101 Reassembly of Condition Monitoring Technology by Lubricating Oil Analysis

Lubricating oil monitoring technology is a commonly used method in aeroengine condition monitoring, which includes particle counting technology, as well as spectral and ferrography technology in offline monitoring. However, these technologies only analyze the characteristics of wear particles and rely on physical and chemical analysis techniques to monitor the oil quality. In order to further advance offline monitoring technology, this paper explores the potential role of differences in wear particle kinematic characteristics in recognizing changes in wear particle diameter and oil viscosity. Firstly, a kinematic force analysis of the wear particles in the microfluid was carried out. Accordingly, a microfluidic channel conducive to observing the movement characteristics of particles was designed. Then, the wear particle kinematic analysis system (WKAS) was designed and fabricated. Secondly, a real-time tracking velocity measurement algorithm was developed by using the Gaussian mixture model (GMM) and the blob-tracking algorithm. Lastly, the WKAS was applied to a pin–disc tester, and the experimental results show that there is a corresponding relationship between the velocity of the particles and their diameter and the oil viscosity. Therefore, WKAS provides a new research idea for intelligent aeroengine lubricating oil monitoring technology. Future work is needed to establish a quantitative relationship between wear particle velocity and particle diameter, density, and oil viscosity.

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Application of Oil Analysis Techniques to Diesel Engine Condition Monitoring

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.224.217 ◽

2012 ◽

Vol 224 ◽

pp. 217-220

Author(s):

Yong Guo Zhang ◽

Xu Feng Jiang ◽

Xiao Wen Wu ◽

Zong Ying

Keyword(s):

Diesel Engine ◽

Condition Monitoring ◽

Diesel Engines ◽

Lubricating Oil ◽

Oil Contamination ◽

Oil Analysis ◽

Mechanical Wear ◽

Analysis Techniques ◽

Wear Condition ◽

Engine Condition

In order to verify the validity of oil analysis for heavy diesel engine condition monitoring, the lubricating oil were sampled from the lubricating system of the domestic diesel engines, and then were tested by oil analysis (including contamination detection, periodic sampling test and ferrography technology). The results showed that oil analysis could monitor the lubricating oil contamination and mechanical wear condition to make diesel engines avoid early mechanical failure.

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Air Compressor Wear Condition Monitoring Based on Oil Analysis Technology

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.66-68.498 ◽

2011 ◽

Vol 66-68 ◽

pp. 498-503

Author(s):

Xu Feng Jiang ◽

Zhen Hui Qiu ◽

Ying Zong

Keyword(s):

Condition Monitoring ◽

Spectroscopic Analysis ◽

Atomic Emission ◽

Oil Products ◽

Lubricating Oil ◽

Oil Analysis ◽

Air Compressor ◽

Large Size ◽

Emission Spectroscopic Analysis ◽

Wear Condition

In order to verify the effectiveness of oil analysis technology on air compressor condition monitoring, oil samples are taken from lubricating system of D-100/8 type air compressor for monitoring by comprehensively using atomic emission spectroscopic analysis technology and ferrographic analysis technology. The result shows that the atomic emission spectroscopic analysis can comprehensively monitor content of additives, contaminants and wear metals in oil products. The effective analysis range of emission spectroscopy is particles with size smaller than 8~10μm and it fails to measure large-size wear particles produced from heavy wear of the equipment. However, the ferrographic analysis can further confirm wear condition of the equipment according to size, shape, material and superheated degree of particles, which just makes up the shortage. Thus, the combination of atomic emission spectroscopic analysis and ferrographic analysis is quite necessary for monitoring contamination and wear condition of lubricating oil products of air compressor and preventing sudden failure of machinery.

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Condition monitoring technology of transformer with multiple sensing methods united

2018 Condition Monitoring and Diagnosis (CMD) ◽

10.1109/cmd.2018.8535921 ◽

2018 ◽

Author(s):

Liang He ◽

Xinghai Zhang ◽

Jiangrong Cheng ◽

Xing Li ◽

Dengwei Ding ◽

...

Keyword(s):

Condition Monitoring ◽

Monitoring Technology ◽

Multiple Sensing

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Lubricating Oil Condition monitoring Through Particle Size Analysis

10.4271/881824 ◽

1988 ◽

Cited By ~ 6

Author(s):

Barry M. Verdegan ◽

Laura Thibodeau ◽

Stephen L. Fallon

Keyword(s):

Particle Size ◽

Condition Monitoring ◽

Particle Size Analysis ◽

Lubricating Oil ◽

Size Analysis ◽

Oil Condition Monitoring

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A DECISION TREE-BASED CLASSIFICATION FRAMEWORK FOR USED OIL ANALYSIS APPLYING RANDOM FOREST FEATURE SELECTION

Journal of Applied Science, Engineering and Technology for Development ◽

10.33803/jasetd.2017.3-1.7 ◽

2018 ◽

Keyword(s):

Decision Making ◽

Feature Selection ◽

Random Forest ◽

Decision Tree ◽

Condition Monitoring ◽

Critical Parameters ◽

Oil Analysis ◽

Used Oil ◽

Maintenance Decision

Lubricant condition monitoring (LCM), part of condition monitoring techniques under Condition Based Maintenance, monitors the condition and state of the lubricant which reveal the condition and state of the equipment. LCM has proved and evidenced to represent a key concept driving maintenance decision making involving sizeable number of parameter (variables) tests requiring classification and interpretation based on the lubricant’s condition. Reduction of the variables to a manageable and admissible level and utilization for prediction is key to ensuring optimization of equipment performance and lubricant condition. This study advances a methodology on feature selection and predictive modelling of in-service oil analysis data to assist in maintenance decision making of critical equipment. Proposed methodology includes data pre-processing involving cleaning, expert assessment and standardization due to the different measurement scales. Limits provided by the Original Equipment Manufacturers (OEM) are used by the analysts to manually classify and indicate samples with significant lubricant deterioration. In the last part of the methodology, Random Forest (RF) is used as a feature selection tool and a Decision Tree-based (DT) classification of the in-service oil samples. A case study of a thermal power plant is advanced, to which the framework is applied. The selection of admissible variables using Random Forest exposes critical used oil analysis (UOA) variables indicative of lubricant/machine degradation, while DT model, besides predicting the classification of samples, offers visual interpretability of parametric impact to the classification outcome. The model evaluation returned acceptable predictive, while the framework renders speedy classification with insights for maintenance decision making, thus ensuring timely interventions. Moreover, the framework highlights critical and relevant oil analysis parameters that are indicative of lubricant degradation; hence, by addressing such critical parameters, organizations can better enhance the reliability of their critical operable equipment.

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