Application of Oil Analysis Techniques to Diesel Engine Condition Monitoring

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.

Air Compressor Wear Condition Monitoring Based on Oil Analysis Technology

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.

Condition Monitoring of Engines by Lubricating Oil Analysis

1999 ◽  
Author(s):  
Luiz Augusto Rocha Baptista ◽  
Luiz Antonio Vaz Pinto ◽  
Carlos Rodrigues Pereira Belchior
Keyword(s):  
Condition Monitoring ◽  
Lubricating Oil ◽  
Oil Analysis

Condition monitoring of marine diesel engines through ferrographic oil analysis

Wear ◽  
1983 ◽  
Vol 90 (2) ◽  
pp. 225-238 ◽  
Author(s):  
Steven C. Hargis ◽  
Herbert F. Taylor ◽  
James S. Gozzo
Keyword(s):  
Condition Monitoring ◽  
Diesel Engines ◽  
Oil Analysis ◽  
Marine Diesel

A new swarm intelligence optimized multiclass multi-kernel relevant vector machine: An experimental analysis in failure diagnostics of diesel engines

2017 ◽  
Vol 17 (6) ◽  
pp. 1503-1519 ◽  
Author(s):  
Zhixiong Li ◽  
Yu Jiang ◽  
Zhihe Duan ◽  
Zhongxiao Peng
Keyword(s):  
Diesel Engine ◽  
Swarm Intelligence ◽  
Condition Monitoring ◽  
Manifold Learning ◽  
Diesel Engines ◽  
Learning Algorithms ◽  
Failure Detection ◽  
Kernel Functions ◽  
Weighted Sum ◽  
Vibration Data

This work attempts to introduce a new intelligent method for condition monitoring of diesel engines. Diesel engine is one of the most important power providers for various industrial applications, including automobiles, ships, agricultures, construction, and electrical machinery. Due to harsh working environment, diesel engines are vulnerable to failures. This article addresses a significant need to improve predictive maintenance activities in diesel engines. A new failure diagnostics approach was proposed based on the manifold learning and swarm intelligence optimized multiclass multi-kernel relevant vector machine. Three manifold learning algorithms were first respectively used to fuse the features that extracted from the original vibration data of the diesel engines into a new nonlinear space. The fused features contain the most distinct health information of the engine by discarding redundant features. Then, the swarm intelligence optimized multiclass multi-kernel relevant vector machine was proposed to identify the failures using the fused features. The contribution of this research is that the dragonfly algorithm is employed to optimize the weights of the multi-kernel functions in the multiclass relevant vector machine. It was also applied to establishing a weighted-sum model by combining the outputs of swarm intelligence optimized multiclass multi-kernel relevant vector machine models with different manifold learning algorithms. Robust failure detection of diesel engines is achieved owing to combined strengths of multiple kernel functions and weighted-sum strategy. The effectiveness of the proposed method is demonstrated by experimental vibration data collected from a commercial diesel engine. The failure detection capability of the proposed manifold learning and swarm intelligence optimized multiclass multi-kernel relevant vector machine method for diesel engines will potentially benefit the machine condition monitoring industry by improving budgeting/forecasting and/or enabling just-in-time maintenance.

scholarly journals Applications Characteristics of Different Biodiesel Blends in Modern Vehicles Engines: A Review

2021 ◽  
Vol 13 (17) ◽  
pp. 9677
Author(s):  
Dong Lin Loo ◽  
Yew Heng Teoh ◽  
Heoy Geok How ◽  
Jun Sheng Teh ◽  
Liviu Catalin Andrei ◽  
...  
Keyword(s):  
Diesel Engine ◽  
Diesel Fuel ◽  
Diesel Engines ◽  
Fossil Fuels ◽  
Lubricating Oil ◽  
Transportation Industry ◽  
High Concentration ◽  
Endurance Tests ◽  
Blended Fuel ◽  
Conventional Diesel Fuel

Two main aspects of the transportation industry are pollution to the environment and depletion of fossil fuels. In the transportation industry, the pollution to the environment can be reduced with the use of cleaner fuel, such as gas-to-liquid fuel, to reduce the exhaust emissions from engines. However, the depletion of fossil fuels is still significant. Biodiesel is a non-toxic, renewable, and biodegradable fuel that is considered an alternative resource to conventional diesel fuel. Even though biodiesel shows advantages as a renewable source, there are still minor drawbacks while operating in diesel engines. Modern vehicle engines are designed to be powered by conventional diesel fuel or gasoline fuel. In this review, the performance, emissions, combustion, and endurance characteristics of different types of diesel engines with various conditions are assessed with biodiesel and blended fuel as well as the effect of biodiesel on the diesel engines. The results show that biodiesel and blended fuel had fewer emissions of CO, HC, and PM but higher NOx emissions than the diesel-fuelled engine. In the endurance test, biodiesel and blended fuel showed less wear and carbon deposits. A high concentration of wear debris was found inside the lubricating oil while the engine operated with biodiesel and blends. The performance, emissions, and combustion characteristics of biodiesel and its blends showed that it can be used in a diesel engine. However, further research on long-term endurance tests is required to obtain a better understanding of endurance characteristics about engine wear of the diesel engine using biodiesel and its blends.

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