Spectrometric Oil Analysis Research Articles

The Automated Spectrometric Oil Analysis Decision Taking Procedure as a Tool to Prevent Aircraft Engine Failures

The Automated Spectrometric Oil Analysis Decision Taking Procedure as a Tool to Prevent Aircraft Engine Failures

Health indicator metrics applicable to inductive wear debris sensors

The Inductive Wear Debris Sensor is a relatively new invention that is increasingly being used for the detection of incipient machinery damage or failures by sensing metallic debris in lubrication systems. This type of sensor is typically used in-line and has a superior particle size detection range compared to traditional techniques such as the ubiquitous spectrometric oil analysis. There is, however, very little in the literature regarding the application and interpretation of data arising from this type of sensor. Unlike other condition monitoring sensors, no data will be generated by an Inductive Wear Debris Sensor in an ideal system; however, in real applications it is necessary to discriminate between occasional particles unrelated to a failure and incipient failure particles. Inductive Wear Debris Sensor data could be misinterpreted if a simple cumulative count limit was applied to the data. A short-term rate of particle generation is sometimes used as an alternative; however, it too can be misleading with short succession particles producing high instantaneous rates possibly causing false alarms. The purpose of this work was to develop a robust metric (or group of metrics) that when applied to Inductive Wear Debris Sensor data would reliably identify a failure event and exclude non-failure related particles. The Health Indicator described herein consists of three subordinate Condition Indices that collectively are shown to reliably detect the onset of rolling contact fatigue. The metrics have been applied to bearing test rig data (seeded fault) and data obtained from a non-seeded fault test of a complex helicopter gearbox.

Condition Monitoring of a Gear-Box Using Wear Particle Analysis Technique

Wear particle analysis as a nondestructive evaluation technique is an effective method to determine the lubricating oil conditions within different lubricated machines, thus monitoring wear modes and imminent failures in these machines, such as gear-boxes and engines. Ferrographic analysis of wear particles in lubricating oil could give complete information about ferrous and non-ferrous solid debris present in the oil sample. Spectrometric oil analysis could give a direct measure of elemental metal content in the oil such as Iron, Aluminum, Lead and Cupper. These techniques provide cheap, fast and easy methods to use predictive maintenance methods which can replace other conventional methods. The objective of the present study is to apply wear particle analysis technique for condition monitoring of an industrial gear-box transmission over two year’s period of time. This gear-box belongs to one of the machines of the Oriental Weavers Company (OWC), one of the largest carpet manufacturers in the world. Spectrometric and ferrographic analysis were used where quantitative and qualitative changes in the concentration and size distribution of different particles were analyzed and compared to baseline values.

On the impact of fine filtration on spectrometric oil analysis and inductive wear debris sensors

Spectrometric oil analysis has been a popular technique used by oil analysis laboratories to determine the concentration of elements in an oil sample. The primary application of this technique has been the identification of fine wear in lubricated systems as an indicator of abnormal wear in order to prevent failure. It has also found some use for monitoring contaminants and fluid additives. While the literature has varying accounts of the effectiveness of this technique for machinery condition monitoring purposes, the introduction of fine filtration to many modern machines has profound implications for the effectiveness of spectrometric oil analysis. This paper will assess the effectiveness of spectrometric oil analysis in a fine filtered system and compare it with the results from an inductive wear debris sensor. The inductive wear debris sensor is a relatively new sensor for detecting abnormal wear in recirculating lubrication systems. A combination of experimental data and aircraft data is presented.

Study on the Reliability Assessment Method of Heavy Vehicle Gearbox Based on Spectrometric Analysis

Spectrometric oil analysis is of great importance for wear condition monitoring of gearbox. In this context, the contents of main elements compositions in the bench test of heavy vehicle gearbox are obtained by atomic emission spectrometric oil analysis first. Then correlation analysis of the test data and wearing mechanism analysis are carried out to get the metal element which could be used to describe the wearing and failure of the gearbox. The spectrometric data after filling/changing oil are corrected, and the laws of the contents of main elements compositions during tests are expressed as linear functions. After that, the reliability assessment is executed with considering the degradation law and discreteness of test data, in which the mean and standard deviation of normal distribution of spectrometric oil data at each time point are adopted. Finally, the influences of the threshold are discussed. It has been proved that the contents of metal element Cu, which is got by spectrometric oil analysis of different samples, could be used to assess the reliability of heavy vehicle gearbox. The reason is that the metal element Cu is closely related to the general wear state of gearbox, and is easy to be measured. When the threshold of Cu content is treated as a constant, bigger threshold means higher reliability at the same time, and the mean value of threshold has significant impact on the reliability assessment results as R > 0.9. When the threshold is treated as a random variable, bigger dispersion of threshold means smaller slope of reliability against time, and also means lower reliability of gearbox as R > 0.9 at the same time. In this study, the spectrometric oil analysis and probability statistics are used together for the reliability assessment of gear box, which extends the application range of spectrometric analysis.

