Industrial asset management has undergone a radical transformation over the past decade. Oil analysis is no longer merely a “condition check” method; it has become a biopsy of the machine, the backbone of plant reliability, and the most powerful decision-making tool in predictive maintenance. Since 2020, the field of oil analysis has entered a new era, shaped by digital sensor technologies, AI-driven interpretations, and updated standards.
For many years, oil analysis – one of the most critical components of industrial maintenance strategies – was conducted as a laboratory-centered discipline. However, particularly during the 2023–2025 period, the field has been undergoing a major transformation driven by online sensors, AI-based decision support algorithms, data integrity protocols, and new or revised standards.
By the mid-2020s, the integration of online sensors that continuously monitor oil-related physical and chemical parameters into industrial facilities had accelerated, enabling the real-time collection and transmission of data. Sensor data is processed on edge-processing devices and transferred to cloud-based platforms, merging laboratory results with online data within a unified data structure. This development requires laboratories to address data management not only from the perspective of measurement accuracy, but also in terms of timestamp synchronization, metadata standardization, and data security.
Artificial Intelligence (AI) has begun to be used for two primary functions in oil analysis:
Failure Prediction (Predictive Modelling)
Oil analysis data, obtained throughout the process – from the personnel collecting the sample at the correct point, through operational conditions, to the qualification of the laboratory performing the analysis – is processed by machine learning algorithms to predict the expected failure modes of the equipment. When combined with indicators such as viscosity trends, elemental wear patterns, and particle distribution, AI models can uncover correlations that human analysts cannot easily detect.
Data Quality Management
Models are employed to identify measurement errors, instrument drift, and sampling inconsistencies in laboratory datasets. This approach strengthens quality assurance processes while reducing the risk of reporting inaccurate results.
Despite these benefits, the reliability of AI models depends entirely on the quality of the training data. Therefore, AI applications cannot be used with full confidence in laboratories without establishing processes for model validation, model monitoring, and explainability.
Additionally, oil analysis-related standards published by ASTM, ISO, and CEN committees underwent significant updates during the 2024–2025 period. Example analysis methods include:
- Automatic particle counting and ISO 4406 coding
- Membrane patch tests (ASTM D7843) and oxidative stability indicators
- ICP-OES elemental analysis calibration guides
- Dielectric property test methods
Compliance with these standards by laboratories is not limited to procedural updates; it also requires reassessing calibration intervals, increasing the frequency of instrument conformity tests, and enhancing personnel training. Incorporating artificial intelligence and sensor applications into laboratory practices to minimize margins of error and evaluate results from a broader perspective will make a significant contribution to reliability management in sectors such as energy, manufacturing, and beyond.
Optimizing oil consumption in field equipment and reducing unnecessary oil changes are becoming increasingly important within sustainability policies. Modern oil analysis has evolved into a directly effective tool for environmental compliance, while also extending oil life, reducing waste oil volumes, and monitoring oil degradation products. Consequently, oil analysis is no longer regarded merely as a maintenance activity but as an integral component of corporate environmental performance reporting.
References
ASTM International. (2024). Annual Book of ASTM Standards.
CEN/TC 19. (2024). Lubricants, Industrial Oils and Related Products.
Chong, S., & He, J. (2023). Machine learning applications in lubricant degradation analysis.
ISO. (2023). ISO 4406.
Mordor Intelligence. (2024). Global Oil Condition Monitoring Market Report.
Smith, R., et al. (2024). Digital transformation in lubricant condition monitoring.
Zhao, L., & Kumar, P. (2024). Real-time oil monitoring sensors.
