2016
DOI: 10.1016/j.chemolab.2016.09.004
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In-situ lubricating oil condition sensoring method based on two-channel and differential dielectric spectroscopy combined with supervised hierarchical clustering analysis

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Cited by 16 publications
(8 citation statements)
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“…CA results at the molecular level were observed to be in good agreement with the changes observed to the physicochemical properties of the samples on the macro level. Gong et al observed that hierarchical CA results based on two-channel and differential dielectric spectroscopy (TD-DES) data were also in good agreement with those based on FT-IR data and proposed using the TD-DES technique to monitor the oil and conduct hierarchical CA [54]. Furthermore, the present study offered a platform for further researching the molecular mechanism of oil sample degradation performance.…”
Section: Ca Of the Oil Samplessupporting
confidence: 75%
See 1 more Smart Citation
“…CA results at the molecular level were observed to be in good agreement with the changes observed to the physicochemical properties of the samples on the macro level. Gong et al observed that hierarchical CA results based on two-channel and differential dielectric spectroscopy (TD-DES) data were also in good agreement with those based on FT-IR data and proposed using the TD-DES technique to monitor the oil and conduct hierarchical CA [54]. Furthermore, the present study offered a platform for further researching the molecular mechanism of oil sample degradation performance.…”
Section: Ca Of the Oil Samplessupporting
confidence: 75%
“…CA is considered a principal task of data exploration, is a common technique for statistical analysis and chemometric approaches, and is widely used in molecular spectroscopy [51][52][53]. The results of CA were intended to elucidate the compound structure and compositional changes of SHALOs resulting from oxidative high-temperature degradation, allowing detection of the degradation stages of SHALOs and signaling of the need for an oil change when warranted by the oil conditions [54]. Furthermore, the SHALO clusters could be used to detect possible relationships between compound structure and compositional changes at the molecular level and, therefore, facilitate the discovery of physicochemical properties at the macro level.…”
Section: Ca Of the Oil Samplesmentioning
confidence: 99%
“…However, dielectric constant also varies with AC frequency and the variation of dielectric constant of a lubricant with frequency can provide information about the concentration and rotational mobility of its polar and polarisable molecules. The study of how dielectric constant varies with frequency is termed dielectric spectroscopy (DES) [150], which can be used to characterise base oil composition as well as oil degradation [151][152][153]. In practice, EIS and DES are closely related.…”
Section: Non-aqueous Lubricantsmentioning
confidence: 99%
“…Their main practical limitation lies in difficulty of interpretation, since most lubricants are complex blends of polar additives that even without degradation provide a complicated impedance spectrum, which can be interpreted fully only if the formulation composition is already known. The addition of ill-defined degradation products such as oxidation products adds to this complexity so that some developers are applying machine-learning and other expert systems to interpret and correlate electrical measurements with lubricant composition [152,153]. While this difficulty of interpretation limits the value of EIS and related measurements as research tools to study lubricants per se, it does not detract from their value as empirical ways to monitor changes in lubricant electrical properties resulting from ageing during use and that provide a measure of lubricant condition.…”
Section: Non-aqueous Lubricantsmentioning
confidence: 99%
“…Incorporating this pattern recognition, image analysis and information(attributes) retrieval generated, while picking effects and interactions which require to be addressed, offer maintenance decision making support. Cluster analysis has been used in several LCM related studies in the recent past such as [163][164] [165]. CA experiences some limitations that influence the subsequent results such as sampling errors and biasness towards setting the optimal number of clusters due to its heuristic nature as well.…”
Section: Logistics Regression (Lr)mentioning
confidence: 99%