An Investigation for Prior Failure of Engine Component through Spectroscopy Oil Analysis Program

Monitoring the condition of the engine through oil analysis will help operators to get the most value from their equipment and their lubricant. This is just one part of an overall Preventive Maintenance (PM) Program. Engine lubricant condition reflects the state of health of engine through its properties. Recognition and analysis of the correlation between engine lubricant system based on the lubricant properties and engine performance is crucial to provide insight into engine health. Aim of this paper is an investigation for prior failure of engine component through oil analysis. Engine is the heart of automobile vehicles. Engine performances are directly dependent upon the health of its components like piston, cylinder, cylinder head, crankshaft, cam shaft, connecting rod etc. Metals in lubricating oil can come from various sources, such as wear, contamination and additives. Wear metals result from friction or corrosion of the engine components; for example pistons, bushing, piston ring and bearings, during operation. Contamination can come from dirt, leaks or residual metal pieces. Additives used as detergents, anti-oxidants and anti-wear agents, are added in order to reduce engine wear. Wear of a specific component is heralded by an increase in the concentration of a particular metal, or the sudden appearance of a metal. Since different engine components are composed of different alloys, the increase of a particular metal can be used to identify impending failure of a specific component. An analysis of trace metals in engine oil has permitted the identification of wearing components before severe failure, without dismantling of the engine. Spectrometric Oil Analysis Program (SOAP). The determination of Al, Cd, Cr, Cu, Fe, Mg, Ni, Pb, Sn and Zn impurities using flame atomic absorption spectroscopy method. The used engine oil samples were digested with HCL

Prediction of Aero Engine Fault by Relative Vector Machine and Genetic Algorithm Model

Diagnosis of engine fault is critical in reducing maintenance costs. A new method which incorporates hybrid relative vector machines and genetic algorithm (RVM-GA) was proposed to predict aero engine fault based on data of the spectrometric oil analysis. Experimental results show that it has a high accuracy and effective properties.

Wear Elemental Spectrometric Quantitative Analysis of Used Perodua Automatic Transmission Fluid-3 Series (ATF-3)

This paper investigates the severity level of wear elements occurred in Perodua MyVi 1300cc automatic transmission (AT) mechanism via multi-elemental spectrometric oil analysis. The work of analysis was performed merely on automatic transmission fluid (ATF) Perodua original equipment manufacturer (OEM) (ATF-3) series. The ATF was analyzed through continuous dynamometer basis at operating speed of 3000rpm and actual travelled distance mission profile categories. The operating mileage tested during continuous dynamometer basis ranges from 0km up to 10,000km maximum operating distance. The traces of wear elements were quantitatively analyzed at each operating mileage of 1,500km, 3,000km, 4,500km, 7,000km and 10,000km, respectively. The mileage selected and observed for second mission profile category was divided into three main categories. Category sample number A1-B3 referred to the actual travelling distance between 800km up to less than 20,000km. Category C1-D2 and E1-F5 each represents the operating travelling distance of 20,000km up to less than 40,000km and 40,000km up to less than 60,000km, respectively. Spectrometric analysis is primarily commenced based on serious limitations of the wear particle size that usually expressed as wear concentration in parts-per-million (ppm) unit. The typical concentration range for every element is between 1-100ppm and the severity level of concentration for every element varies from one another. The element of Aluminum (Al), Chromium (Cr), Copper (Cu), (Ferum) Fe, Lead (Pb), Nickel (Ni), Tin (Sn), Titanium (Ti), Vanadium (V), Manganese (Mn), Argentum (Ag) and Cadmium (Cd) are principally categorized as the wear elements. It was found that the occurrence of wear by product of these elements presence in the AT mechanism can be classified as benign wear and the presence of wear particles on both mission profiles are considered as normal wear particles mode even the ATF has been used at continuous operating speed of 3000rpm and extendedly used beyond its recommended drain period of every 20,000km operating travelling distance.

