| Résumé | The performance, efficiency and lifetime of modern combustion engines significantly depend on the quality of the engine lubricants. However, contaminants, such as gasoline, moisture, coolant and wear particles, reduce the life of engine mechanical components and lubricant quality. Therefore, direct and indirect measurements of engine lubricant properties, such as physical-mechanical, electro-magnetic, chemical and optical properties, are intensively utilized in engine condition monitoring systems and sensors developed within the last decade. Such sensors for the measurement of engine lubricant properties can be used to detect a functional limit of the in-use lubricant, increase drain interval and reduce the environmental impact. This paper proposes two new methodologies for the quantitative and qualitative analysis of the presence of contaminants in the engine lubricants. The methodologies are based on optical analysis of the distortion effect when an object image is obtained through a thin random optical medium (e.g. engine lubricant). The novelty of the proposed methodologies is in the introduction of an object with a known periodic shape behind a thin film of the contaminated lubricant. In this case, an acquired image represents a combined lubricant-object optical appearance, where an a priori known periodic structure of the object is distorted by a contaminated lubricant. In the object shape-based optical analysis, several parameters of an acquired optical image, such as the gray scale intensity of lubricant and object, shape width at object and lubricant levels, object relative intensity and width non-uniformity coefficient are newly proposed. Variations in the contaminant concentration and use of different contaminants lead to the changes of these parameters measured on-line. In the statistical optical analysis methodology, statistical auto- and cross-characteristics (e.g. auto- and cross-correlation functions, auto- and cross-spectrums, transfer function, coherence function, etc) are used for the analysis of combined object-lubricant images. Both proposed methodologies utilize the comparison of measured parameters and calculated object shape-based and statistical characteristics for fresh and contaminated lubricants. Developed methodologies are verified experimentally showing an ability to distinguish lubricant with 0%, 3%, 7% and 10% water and coolant contamination. This proves the potential applicability of the developed methodologies for on-line measurement, monitoring and control of the engine lubricant condition. © 2012 IOP Publishing Ltd. |
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