Oil Analysis Compact MiniLabs

Smart Lu D

Part Number: Lu D
Smart Lu D is a modular, compact oil diagnostic lab designed for on-site analysis. It integrates three essential portable modules:

PO100 – Rotating-Disc Oil Spectrometer (OES) for elemental wear metal analysis

PJ500 – Thistle-Tube Ferrospectrometer for wear particle morphology

VS800 – Portable Kinematic Viscometer compliant with ASTM D8092

Forming a versatile, field-friendly toolkit, Smart Lu D enables maintenance teams to measure wear metals, classify wear particle types, and check lubricant viscosity—all in one streamlined platform.
PO100 – Elemental Metals
RDE-OES detection of wear and additive elements (Fe, Cu, Al, Pb, Zn, Ca, P, etc.) in ppm, with no sample prep.
PJ500 – Wear Particle Imaging
Gravity-fed thistle-tube ferrography with high-gradient magnetic capture, ideal for wear diagnosis based on morphology.
VS800 – Viscosity Measurement
Portable outputs kinematic viscosity (10–350 cSt @40 °C) using only ~60 μL oil; no pretreatment needed.
Compact & Field-Ready
Lightweight, efficient, and suitable for mobile workshops or remote maintenance tasks.
Integrated Results
Delivers wear metal levels, ferrogram images, and viscosity values ready for trend tracking.
Specification Value
Modules Included PO100 (elemental OES), PJ500 (ferrographic wear analyzer), VS800 (kinematic viscometer)
Elemental Analysis RDE-OES, detects Fe, Cu, Al, Pb, Zn, Ca, P etc.; ppm-level results in <30 sec
Ferrographic Analysis Particle size 0–800 µm; gravity-based deposition on slide; auto-cleaning slide mechanism
Viscosity Range (VS800) 10–350 cSt @ 40 °C; accuracy ≤ ±3%, RSD ≤ ±3%, sample ~60 µL
Test Volume & Time Elemental: direct oil (seconds); Ferrography: 2–3 mL (minutes); Viscosity: 60 µL (seconds–minutes)
Power & Portability 110–240 V AC for PO100/PJ500; VS800 uses 24 V/2.6 Ah rechargeable battery
Data Interface Touchscreen or PC software; USB/Ethernet data export
Ideal Applications Field lubrication reliability, wear diagnostics, viscosity compliance testing, mobile predictive maintenance