Condition monitoring of industrial machinery through lubricant oil analysis

Tribotest ◽  
1996 ◽  
Vol 2 (4) ◽  
pp. 317-328 ◽  
Author(s):  
V. M. de A. Leão ◽  
M. H. Jones ◽  
B. J. Roylance
Keyword(s):  
Condition Monitoring ◽  
Oil Analysis ◽  
Lubricant Oil

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

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.

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

Magnetoelastic Viscosity Sensor for Lubricant Oil Condition Monitoring

2011 ◽  
Vol 495 ◽  
pp. 71-74
Author(s):  
I. Bravo-Imaz ◽  
A. García-Arribas ◽  
E. Gorritxategi ◽  
M. Hernaiz ◽  
A. Arnaiz ◽  
...  
Keyword(s):  
Real Time ◽  
Condition Monitoring ◽  
Environmental Issues ◽  
Real Time Monitoring ◽  
Oil Viscosity ◽  
Lubricant Oil ◽  
On Line ◽  
Magnetoelastic Materials ◽  
Working Device

Actual trends in machinery maintenance point to the necessity of an on-line real-time monitoring of the condition of the lubricant oil. Excessive delay in replacing the lubricant oil can have catastrophic results, whereas doing it too early produces evident economic and environmental issues. Magnetoelastic materials offer a good sensing principle for assessing lubricant oil viscosity; which is one of the most important properties to assure its proper lubricant capacity. Among others, one of the most remarkable properties of this sensing principle is the capability of being used through a wide viscosity range. In this work, we describe the experiments performed to evaluate the usefulness of this technology for testing the viscosity of different test oils in order to develop a working device for on-line, real-time monitoring the quality of lubricant oils.

Experimental Investigation of the Influence of Engine Operating and Lubricant Oil Parameters on Sliding Bearing and Friction Behavior in a Heavy-Duty Diesel Engine

2021 ◽  
Author(s):  
Matheus Marques da Silva ◽  
Constantin Kiesling ◽  
Christof Gumhold ◽  
Sven Warter ◽  
Andreas Wimmer ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Lubricating Oil ◽  
Main Bearing ◽  
Friction Behavior ◽  
Engine Operation ◽  
Engine Efficiency ◽  
Lubricant Oil ◽  
Heavy Duty Diesel Engine ◽  
Heavy Duty Diesel ◽  
The Impact

Abstract In order to rise to global challenges such as climate change, environmental pollution and conservation of resources, internal combustion engine manufacturers must meet the requirements of substantially reduced emissions of CO2 and other greenhouse gases, zero pollutant emissions and increased durability. This publication addresses approaches that can help improve engine efficiency and durability through the engine crankshaft bearing and lubricant system. An understanding of the operating behavior of key engine components such as crankshaft main bearings in fired engine operation allows the development of appropriate tools for bearing condition monitoring and condition-based maintenance so as to avoid critical engine operation and engine failure as well as unnecessary engine downtime. Such tools are especially important when newly developed low viscosity oils are employed. Though these oils have the potential to reduce friction and to increase engine efficiency, their use comes with a higher risk of accelerated bearing wear and ultimately bearing failure. The specific target of this paper is therefore to obtain detailed knowledge of the influence of engine operating parameters and oil parameters on crankshaft main bearing temperature behavior and engine friction behavior in fired operation as a starting point for condition monitoring and condition-based maintenance approaches and as a basis for improving the bearing and lubricant system as a whole. To achieve this target, experimental investigations were carried out on an engine test bed employing an in-line six-cylinder heavy-duty diesel engine with a displacement of approximately 12.4 dm3. Defined and accurately reproducible engine operating conditions were ensured by comprehensive external conditioning systems for the coolant, lubricating oil, fuel, charge air and ambient air. Since the focus was on investigating the bearing and friction behavior by means of the base engine, several auxiliary systems were removed; these included the lubricating oil and coolant pumps, the front-end accessory drive and the generator. Each crankshaft main bearing was instrumented with a thermocouple on the back of its bottom bearing shell to measure the bearing temperature. Piezoelectric pressure transducers were applied to all six cylinders in order to facilitate the accurate determination of the friction mean effective pressure (FMEP) based on indicated and brake mean effective pressures. The variations in engine operating parameters (engine speed and torque) mainly serve as a reference for the variations in oil parameters. They confirm the existing knowledge that engine speed has a significant impact on FMEP and bearing temperature while the impact of engine torque is comparatively low. The variations in oil parameters reveal that lowering the viscosity grade from SAE 10W-40 to 5W-20 leads to a decrease in both bearing temperature and FMEP, which can be explained by the lower fluid friction in the bearing system and the increased mass flow and convective heat transport with the lower viscosity oil. An increase in the lubricating oil temperature at the engine inlet leads to a significant increase in bearing temperature and a decrease in FMEP; the former is explained by the increased heat influx from the lubricant oil, and the latter is caused mainly by the temperature dependency of the lubricant oil viscosity and its impact on fluid friction. The impact of engine oil inlet pressure on bearing temperature and FMEP is generally found to be low. The results will serve as the basis for future research that includes approaches to condition monitoring and evaluating improved engine operating strategies with regard to oil parameters.

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

Condition Monitoring of Lift Gear Boxes by Means of Sound Analysis

Volume 3 ◽  
2004 ◽  
Author(s):  
Mustafa Ozkirim ◽  
C. Erdem Imrak ◽  
Hakan Uzunoglu
Keyword(s):  
Condition Monitoring ◽  
Condition Based Maintenance ◽  
Oil Analysis ◽  
Electric Motors ◽  
Effective Technique ◽  
Sound Analysis ◽  
Fault Monitoring ◽  
Worm Gears ◽  
Potential Failure ◽  
Condition Monitoring System

The method of Condition Monitoring (CM) or Condition Based Maintenance (CBM) of machinery is straightforward since it aims to identify the changes in the condition of a machine during operation that will indicate some potential failure. This is achieved by utilizing various techniques such as Thermography, Oil Analysis and Ultrasonics. For all maintenance engineers’ diagnosis of gear defects by using sound analysis may be an effective technique in that it provides economic and continuous fault monitoring. In the study, in order to detect the defects in worm gears driven by electric motors, the samples of the sound vibrations emitted from the gears with and without defects are recorded by means of a sound recorder. The comparison of these sound records proved that the acoustic condition monitoring system is able to detect the faulty gears of the elevator drive units.

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