Lubricant Properties and Test Methods

Toms, Larry A.; Toms, Allison M.

doi:10.1201/9781420089363.ch3

Cited by 17 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although some previous works, for example, those by Toms and Toms, 3 analyse the oil obtained from heavy machines, they do not go deeper into details. The authors did not deal with the potential of oil data.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of fuzzy inference system for analysis of oil field data to optimize combustion engine maintenance

Vališ

Žák

Vintr

2019

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

The condition of a technical system has been subject to intense scrutiny in recent years. Monitoring the technical condition of a system may be performed by applying different approaches. The main intention of the monitoring is to get the information about the instant system condition, and to estimate and predict reliability measures. In the article, the authors suggest possible ways to process diagnostic measures which have the potential to determine the system condition and to predict its future development. The diagnostic measures are in this case indirect and they are introduced in the form of oil data. The diagnostic data are obtained from the tribodiagnostic system which is composed of kinematic pairs and oil. The analysed oil samples come from the combustion engine of a heavy ground vehicle. The authors focus on the output values in the form of wear particles, iron and lead, and additive particles. The concentration of these particles in the oil is influenced by operating time and calendar time. However, the particles include inherent and natural levels of uncertainty and fuzziness. Therefore, the authors apply and present the models imitating the development of the particles which are based on a fuzzy inference system. Highly valuable and extensive data set records enabled the authors to perform two-dimensional data modelling based both on operation time and calendar time. The obtained results enable us to predict the remaining useful life of the system. Moreover, the results could also be beneficial when modifying hard time scheduled preventive maintenance intervals (e.g. when to change the oil). The major contribution of this paper is the fact that all analysed diagnostic data are not artificial but real; moreover, they were collected for more than 10 years and therefore contain hundreds of records.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Application of fuzzy inference system for analysis of oil field data to optimize combustion engine maintenance

Vališ

Žák

Vintr

2019

Proceedings of the Institution of Mechanical Engineers, Part D:

View full text Add to dashboard Cite

show abstract

“…First introduced in the late forties [ 24 ], the laboratory analysis of lubricating oil samples looking for evidence of ageing or degradation has been continuously evolving to offer a more accurate diagnosis about the remaining life time of the lubricants, or about any latent or imminent failure in the equipment or processes where they are being used. These laboratory analyses are nowadays standardized as a set of parameters describing the quality of an oil, which some of them are described below [ 25 , 26 ]: Acid Number (AN), is a measurement of oil acidity and it is considered a key indicator that may reflects oil quality deterioration due to chemical reactions, oxidation, incorrect oils, additive depletion and contamination. ASTM (American Society for Testing and Materials) D974 and ASTM D664 methods are the current industry standard methods for measuring the acid number [ 27 , 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Low Cost Photonic Sensor for in-Line Oil Quality Monitoring: Methodological Development Process towards Uncertainty Mitigation

Palomo‐López

Mabe

Mezquita³

et al. 2018

Sensors

View full text Add to dashboard Cite

Lubricant and hydraulic fluid ageing impacts the performance of the machines, gears, transmissions or automatisms where they are being used. This manuscript describes the work accomplished for bringing an innovative measurement concept for analysing the physical- chemical properties of these fluids, to a real industrial product ready to be integrated into different industrial equipment. The steps taken to deal with uncertainties and evolving requirements while progressing in the sensor development are described, covering the stages of theoretical formulation of the problem, optical and fluidic simulations, sensor prototype development and tests. The sensor working principle is based on a combination of transmittance and diffuse reflectance photonic inspection of the fluid sample that is collected in a micro-cavity through a standard hydraulic fitting. Photonics, electronics, micro-mechanics, fluidics, data processing and analysis has been merged with a deep knowledge in the lubricant degradation process to develop a sensor solution that is able to measure the Oil Degradation Index, Oil Oxidation, Acid Number, Ruler and Membrane Patch Colorimetry data from an in-service lubricating oil sample. The photonic micro sensor presented here offers a powerful tool that operates directly immersed in the fluid, at an economic cost and compacted size for in-line oil degradation monitoring.

show abstract

“…Peng and Chu (2004) have allowed the conversion of the time domain data into frequency spectra with ease, as the data was already stored in a digital format. This contrast to oil and wear debris analysis techniques, which often rely on extensive chemical analysis and data interpretation by experienced/trained analysts as reported by Toms (1998), Gadd and Mitchell (1984), Leblanc et al (1990) and Cameron and Stuckey (1994). These methods have traditionally been applied, separately in time and frequency domains.…”

Section: Introductionmentioning

confidence: 99%

Vibration signal analysis using histogram features and support vector machine for gear box fault diagnosis

Chandrasekaran

2017

IJSCC

View full text Add to dashboard Cite

This paper discusses about the extraction of histogram features from the vibration signal of the different conditions of the gear box under investigation, and the application of machine learning method, support vector machine in machine condition monitoring and diagnostics. This paper aims at using classification methods for fault diagnosis of the gear box under investigation. In this paper fault diagnostics of spur bevel gear box is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, and classification. This work investigates the use histogram features and support vector machine for classification. The results show that the developed method can reliably diagnose different conditions of the gear box.

show abstract

Lubricant Properties and Test Methods

Cited by 17 publications

References 0 publications

Application of fuzzy inference system for analysis of oil field data to optimize combustion engine maintenance

Application of fuzzy inference system for analysis of oil field data to optimize combustion engine maintenance

Low Cost Photonic Sensor for in-Line Oil Quality Monitoring: Methodological Development Process towards Uncertainty Mitigation

Vibration signal analysis using histogram features and support vector machine for gear box fault diagnosis

Contact Info

Product

Resources

About