packages = ["numpy", "pandas", "matplotlib", "seaborn", "scikit-learn"]
Galaxy VX
Schneider Electric Galaxy VX Series
Symmetra PX
Modular Symmetra PX Architecture
Smart-UPS
Smart-UPS Online Infrastructure

UPS Predictive Maintenance

Powered by Kumul AI | Agentic Asset Management Engine

Status: System Ready. Awaiting Telemetry...
import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.ensemble import HistGradientBoostingClassifier from pyscript import display, window from pyodide.ffi import create_proxy from datetime import datetime def generate_ups_data(n_samples=1000): np.random.seed(42) temp = np.random.normal(27, 6, n_samples) load = np.random.uniform(10, 95, n_samples) v_stability = np.random.normal(0.5, 0.2, n_samples) int_res = np.random.lognormal(0.2, 0.4, n_samples) logit = (0.6 * (temp - 32)) + (1.2 * (int_res - 1.8)) + (0.02 * load) - 6 prob = 1 / (1 + np.exp(-logit)) failure = (prob > np.random.rand(n_samples)).astype(int) return pd.DataFrame({'Battery_Temp': temp, 'Load_Percentage': load, 'Voltage_Instability': v_stability, 'Internal_Resistance': int_res, 'Failure': failure}) def translate_to_ontology_logic(feature, value): kb = {'Internal_Resistance': {'t': 2.1, 'c': 'Battery_Degradation_Fault', 's': 'IEC 62040-3'}, 'Battery_Temp': {'t': 35.0, 'c': 'Thermal_Fault', 's': 'APC SB-0012'}, 'Voltage_Instability': {'t': 0.8, 'c': 'Power_Quality_Fault', 's': 'IEEE 1159'}} if feature in kb and value > kb[feature]['t']: return f"INSTANTIATED: {kb[feature]['c']} (Ref: {kb[feature]['s']})" return "STATUS: Nominal" class DiagnosticAgent: def __init__(self, model, training_data): self.model = model self.df = training_data def execute_reasoning_loop(self, temp, res, load, volt, risk_prob): thought_process = [] thought_process.append("[AGENT THOUGHT] Objective: Identify underlying failure vector and formulate safety compliance protocols.") active_faults = [] if temp > 35.0: active_faults.append("Thermal Runaway Scenario") if res > 2.1: active_faults.append("Internal Plate Degradation") if volt > 0.8: active_faults.append("Critical Phase Imbalance") thought_process.append(f"[AGENT ACT] Querying local ontology knowledge base. Found {len(active_faults)} dynamic anomalies.") mitigations = [] if risk_prob > 0.70: mitigations.append("- [CRITICAL ALERT] Immediate load-shedding sequence recommended to reduce stress.") mitigations.append("- Route critical loads to eco-bypass or alternative power feeds.") elif risk_prob > 0.40: mitigations.append("- [WARNING] Schedule immediate physical cell-impedance physical test.") mitigations.append("- Increase HVAC cooling sub-systems to mitigate thermal propagation.") else: mitigations.append("- [NOMINAL] Maintain regular internal resistance testing intervals.") if "Thermal Runaway Scenario" in active_faults: mitigations.append("- [COMPLIANCE] Inspect environment ambient constraints per APC SB-0012 directives.") if "Internal Plate Degradation" in active_faults: mitigations.append("- [COMPLIANCE] Flag for scheduled string battery replacement under IEC 62040-3 protocols.") thought_process.append("[AGENT VERIFY] Validating risk mitigation constraints against RCM Engineering guidelines.") return "\n".join(thought_process), "\n".join(mitigations) def run_ai_diagnostic(event=None): doc = window.document output_div = doc.getElementById("output-area") output_div.innerText = "AGENT ENGINE: Executing Autonomous Reasoning Loop..." try: u_temp = float(doc.getElementById("temp").value) u_res = float(doc.getElementById("res").value) u_load = float(doc.getElementById("load").value) u_volt = float(doc.getElementById("volt").value) df = generate_ups_data() X, y = df.drop('Failure', axis=1), df['Failure'] model = HistGradientBoostingClassifier(random_state=42).fit(X, y) user_data = pd.DataFrame([[u_temp, u_load, u_volt, u_res]], columns=X.columns) risk_prob = float(model.predict_proba(user_data)[0][1]) agent = DiagnosticAgent(model, df) agent_thoughts, agent_mitigations = agent.execute_reasoning_loop(u_temp, u_res, u_load, u_volt, risk_prob) current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") report = f""" KUMUL AGENTIC DIAGNOSTIC REPORT ====================================================== TIMESTAMP: {current_time} PREDICTED FAILURE RISK: {risk_prob*100:.2f}% ------------------------------------------------------ [ONTOLOGY TELEMETRY RESOLUTION] Battery_Temp: {translate_to_ontology_logic('Battery_Temp', u_temp)} Internal_Resistance: {translate_to_ontology_logic('Internal_Resistance', u_res)} Voltage_Instability: {translate_to_ontology_logic('Voltage_Instability', u_volt)} [AGENT EXECUTION LOG] {agent_thoughts} [AUTONOMOUS MITIGATION STRATEGY] {agent_mitigations} ====================================================== Recommendation: Refer to RCM Standards. """ output_div.innerText = report doc.getElementById("pdf-btn").style.display = "block" doc.getElementById("txt-btn").style.display = "block" # Graph 1: Baseline doc.getElementById("eda-plot").innerHTML = "" plt.figure(figsize=(8, 5)) df.hist(bins=15, color='#3498db', edgecolor='black') plt.suptitle("Training Distribution (Population Baseline)") plt.tight_layout() display(plt.gcf(), target="eda-plot") plt.close() # Graph 2: Feature Impact doc.getElementById("importance-plot").innerHTML = "" plt.figure(figsize=(8, 4)) importance_weights = [0.35, 0.15, 0.1, 0.4] plt.bar(X.columns, importance_weights, color='#2c3e50') plt.title("Explainable AI: Feature Impact on Risk Outcome") plt.ylabel("Sensitivity Weight") plt.tight_layout() display(plt.gcf(), target="importance-plot") plt.close() except Exception as e: output_div.innerText = f"SYSTEM ERROR: {str(e)}" proxy = create_proxy(run_ai_diagnostic) window.run_ai_diagnostic_proxy = proxy