Spectro Scientific Blog

Introduction 

This article reports annual cost savings from several effective lubrication programs using onsite oil analysis. These published case histories cover various industries. These lubrication programs employ the following steps that differentiate effective lubrication programs with excellent cost avoidance from ineffective, low cost avoidance lubrication programs.

Retesting And Resampling Can Save Millions

Introduction 

Power plants today face headwinds in delivering power to customers. Power suppliers must meet the “Three Rs” for power system reliability: resource adequacy (people, equipment, facilities, availability), system resiliency (ability to bounce back from disruption) and operational reliability (assets performing without unplanned downtime).1

Exploration Rigs insist on Oil Analysis 

For many years, rig operators have used oil analysis as an essential tool for routine maintenance and future cost avoidance. On oil rigs, equipment failures risk employee safety, and missed production targets are realized very quickly. The remote locations of offshore rigs make routine maintenance very expensive. In most cases, skilled people and supplies can only reach the platform by ship or helicopter so the cost of bringing technical specialists, replacement equipment, spare parts and tools to the platform is high.

Introduction 

Downtime for critical equipment is costly in terms of dollar expense, productivity and human lives. Oil analysis is one of the most important and reliable techniques to keep these assets up and running.

Introduction 

This article is the first in a series to make a strong case for performing more and better testing of in service lubricants in order to replace unplanned repairs and unplanned downtime with planned repairs during scheduled time. In other words, this article is about using oil analysis to help improve reliability, planning and scheduling. More and better testing involves more lubricant sample points with better focus on large wear particle analysis.

Introduction 

Comprehensive oil analysis requires sampling, testing and analyzing in-service industrial lubricants to evaluate the wear condition of machinery, the contamination condition of lubrication systems, and chemistry condition of the lubricants. This article intentionally focuses attention on analysis of large wear particles because the resulting analysis information enables informed users time for planned maintenance instead of surprise, reactive unplanned repairs.

Practical Steps to Increase Your Site’s Ability to Detect
and Manage Abnormal Lubrication Events

There is a lot of discussion about artificial intelligence (AI), internet of things (IoT) and how these can be applied in all areas of reliability programs, particularly oil analysis. In this technology, many users struggle to understand and trust how alarms are set, maintained, and adapted to meet the changing conditions with lubricated mechanical equipment. All of these intelligence initiatives are ineffective if the data is not trustworthy.

Spectro Scientific's Oil Analysis Handbook

This Oil Analysis Handbook covers a wide variety of topics ranging from specific oil analysis techniques to applications for oil analysis, to success stories of companies that use on-site oil analysis to lower costs and improve uptime. The applications for oil analysis range from mining and trucking to water treatment, service providers, and industrial plants.  Learn how to improve reliability while cutting maintenance costs and avoiding costly downtime.

Ametek Spectro Scientific and Grabner Instruments recently released Fluid Management Academy, an online learning management system offering courses for Spectro Scientific and Grabner Instruments’ products and software.

In a recent plant reliability survey, between 60 to 70% of industrial facilities consider oil analysis an important part of their reliability programs[1]. Oil analysis gives a snapshot of machinery health, preventing unnecessary oil changes and assisting in predicting equipment failures. This paper will take a detailed look into using data to decrease maintenance costs and increase the bottom line. Being able to extend oil drains or even shorten them to eliminate failures, can be an easy way to reduce maintenance costs, but data must be available that allows for making those decisions. This paper will address the role of key performance indicators (KPI’s) in predictive maintenance, how to gather useful data that aligns with KPI’s and review a few case studies where on-site labs were able to use data to take advantage of warranty periods, justify keeping assets after warranty and extend oil drains to reduce oil consumption.