Object shape-based optical sensing methodology and system for condition monitoring of contaminated engine lubricants

Abstract

Presence of contaminants, such as gasoline, moisture, and coolant in the engine lubricant indicates mechanical failure within the engine and significantly reduces lubricant quality. This paper describes a novel sensing system, its methodology and experimental verifications for analysis of the presence of contaminants in the engine lubricants. The sensing methodology is based on the statistical shape analysis methodology utilizing optical analysis of the distortion effect when an object image is obtained through a thin random optical medium. The novelty of the proposed sensing system lies within the employed methodology which an object with a known periodic shape is introduced 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 periodical structure of the object is distorted by a contaminated lubricant. The object, e.g. a stainless steel woven wire cloth with a mesh size of 65×65µm2 and a circular wire diameter of 33µm was placed behind a microfluidic channel, containing engine lubricant and optical images of flowing lubricant with stationary object were acquired and analyzed. Several parameters of acquired optical images, such as, color of lubricant and object, object shape width at object and lubricant levels, object relative color, and object width non-uniformity coefficient, were proposed. Measured on-line parameters were used for optical analysis of fresh and contaminated lubricants. Estimation of contaminant presence and lubricant condition was performed by comparison of parameters for fresh and contaminated lubricants. Developed methodology was verified experimentally showing ability to distinguish lubricants with 1%, 4%, 7%, and 10% coolant, gasoline and water contamination individually and in a combination form of coolant (0%–5%) and gasoline (0%–5%).