packages = ["numpy", "pandas", "matplotlib"]

KumulCloud Edge Intelligence

ISO 55001 / RCM Framework
Asset Telemetry Injector Node
27
1.2
50
0.2
Inference System Terminal
SYSTEM READY: Adjust inputs above and trigger the diagnostic loop...

DSP Spectrum
No analytics plotted. Execute system run...
Localized Shifts
No analytics plotted. Execute system run...
import numpy as np import pandas as pd import matplotlib.pyplot as plt from pyscript import display, window from pyodide.ffi import create_proxy from datetime import datetime def generate_raw_voltage_wave(harmonic_distortion_factor, noise_level=0.05): t = np.linspace(0, 0.1, 1000) fundamental = np.sin(2 * np.pi * 50 * t) third_harmonic = harmonic_distortion_factor * np.sin(2 * np.pi * 150 * t) noise = np.random.normal(0, noise_level, len(t)) return fundamental + third_harmonic + noise def extract_fft_thd(wave): fft_vals = np.abs(np.fft.rfft(wave)) fundamental_mag = fft_vals[5] third_harmonic_mag = fft_vals[15] if fundamental_mag == 0: return 0.0 return float(third_harmonic_mag / fundamental_mag) def generate_ups_population_baseline(n_samples=500): np.random.seed(42) temp = np.random.normal(27, 5, n_samples) int_res = np.random.lognormal(0.2, 0.3, n_samples) degradation_factor = 0.1 + (0.3 * (int_res - 1.0)) + np.random.uniform(0, 0.2, n_samples) degradation_factor = np.clip(degradation_factor, 0.0, 1.5) thd_features = [] for dist in degradation_factor: wave = generate_raw_voltage_wave(harmonic_distortion_factor=dist) thd_features.append(extract_fft_thd(wave)) df = pd.DataFrame({ 'Battery_Temp': temp, 'Internal_Resistance': int_res, 'V_3rd_Harmonic_THD': thd_features }) logit = (0.5 * (df['Battery_Temp'] - 32)) + (2.5 * (df['Internal_Resistance'] - 1.8)) + (8.0 * df['V_3rd_Harmonic_THD']) - 2.0 prob = 1 / (1 + np.exp(-logit)) df['Failure'] = (prob > np.random.rand(n_samples)).astype(int) return df class ExplanatoryGlassBoxGAM: def __init__(self): self.w_temp = 0.5 self.w_res = 2.5 self.w_thd = 8.0 self.intercept = -2.0 def predict_risk(self, temp, res, thd, load_factor=1.0): f_temp = self.w_temp * (temp - 32) f_res = self.w_res * (res - 1.8) f_thd = self.w_thd * thd f_load = 0.02 * (load_factor - 50.0) logit = self.intercept + f_temp + f_res + f_thd + f_load prob = 1 / (1 + np.exp(-logit)) contributions = { 'Battery_Temp': f_temp, 'Internal_Resistance': f_res, 'V_3rd_Harmonic_THD': f_thd, 'Infrastructure_Load': f_load, 'Base_Intercept': self.intercept } return float(prob), contributions class DiagnosticAgent: def __init__(self, model): self.model = model def execute_reasoning_loop(self, temp, res, thd, load, risk_prob, contributions): thought_process = [] thought_process.append("[THOUGHT] Objective: Deconstruct glass-box risk vectors and verify compliance boundaries.") primary_driver = max(contributions, key=contributions.get) thought_process.append(f"[ACT] Auditing local logit paths. Primary failure driver isolated: {primary_driver} (+{contributions[primary_driver]:.2f} logit points).") mitigations = [] if risk_prob > 0.70: mitigations.append("- [CRITICAL ALERT] Immediate load-shedding sequence recommended to reduce thermal and harmonic strain.") mitigations.append("- Isolate asset immediately and route critical facility loads to static alternative bypass.") elif risk_prob > 0.40: mitigations.append("- [WARNING] Schedule engineering cell-impedance profiling within 48 hours.") mitigations.append("- Optimize HVAC chilling runtime profile to counter internal thermodynamic cell stress.") else: mitigations.append("- [NOMINAL] Maintain standard Reliability-Centered Maintenance (RCM) telemetry cycles.") if temp > 35.0: mitigations.append("- [COMPLIANCE - THERMAL] Flagged ambient boundary breach per APC SB-0012 directives.") if res > 2.1: mitigations.append("- [COMPLIANCE - MECHANICAL] Flagged internal plate degradation under strict IEC 62040-3 asset lifecycle rules.") if thd > 0.15: mitigations.append("- [COMPLIANCE - POWER QUALITY] 3rd harmonic threshold exceeded. Remediate inverter switching gates per IEEE 1159 protocols.") if load > 85.0: mitigations.append("- [RISK - OVERLOAD] Infrastructure load operating inside vulnerable safety margins.") thought_process.append("[VERIFY] Validating risk mitigation constraints against RCM engineering matrix.") 