Monitoring Natural Pitting Progress on Helical Gear Mesh Using Acoustic Emission and Vibration

Over the last few decades, there has been an awareness of the role of predictive maintenance on large industrial gearboxes. To apply effective condition base maintenance on gears, it is important to diagnose the gear fault at its earliest stage of development. The application of the acoustic emission (AE) technology in relation to gear box diagnosis is well documented [1–4]. Asperity contact has been found to be the major source of AE activity within the gear mesh [5]. Researchers have investigated the influence of other operational factors such as oil film thickness [6], load and speed [7, 8] on the level of AE activity. Tan et al. [9] presented the first and only conclusive research in which the applicability of AE in monitoring the natural progressive pitting was investigated. Tan employed a back-to-back gearbox to study the accelerated surface pitting on spur gears while a comparative study between effectiveness of spectrometric oil analysis, AE and vibration techniques was undertaken. Their research showed that the AE r.m.s from the pinion gear was more sensitive in detecting the pitting progress on the gear surface. Raja et al. [7] performed a comparative study on the effect load and speed on the generation of AE for both helical and spur gears, while Eftekharnejad et al. [10] investigated the capability of AE technology in locating and identifying seeded surface defects on helical gears. To date, apart from the work by Eftekharnejd et al. and Raja et al. [7, 10], there has been no other investigation into application of AE in diagnosis of the helical gears. The use of vibration analysis for gear fault diagnosis and monitoring has been widely investigated and its application in industry is well established [11]. This is mainly reflected in the aviation industry for diagnosis of the helicopter engine, drive trains and rotor systems [12]. The spectral kurtosis (SK) is gaining ground in application to vibration diagnosis. To determine the SK, the signal is first decomposed into the time-frequency domain after which the Kurtosis values are determined for each frequency band [13]. The concept of SK analysis was first developed by Dwyer [14] as a tool which was able to trace non-Gaussian features in different frequency bands using the fourth order moment of the real part of the short-Time Fourier transform (STFT). Dwyer only investigated the application of SK on stationary processes but did not account for non-stationary vibration signatures typical of rotating machines. To date, the most comprehensive calculations of the SK has been developed by Antoni [15] as the fourth order cumulant of the spectral moment (K):

Engine Wear Monitoring with OLVF

In this article, an on-line visual ferrograph (OLVF) was used to monitor the wear condition of a vehicle engine during a bench test. A quantitative index of particle coverage area (IPCA) and wear debris ferro-image were obtained via OLVF to characterize wear degrees. On-line ferrograph analysis indicated some problems with the engine from about the 17th hour. A linear threshold value method to identify running condition of engines with the OLVF data was proposed. The OLVF data were in a fault zone after running for 25 h, when a base bolt broke and the engine stopped working during the test. The engine was disassembled and inspected after the test. Several wear scars were found on the cylinder walls as well as on the bearing surfaces. The oil was added at the 26th and 55th hours, which was also reflected by the variation of IPCA. Moreover, several off-line analyses were carried out in parallel. Off-line ferrograph data did not give any information about the abnormality and only showed stable wear debris content. Spectrometric oil analysis showed that there was no significant change in contents of metal elements.

Advanced condition monitoring of tapered roller bearings, Part 1

A new condition monitoring technique is under development which is based on electrostatic charge. This was originally employed for the detection of debris in the gas path of jet engines and gas turbines, and is now being developed for lubricated systems. Previous investigations have demonstrated the viability of this technique in dry and lubricated tribo-contacts using laboratory-based equipment. This paper reports on the evolution of wear during a tapered roller bearing test, using a suite of condition monitoring techniques, that incorporated electrostatic wear-site sensors to identify charge during surface wear and oil-line sensors to detect debris in oil scavenge lines. The multi-sensor arrangement also included a vibration accelerometer, thermocouples, inductive and ferromagnetic particle counters. Additionally, oil samples were taken during various stages of the test and were analysed for sub-100 μm debris content. Off-line debris analysis included optical particle counting, ferrography, spectrometric oil analysis and electron microscopy. Further tribological assessments included mass loss calculations and photographic evidence of damage. During the initial 7 h, running-in wear was identified by dynamic wear site charge, acceleration and temperature. Also at this time, increases in oil-line charge correlated with debris detection by the inductive sensor, optical particle counter and ferrographic analysis. Following running-in, benign wear was indicated by a reduction in wear site charge, acceleration and temperature, as well as reductions in oil-line charge, inductive and optical particle counts. Around 42 h, increases in inductive and optical particle count were considered to be an early indicator (or precursor) of the impending wear out phase. At 53.5 h, wear out was identified by increases in wear site charge, acceleration and temperature. Evidence of wear debris was shown by increases in oil-line charge, inductive and ferromagnetic particle count. The debris was further corroborated by optical particle count, ferrography and spectrometry. The test was then stopped after 63 h and post-test analysis confirmed outer race and roller spallation.

