanical Centering: The moving contact pin is hitting the side or edge of the stationary rosette fingers before forcing its way in. Caused by loose nuts, kinematic play, or guide ring failure.
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| | Misalignment (Arcing) | "Rough Entry"<br>Erratic resistance spikes occurring specifically during the initial entry (commutation), well before the main contacts engage. | Tip Eccentricity: The arcing pin is not entering the nozzle concentrically. It is scraping the nozzle throat or hitting the side, indicating a bent rod or skewed interrupter.
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| | Slow Mechanism | "Stretched Time"<br>The entire resistance profile is elongated along the X-axis. Events happen later than normal. | Energy Starvation: Low spring charge, hydraulic pressure loss, or high friction due to hardened grease in the linkage.
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| |
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| | 2. Analysis Logic (The "Signal-to-Noise" Filter)
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| | Before declaring a defect, run these logic checks:
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| | The "Noise Floor" Test (For Main Contacts):
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| | Is the plateau variance uniform and small (< 10 μΩ)? -> Classify as Healthy (Sensor/Manufacturing artifact).
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| | Is the variance erratic, jagged, and large (> 15 μΩ)? -> Classify as Corrosion/Oxidation.
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| | The "Duration" Test (For Misalignment):
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| | Are the pre-plateau peaks < 2ms? -> Ignore (Benign Bounce).
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| | Do the peaks persist > 3-5ms before settling? -> Classify as Misalignment.
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| | The "Combination" Check:
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| | Does the trace show both "Rough Entry" AND "Stretched Time"? -> Report Both (Misalignment + Slow Mechanism).
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| |
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| | 3. Output Structure
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| | Provide a concise Executive Lead followed by the JSON.
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| |
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| | Executive Lead (3-4 Lines)
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| | Status: Healthy | Warning | Critical.
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| | Key Findings: Summary of valid defects found (ignoring sensor noise).
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| | Action: "Return to service" or specific repair instruction.
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| |
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| | JSON Schema
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| | Return ONLY this JSON object:
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| | {{
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| | "image_url": "string",
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| | "overall_condition": "Healthy|Warning|Critical",
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| | "executive_lead": "string (The 3-4 line summary)",
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| | "detected_issues": [
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| | {{
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| | "issue_type": "Main Contact Issue (Corrosion/Oxidation)|Arcing Contact Wear|Misalignment (Main)|Misalignment (Arcing)|Slow Mechanism",
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| | "confidence": "High|Medium|Low",
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| | "visual_evidence": "string (e.g., 'Plateau instability >20 micro-ohms detected, exceeding sensor noise threshold.')",
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| | "mechanical_significance": "string (Root cause from table)",
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| | "severity": "Low|Medium|High"
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| | }}
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| | ],
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| | "analysis_metrics": {{
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| | "static_resistance_Rp_uOhm": "float",
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| | "signal_noise_level": "Low (Sensor/Mfg)|High (Defect)",
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| | "wipe_quality": "Normal|Short|Erratic"
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| | }},
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| | "maintenance_recommendation": "string"
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| | }}
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| | """
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| |
|
| | def perform_defect_analysis(df, llm):
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| | """
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| | Performs advanced defect analysis using the specialized DCRM prompt.
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| | """
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| | try:
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| |
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| | data_str = df.to_string(index=False)
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| | prompt_text = get_defect_prompt(data_str)
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| |
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| |
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| | fig = make_subplots(specs=[[{"secondary_y": True}]])
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| | fig.add_trace(go.Scatter(x=df['Time_ms'], y=df['Current'], name="Current", line=dict(color='blue')), secondary_y=False)
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| | fig.add_trace(go.Scatter(x=df['Time_ms'], y=df['Resistance'], name="Resistance", line=dict(color='green')), secondary_y=False)
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| | fig.add_trace(go.Scatter(x=df['Time_ms'], y=df['Travel'], name="Travel", line=dict(color='red')), secondary_y=True)
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| | fig.update_layout(title="DCRM Graph for Defect Analysis", showlegend=True)
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| |
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| |
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| | img_bytes = fig.to_image(format="png", width=1024, height=600)
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| | base64_image = base64.b64encode(img_bytes).decode('utf-8')
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| |
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| |
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| | message = HumanMessage(
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| | content=[
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| | {"type": "text", "text": prompt_text},
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| | {
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| | "type": "image_url",
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| | "image_url": {"url": f"data:image/png;base64,{base64_image}"}
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| | }
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| | ]
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| | )
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| |
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| |
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| | response = llm.invoke([message])
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| | content = response.content.replace("```json", "").replace("```", "").strip()
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| |
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| |
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| | try:
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| | start_idx = content.find("{")
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| | end_idx = content.rfind("}") + 1
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| | if start_idx != -1 and end_idx != -1:
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| | json_str = content[start_idx:end_idx]
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| | result = json.loads(json_str)
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| | else:
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| | raise ValueError("No JSON found in response")
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| | except json.JSONDecodeError:
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| |
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| | result = json.loads(content)
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| |
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| | return result
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| |
|
| | except Exception as e:
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| | st.error(f"Defect Analysis failed: {str(e)}")
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| | return {
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| | "overall_condition": "Unknown",
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| | "executive_lead": f"Analysis failed due to error: {str(e)}",
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| | "detected_issues": [],
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| | "analysis_metrics": {},
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| | "maintenance_recommendation": "Check system logs."
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| | }
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| |
|
