Automating the Oil Condition Monitoring (OCM) process

Lubricating oil serves as the lifeblood of rotating equipment used in industrial processes, facilitating peak performance, and ensuring optimal efficiency of the rotating equipment. It serves as a key repository of information on the health condition of the rotating equipment. This repository is accessed by Oil Condition Monitoring (OCM). 

What is OCM? 

OCM is a practice of analysing the lubricating oil in rotating equipment to unlock the information stored in its vast repository. OCM encompasses a wide range of sophisticated techniques such as mass spectrometry, optical spectroscopy etc., to measure lubricating oil for deterioration and contamination. It is also a means to capture any wear of the metal parts of a rotating equipment.  

The analysis of lubricating oil can be in the form of physical quality measurements such as measuring the viscosity or chemical composition analysis which gives information on change in oil chemistry, e.g., acidity, alkaline reserve, metals etc. 

Essentially, OCM provides insights on change of lubricity of the oil that results from anomalous conditions such as water ingress, fuel contamination, metal wear etc., that lead to poor lubrication, poor equipment performance and eventual failure. 

The Evolution of OCM 

OCM has traditionally been laboratory based where a sample is taken from a rotating equipment in the field and transported offsite to a laboratory for analysis. Laboratories provide the most robust information as they can perform detailed chemical composition analysis in addition to physical quality measurements of the oil. However, there is a shift away from manual traditional laboratory analysis because of innate inefficiencies. Some of these inefficiencies include time to return results (for instance, it can take 5 days to 3 weeks to get results from the laboratory), cost of regular laboratory sampling, sample mix up and contamination, large environmental footprint etc. 

Inefficiencies in traditional methods ensure that across many industries, laboratory oil analysis is only performed quarterly or monthly in the best cases. As a result, the information captured is often too late to permit condition-based maintenance (CbM). There is the added implication of low resolution of data (12 data points per year in the best cases), that restricts data trending for anomaly detection and failure prediction. Consequently, there is a concerted move to automation.  

The Drive to Automation and Digitisation of OCM 

The transformative potential of automation is illustrated in figure 1. In a marine shipping vessel case study, two 4-stroke diesel engines (i.e., portside, and starboard) were monitored daily in over 430 hours of operation, this was equivalent to one month of operation. Using one of the chemical composition parameters (acidity) for example, figure 1, highlights a sudden and continuous rise in acidity of the portside engine compared to the starboard engine over the period of monitoring. The enrichment of the acidity in the portside engine were sudden and continued beyond the onset of corrosion (i.e., an allowable level of 3.5 mgKOH/g).  

Time-series of TAN measurements

Figure 1: Illustration of acid content in two 4-stroke engines in a shipping vessel over a period of one month operation. 

Further investigation revealed a seal leak that resulted in fuel dilution which increased acidity. Such case study highlights the transformative power of automation as it permits frequent sampling and an increased resolution of data for detailed insights. Such insights would be missed with quarterly or monthly sampling. 

Most recently, proactive reliability engineers, asset and maintenance managers have been making the move towards automating their OCM process based on the insights achievable from regular testing. Costly lubrication related failures, equipment breakdowns, and associated down times have necessitated this shift in strategy. The proliferation of technologies that allow onsite or online/inline analysis has enabled this shift away from traditional manual offsite lubricating oil testing. Over the past decade, technologies like optical spectroscopy have moved outside of primarily laboratory domains into field application. This has allowed detailed chemical compositional analysis and physical quality measurements to be performed in the field by untrained users. One such technology is the microfluidic lab-on-a-chip developed by RAB-Microfluidics that allows users to test lubricating oil in real-time. 

Benefits of automating OCM  

An obvious benefit to automating the manual process of laboratory analysis is the possibility for real-time actionable equipment condition insights for real-time decision making. Engineers and maintenance teams become able to identify early lubrication related deterioration of equipment and plan maintenance accordingly. More importantly, maintenance practices can be based on the condition of the equipment at time of testing. Furthermore, predictive analysis becomes possible with higher resolution data set.  

The positive consequence of identifying lubrication related failures early and in advance of the failure is the exponential possibility to avoid the failure and resulting downtime. There is the concomitant reduction in operating and maintenance cost while increasing productivity and uptime. 


RAB-Microfluidics is pioneering the automation of chemical and physical compositional analysis of lubricating oil with its microfluidic lab-on-a-chip technology to tackle the challenges of equipment reliability and availability. 

As a Research & Development company, we’re constantly pushing the boundaries of what’s possible to deliver innovative solutions that meet the needs of our clients. 

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