For industries heavily reliant on lubricated machinery, such as maritime, energy, mining, manufacturing etc., preventing breakdowns, eliminating downtime, and increasing uptime are essential. Equipment failure can lead to lost production and unnecessary maintenance costs which impact a business’ bottom line. Consequently, Condition Monitoring (CM); a non-destructive process of gathering and analysing data on equipment operation to identify potential issues before they become catastrophic failures is critical. The right maintenance decisions eliminate risk of failure and maximise equipment efficiency, ensuring equipment reliability and availability.
Many CM programs do not achieve their full potential because the techniques used lack complementarity. For instance, the four key CM techniques; Vibration Analysis (VA), thermography, acoustics, and Oil Condition Monitoring (OCM) do not produce complementary data sets which can be overlaid/overlapped to give a complete view of engine condition. For example, VA is an automated processes whereas traditional OCM is manual and, as such does not have the same frequency of data collection. Therefore, these techniques do not produce data sets with similar resolution to be compared and used to make informed maintenance decisions.
The limitations of traditional oil condition monitoring methods
OCM involves analysing an equipment’s lubricating oil for contamination, degradation, and wear to determine the health status of the equipment. The present state-of-the-art technique for OCM is offsite analysis of manually sampled lubricating oil in a commercial laboratory. Here, a technician or engineer collects a sample of the lubricating oil from the operational equipment typically at monthly or quarterly intervals, packages the sample and transports offsite to centralised laboratories for analysis with the results taking 5 days to 3 weeks to be returned.
The traditional method of sending lube oil samples to centralised laboratories for analysis makes performing frequent or real-time analysis challenging. Such limitations have led to the proliferation of onsite diagnostic tools. Present mobile onsite equipment ranges from single oil property sensors such as water, particle, viscosity sensors to multi-oil property bench-top devices such as Fourier-Transform-Infrared spectrometers (FTIR-devices). The drawback of these systems is that they are either too simple to provide robust compositional information of the oils (e.g., single oil property sensors) or, where they do such as with FTIR, they require trained specialists to interpret spectral data using complex chemometric models and are too expensive for mass deployment. These limitations have made traditional centralised laboratory analysis the attractive de facto option despite the obvious constraint of offsite analysis.
The ‘missing link’ in condition monitoring
The data obtained from centralised laboratories, which take 5 days to 3 weeks to be returned, do not lend themselves to Condition-based Maintenance (CbM) or Predictive Maintenance (PdM) practices. Using outdated data to inform maintenance actions lead to inefficiencies, hence increased reliance on more real-time information provided by other CM techniques. However, these alternative CM techniques by their nature give information on equipment deterioration at an advanced stage of the degradation. For instance, an incipient fault that leads to elevated temperature of an equipment is often at an advanced stage when it becomes detectable by thermography. Conversely, corrosion or abrasive wear often begin gradually with wear particles deposited in the lubrication system and enriched overtime as the wear increases. This can be picked up early in an OCM process and tracked over time with frequent or real-time monitoring. The Potential failure to Functional failure (P-T) curve in figure 1 illustrates this clearly.
Potential failure to functional failure (P-F) curve. Source: Texas Instruments 2018For true ‘early’ detection of potential failure, frequent or real-time chemical and physical compositional analysis of the lubricating oil is thus indispensable. Such frequent or real-time compositional analysis is the missing link in the suite of CM techniques.
The transformative potential of automated lubricating oil analysis
Automating chemical and physical compositional analysis of the lubricating oil address the limitations in current OCM methods. Such automation is akin to bringing the laboratory to the equipment rather than taking the sample to centralised laboratories as currently practiced. Automation in this way allows for contamination, degradation, and wear data to be collected at a significantly higher frequency that permits complementarity to other CM techniques. It then becomes possible to create algorithms and utilise machine learning techniques to forward model machinery behaviour based on such high-resolution datasets. This creates the potential to completely transform the CM process and drive maintenance strategies to Condition-based Maintenance (CbM) or Predictive Maintenance (PdM) strategies.
Overall, the automation of lubricating oil analysis presents an exciting opportunity for equipment operators to improve reliability and availability of their equipment through data driven insights. Does your CM programme include automated chemical and physical compositional analysis of the lubricating oil?
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.
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