NewSeries of Testing Devices for Oil Analysis, Condition Monitoring, and Maintenance and Reliability Programs

The Spectro (USA) LaserNet Fines (“LNF”) Q200 Series is a powerful analytical platform for oil analysis, condition monitoring and maintenance and reliability programs. It provides a particle counter, wear particle classifier, and ferrous monitor all in one scalable and upgradeable package. The LNF technology, developed through a partnership with the U.S. Navy, provides particle counts and codes, dirt ingress, abnormal wear classification, ferrous wear measurement, and free water calculation. The Q200 is simple to use and yields rapid results.

“These instruments allow operators to monitor equipment conditions quickly and easily. All devices in the LNF Q200 Series require no calibration and feature an intuitive, easy-to-use GUI so that operator training can be accomplished in hours, not days,” explained Spectro Scientific President and CEO Brian Mitchell.

The LNF Q200 Series is now available in three configurations to best meet the needs of different facilities. The Q210 features an industry best particle counter with the unique capability to segregate wear particles from dirt ingress while the Q220 adds the LNF automatic shape classifier. The Q230 configuration includes the particle counter, automatic shape classifier, and a magnetometer that quantifies and trends ferrous content in the form of an actual calibrated measurement of the ferrous content in PPM by volume. Viscosity measurement and an automatic sample changer are available on all models.

The Q200 series is designed specifically for in-service lubrication oil analysis. The system works on dark fluids containing up to 5 million particles per milliliter or 2 percent soot with automatic laser control. It differentiates between water and air bubbles, providing error corrections for all. Commercial lab managers and PdM managers will appreciate the Q200’s ability to calculate free water, differentiate contaminants (silica) from machine wear (metal) and classify wear particle shape by identifying the type of machinery wear, wear mode, and the potential source.