Analysis of the Behaviour of Rolling Bearings in Contaminated Oil Using Some Condition Monitoring Techniques

Friction and wear behaviours of rolling bearing in contaminated oil containing white-fused alumina particles were studied. The friction and wear processes were monitored using wear debris analysis, such as ferrography and spectrometric oil analysis program, and vibration analysis. Test bearing was a deep groove ball bearing (6002P5); Wear debris and worn surfaces of the bearing components were observed with a scanning electron microscope (SEM). It was found that the friction coefficient in the contaminated oil became lower by about 0.001 than that in the new oil for the large contaminants. The results of wear debris analysis showed that the large contaminants caused the high wear rate in the bearing. Three types of wear debris were commonly observed: thread-like debris, cutting chip debris, and plate-like debris. On the basis of the SEM observation results of the worn surfaces, wear mechanisms of these wear debris were discussed. The results of vibration analysis showed that the probability density function of vibration waveform was normal distribution in both the new and contaminated oils. In the contaminated oil, it changed depending on the contaminant size and the runtime, i.e. the progress of wear in the bearing. The result of wear debris analysis was related to that of vibration analysis and discussed.

A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears

Prognosis of gear life using the acoustic emission (AE) technique is relatively new in condition monitoring of rotating machinery. This paper describes an experimental investigation on spur gears in which natural pitting was allowed to occur. Throughout the test period, AE, vibration and spectrometric oil samples were monitored continuously in order to correlate and compare these techniques to natural life degradation of the gears. It was observed that based on the analysis of root mean square (rms) levels only the AE technique was more sensitive in detecting and monitoring pitting than either the vibration or spectrometric oil analysis (SOA) techniques. It is concluded that as AE exhibited a direct relationship with pitting progression, it offers the opportunity for prognosis.

Diagnostics and prognostics with acoustic emission, vibration and spectrometric oil analysis for spur gears ? a comparative study

Whilst vibration and spectrometric oil analysis for gear fault diagnosis are well established, the application of AE to this field is still in its infancy. This paper describes an experimental investigation on spur gears in which natural pitting was allowed to occur. Throughout the test period, AE, vibration and spectrometric oil samples were monitored continuously in order to correlate and compare these techniques to natural life degradation of the gears. It was observed that the AE technique was the most sensitive in detecting and monitoring pitting.

A model to predict the residual life of aircraft engines based upon oil analysis data

AbstractThis paper reports on a study using the available oil monitoring information, such as the data obtained using the Spectrometric Oil Analysis Programme (SOAP), to predict the residual life of a set of aircraft engines. The relationship between oil monitoring information and the residual life is established using the concept of the proportional residual, which states that the predicted residual life may be proportional to the wear increment measured by the oil analysis programmes. Assuming such a relationship between wear and the residual life exists, we formulated a recursive prediction model for the item's residual life given measured oil monitoring information to date. A set of censored life data of 30 aircraft engines (right censored due to preventive overhaul) along with the history of their monitored metal concentration information are available to us. The metal concentration information includes many variables, such as Fe, Cu, Al, etc.; not all of them are useful, and some of them may be correlated. The principal component analysis (PCA) has been adopted to reduce the dimension of the original data set and to produce a new set of uncorrelated variables, which we shall use in the prediction model. The procedure associated with estimating model parameters is discussed. The model is fitted to the actual SOAP data from the aircraft engines, and the goodness‐of‐fit test has been carried out. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005.