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 = "KUMUL PLATFORM ENGINE: Processing Signal FFT & Executing Glass-Box Inference..." try: u_temp = float(doc.getElementById("temp").value) u_res = float(doc.getElementById("res").value) u_load = float(doc.getElementById("load").value) u_distortion_knob = float(doc.getElementById("volt").value) simulated_wave = generate_raw_voltage_wave(harmonic_distortion_factor=u_distortion_knob) u_thd = extract_fft_thd(simulated_wave) df_population = generate_ups_population_baseline() model = ExplanatoryGlassBoxGAM() risk_prob, local_contributions = model.predict_risk(u_temp, u_res, u_thd, u_load) agent = DiagnosticAgent(model) agent_thoughts, agent_mitigations = agent.execute_reasoning_loop(u_temp, u_res, u_thd, u_load, risk_prob, local_contributions) current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") report = f"""KUMUL AI DIAGNOSTIC REPORT =========================================================================== TIMESTAMP: {current_time} | PLATFORM: KumulCloud Enterprise v2.5 PREDICTED CRITICAL FAILURE RISK: {risk_prob*100:.2f}% --------------------------------------------------------------------------- [DIGITAL SIGNAL PROCESSING (DSP) SPECTRUM DATA] Raw Hardware Voltage Sampling: Complete (10kHz, 1000pt block) Isolated 3rd Harmonic V_Distortion (150Hz): {u_thd*100:.2f}% Total Energy Current Active Operating Load Baseline: {u_load:.1f}% [MATRICES] Feature Impact Scores (f_i): -> Baseline Constant (Intercept): {local_contributions['Base_Intercept']:.2f} -> Battery Thermal Contribution: {local_contributions['Battery_Temp']:.2f} -> Internal Plate Resistance: {local_contributions['Internal_Resistance']:.2f} -> 3rd Harmonic Frequency Impact: {local_contributions['V_3rd_Harmonic_THD']:.2f} -> Load Environment Variance: {local_contributions['Infrastructure_Load']:.2f} [EXECUTION REASONING LOG] {agent_thoughts} [AUTONOMOUS ASSET MITIGATION STRATEGY] {agent_mitigations} =========================================================================== Recommendation: Refer to RCM Standards / ISO 55001 Asset Management Framework.""" output_div.innerText = report doc.getElementById("pdf-btn").style.display = "block" doc.getElementById("txt-btn").style.display = "block" # Graph 1 doc.getElementById("eda-plot").innerHTML = "" plt.figure(figsize=(6, 4.2)) df_population.hist(bins=15, color='#1abc9c', edgecolor='black', grid=False) plt.suptitle("Training Distribution (Population Baseline)", fontsize=10) plt.tight_layout() display(plt.gcf(), target="eda-plot") plt.close() # Graph 2 doc.getElementById("importance-plot").innerHTML = "" plt.figure(figsize=(6, 3.5)) features = ['Battery_Temp', 'Internal_Resistance', 'V_3rd_Harmonic_THD', 'Infrastructure_Load'] impacts = [local_contributions['Battery_Temp'], local_contributions['Internal_Resistance'], local_contributions['V_3rd_Harmonic_THD'], local_contributions['Infrastructure_Load']] colors = ['#e74c3c' if x > 0 else '#2ecc71' for x in impacts] plt.barh(features, impacts, color=colors, edgecolor='black') plt.axvline(x=0, color='black', linestyle='--', alpha=0.7) plt.title("Local Explanatory Boundary: Logit Shift Matrix", fontsize=10) plt.xlabel("Logit Point Impact Calculation") 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