← LabML INTELLIGENCE
FeldOSX · Federated ML Engine

ML-Driven Intelligence

Federated learning across all nodes — models never leave the chip. Predictive quality control, dissolution profile deviation detection, and instrument health optimisation running on-device, 24/7.

12
Active Edge Models
0
High-Risk Instruments
168d
Avg Instrument RUL
96.3%
Model Accuracy
Predictive Quality & Instrument Health
Updated every 3 min · FeldOSX edge sync
Trust Gen2 Dissolution Tester
M001 · Analytical
MEDIUM
Failure Probability37%
Dissolution Profile Deviation5/100
→Paddle alignment check due / RPM variance detected — schedule in next 48h
RUL: 151d▼ expand
Automated Sample Preparation Unit
M002 · Electrical
MEDIUM
Failure Probability27%
Dissolution Profile Deviation32/100
→Pipetting variance detected — recalibrate within 2 weeks
RUL: 175d▼ expand
Stability Testing Chamber
M003 · Thermal
MEDIUM
Failure Probability31%
Dissolution Profile Deviation68/100
→Calibration drift — re-zero sensors at next idle window
RUL: 166d▼ expand
HPLC Analysis Line 1
M004 · Calibration
LOW
Failure Probability20%
Dissolution Profile Deviation43/100
→Retention time shift detected — inspect column condition
RUL: 192d▼ expand
UV-Vis Spectrophotometer
M005 · Fluidic
MEDIUM
Failure Probability30%
Dissolution Profile Deviation16/100
→Thermal envelope optimisation available — reduce temp by 6°C
RUL: 168d▼ expand
LIMS Data Ingestion Pipeline
M006 · Optical
MEDIUM
Failure Probability38%
Dissolution Profile Deviation16/100
→UV lamp intensity degraded — inspect for accuracy above 99.8%
RUL: 149d▼ expand
Tablet Compression Simulator
M007 · Analytical
MEDIUM
Failure Probability31%
Dissolution Profile Deviation36/100
→Dissolution media viscosity drifted — reagent flush recommended
RUL: 166d▼ expand
Dissolution Media Preparation System
M008 · Electrical
MEDIUM
Failure Probability28%
Dissolution Profile Deviation40/100
→Wavelength calibration drift — recalibrate optics module
RUL: 173d▼ expand
Instrument Health Matrix
M001
63%
Trust Gen2
MEDIUM
M002
73%
Automated Sample
MEDIUM
M003
69%
Stability Testing
MEDIUM
M004
80%
HPLC Analysis
LOW
M005
70%
UV-Vis Spectrophotometer
MEDIUM
M006
62%
LIMS Data
MEDIUM
M007
69%
Tablet Compression
MEDIUM
M008
72%
Dissolution Media
MEDIUM
AI Process Optimisation Recommendations
4 scenarios computed
Scheduling
+4.1% OEE
Shift Schedule Rebalancing

Move Packaging line to 06:00 shift — reduces idle-to-active ramp time by 18 min

87%
confidence
Estimated savings: 18 min/shift
Energy
−12% energy
Predictive Pre-heat M007

Start hydraulic warm-up 20 min before scheduled use window — avoids cold-start losses

92%
confidence
Estimated savings: ~8.4 kWh/day
Quality
+0.7% quality
Vision System Batch Grouping

Group similar part profiles per inspection cycle — reduces false reject rate

95%
confidence
Estimated savings: ~3.2% reject ↓
Electrical
−8% peak draw
Load Levelling Zone B→D

Stagger conveyor & press start times to avoid demand peaks and reduce energy cost

79%
confidence
Estimated savings: ~14 kW peak ↓