Monitoring of the Wear Condition and Research on the Wear Process for Running Equipment©

The wear process of tribological systems in general meaning is strictly analyzed and discussed in this paper. The dynamic model for the wear process and the characteristic parameters of wear condition have been established. The performance of wear has been well monitored with the parameters. In this paper, the wear process in the failure stage is analyzed and the concept of a “wear marginal failure state” is proposed. It is proposed that the wear process on the failure condition is composed of a few “marginal failure states.” The wear condition of the tribological system of the diesel engine is monitored with spectrometric oil analysis, and the results of monitoring agree with the views proposed. Printed as a Society of Tribologists and Lubrication Engineers paper at the ASME/STLE Tribology Conference in Lahaina, Hawaii, October 16–20, 1994

A Modular Intelligent Data Administration Approach for Helicopter Health and Usage Monitoring Systems

Much effort and expenditure has been focused on the introduction of acquisition and processing hardware for helicopter health and usage monitoring systems (HUMS) operations. In contrast, the potential difficulties in the management of HUMS data have received much less attention. HUMS hardware will typically generate in excess of one megabyte of data per flight. This data will be downloaded to ground station computers for further analysis. A modular intelligent data administration system (MIDAS) is highly desirable in order to efficiently extract useful information from the huge amount of HUMS data, and provide general robust diagnostic strategies. A state-of-the-art implementation of relevant technologies and analysis methods is essential to realize such a system. This paper presents the requirements and structure of MIDAS and details an approach that integrates neural network technology, helicopter mathematical dynamic models and unsupervised pattern recognition techniques. Examples using vibration and spectrometric oil analysis programmes (SOAP) as a means to evaluate the health and usage of helicopter components are presented.

NDE Handbook, Non-destructive examination methods for condition monitoring : Butterworths (1989). ISBN 0-408-04392-X

Acoustic cross correlation acoustic emission coating thickness dye penetrant examination eddy current testing emission spectroscopy endoscopy ER probe ferrography hardness testing hydrogen cell isotope techniques laser distance measurements leak testing LPR probe magnetic plugs magnetic particle examination mechanical calibration NDE method combination NDE methods under development noise measurement optical pattern recognition P scan pinhole detection pressure testing radiography replical technique spectrometric oil analysis program (SOAP) strain gauge technology stroboscopy test coupons thermography ultrasonic leak detection ultrasonics vibration monitoring visual inspection X-ray crawlers X-ray diffraction.

Ferrographic analysis of failure process in a full-scale journal bearing

The failure process of a full-scale journal bearing, caused by insufficient oil supply to the interface, was monitored and analyzed with ferrography and the spectrometric oil analysis procedure (SOAP). The temperature of the bearing metal and the journal deviation were also measured to monitor the behavior of the bearing. Wear debris was observed and analyzed in detail with a scanning electron microscope and a wavelength-dispersive X-ray analysis facility. It was found that ferrography and SOAP could promptly detect the failure symptoms. Ferrography could also detect the progressive failure, that is the progress of damage. With the progress of failure, the types, sizes and composition of wear debris changed significantly; a small amount of rubbing debris was generated from the journal at the normal state, much cutting and plate-like debris from the journal, some plate-like debris from the bearing at the early stage of failure, much polymer-like debris containing both the journal and bearing materials at the middle stage and much cutting, plate-like and oxide-like debris from both the journal and lining metal (steel) at the catastrophic stage. The wear mechanisms are discussed.

Correlation of portable wear metal analyzer (PWMA-2) and Baird A/E35U-3 oil analysis data

Correlation of the Perkin-Elmer Corporation portable wear metal analyzer, version two (PWMA-2) and the Baird Corporation A/E35U-3 spectrometric oil analysis data is accomplished by curve fitting of data from aircraft, bearing test rigs, and organometallic oil samples analyzed on both instruments. Each of nine wear metal elements (iron, silver, aluminum, chromium, copper, magnesium, nickel, silicon and titanium) was separately fitted to linear, logarithmic, power law and low order polynomial curves. Power law curve fits were best for most of the elements, especially in the important low concentration (0–5 ppm) range, where U.S. Air Force gas turbine aircraft engines typically operate. The power law curve was applied to all the elements to construct correlation algorithms, with a general uncertainty in correlation values of about 0.5–1 ppm or 10%, whichever was the greater. The coefficients of determination for the power law curve fits ranged from 0.935 for aluminum to 0.993 for iron. The fit for silicon was poor for any type of curve (the best coefficient of determination was 0.564 for the power law curve fit) and therefore correlation of silicon values is not recommended.