Wear condition monitoring for diesel engine via online visual ferrograph

2015 ◽  
pp. 271-275
Keyword(s):  
Diesel Engine ◽  
Condition Monitoring ◽  
Wear Condition

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.

Fuzzy model-based condition monitoring and fault diagnosis of a diesel engine cooling system

2002 ◽  
Vol 216 (3) ◽  
pp. 215-224 ◽  
Author(s):  
J A Twiddle ◽  
N B Jones
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Condition Monitoring ◽  
Cooling System ◽  
Fuzzy Model ◽  
Operating Conditions ◽  
Experimental Simulation ◽  
Model Based ◽  
Potential System ◽  
On Line

This paper describes a fuzzy model-based diagnostic system and its application to the cooling system of a diesel engine. The aim is to develop generic cost-effective knowledge-based techniques for condition monitoring and fault diagnosis of engine systems. A number of fuzzy systems have been developed to model the cooling system components. Residuals are generated on line by comparison of measured data with model outputs. The residuals are then analysed on line and classified into a number of fuzzy classes symptomatic of potential system conditions. A fuzzy rule-based system is designed to infer a number of typical fault conditions from the estimated state of the valve and patterns in the residual classes. The ability to diagnose certain faults in the system depends on the state of the thermostatic valve. The diagnostic systems have been tested with data obtained by experimental simulation of a number of target fault conditions on a diesel generator set test bed. In five test cases for separate cooling system operating conditions, the diagnostic system's successful diagnosis rate ranged between 73 and 97.7 per cent of the test data.

Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

2014 ◽  
Vol 541-542 ◽  
pp. 1419-1423 ◽  
Author(s):  
Min Zhang ◽  
Hong Qi Liu ◽  
Bin Li
Keyword(s):  
Wavelet Transform ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Shannon Entropy ◽  
Spindle Motor ◽  
Face Milling ◽  
Motor Current ◽  
Milling Tool ◽  
Spindle Motor Current ◽  
Wear Condition

Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

A graphic pattern feature-mapping-based data-driven condition monitoring method for diesel engine malfunction identification and classification

2018 ◽  
Vol 233 (1) ◽  
pp. 202-212 ◽  
Author(s):  
Yiqing Li ◽  
Wen Zhou ◽  
Yanyang Zi
Keyword(s):  
Diesel Engine ◽  
Condition Monitoring ◽  
Data Driven ◽  
Injection System ◽  
Cylinder Pressure ◽  
Feature Mapping ◽  
Monitoring Method ◽  
Large Power ◽  
Pressure Signal ◽  
Graphic Pattern

Effective condition monitoring of diesel engine can ensure the reliability of large-power machines and prevent catastrophic consequences. Cylinder pressure is capable of reflecting the whole combustion process of diesel engine, and hence can help to identify the malfunctions of the diesel engine during operation. In this paper, a graphic pattern feature-mapping method is proposed for graphic pattern feature recognition in data-driven condition monitoring. The graphic feature extraction and recognition are linked by labeled feature-mapping. It is used for identifying the running condition of the diesel engine via analyzing the cylinder pressure signal of the diesel engine. The different types of the malfunctions which are caused by different parts of the diesel engine such as induction system, valve actuating mechanism, fuel system, fuel injection system, etc. can be identified just by cylinder pressure signal. The bench experiment of a large-power diesel engine is performed to validate this graphic pattern recognition method. The results show that it has good accuracy on multi-malfunction identification and classification when the engine operates at one speed and one load.

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