High-frequency microtitrimetric determination of acidic and basic constituents in lubricating oils. Part 1. Determination of total acid number

Fernández, T.; Rocha, Jesus; Rufino, N.; Luís, André; Montelongo, F. García

doi:10.1039/an9780301249

Cited by 5 publications

(1 citation statement)

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“…Sensor 3 was made of gold IDTs without any coating layer on electrodes (see figure 1(a)). Soot in lubricant oil, amorphous carbon particles produced by incomplete combustion, tends to adhere to the gold electrodes easily [18]. Because of this effect, the capacitance change of this IDT sensor is primarily caused by soot, while other property factors in oil also affect the output.…”

Section: Sensors Design and Fabricationmentioning

confidence: 99%

A microsensor array for quantification of lubricant contaminants using a back propagation artificial neural network

Zhu

Liu

et al. 2016

J. Micromech. Microeng.

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We present a method based on an electrochemical sensor array and a back propagation artificial neural network for detection and quantification of four properties of lubrication oil, namely water (0, 500 ppm, 1000 ppm), total acid number (TAN) (13.1, 13.7, 14.4, 15.6 mg KOH g−1), soot (0, 1%, 2%, 3%) and sulfur content (1.3%, 1.37%, 1.44%, 1.51%). The sensor array, consisting of four micromachined electrochemical sensors, detects the four properties with overlapping sensitivities. A total set of 36 oil samples containing mixtures of water, soot, and sulfuric acid with different concentrations were prepared for testing. The sensor array’s responses were then divided to three sets: training sets (80% data), validation sets (10%) and testing sets (10%). Several back propagation artificial neural network architectures were trained with the training and validation sets; one architecture with four input neurons, 50 and 5 neurons in the first and second hidden layer, and four neurons in the output layer was selected. The selected neural network was then tested using the four sets of testing data (10%). Test results demonstrated that the developed artificial neural network is able to quantitatively determine the four lubrication properties (water, TAN, soot, and sulfur content) with a maximum prediction error of 18.8%, 6.0%, 6.7%, and 5.4%, respectively, indicting a good match between the target and predicted values. With the developed network, the sensor array could be potentially used for online lubricant oil condition monitoring.

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Section: Sensors Design and Fabricationmentioning

confidence: 99%