THE INVESTIGATION INTO THE CONDITION MONITORING OF TRIBOLOGICAL BEHAVIOUR BETWEEN PISTON RING AND CYLINDER LINER USING ACOUSTIC EMISSIONS

To improve engine operational performance and reliability, this study focuses on the investigation into the behaviour of tribological conjunction between the ring - liner based on a comprehensive analysis of non-intrusive acoustic emission (AE) measurement. Particularly, the study will provide more knowledge of using AE for online monitoring and diagnosing the performances of the conjunction. To fulfil this study, it integrates analytical predictions of the theoretical modelling for the AE generation mechanism with extensive experimental evaluations. Moreover, effective signal processing techniques are implemented with a combination of the model based AE predictions to extract the weak and nonstationary AE contents that correlate more with the tribological behaviour. Based on conventional tribological models, tribological AE is modelled to be due to two main dynamic effects: asperity-asperity collision (AAC) and fluid-asperity interaction (FAI), which allows measured AE signals from the tribological conjunction to be explained under different scenarios, especially under abnormal behaviours. FAI induced AE is more correlated with lubricants and velocity. It presents mainly in the middle of engine strokes but is much weaker and severely interfered with AEs from not only valve landings, combustion and fuel injection shocks but also the effect of considerable AACs due to direct contacts and solid particles in oils. To extract weak AEs for accurately diagnosing the tribological behaviours, wavelet transform analysis is applied to AE signals with three novel schemes: 1) hard threshold based wavelet coefficients selection in which the threshold value and wavelet analysis parameters are determined using a modified velocity of piston motion which has high dependence on the AE characteristics predicted by the two models; 2) Adaptive threshold wavelet coefficients selection in which the threshold is gradually updated to minimise the distance between the AE envelopes and the predicted dependence; and 3) wavelet packet transform (WPT) analysis is carried out by an optimised Daubechies wavelet through a novel approach based on minimising the time and frequency overlaps in WPT spectrum. Based on these optimal analyses, the local envelope amplitude (LEA) and the average residual wavelet coefficient (ARWC) are developed from AE signals as novel indicators to reflect the tribological behaviours.\ud Both the hard threshold based LEA and wavelet packet transform LEA values allow two different new lubricants to be diagnosed in accordance with model predictions whereas they produce less consistent results in differentiating the used oil under several operating conditions. Nevertheless, ARWC can separate the used oil successfully in that it can highlight the AAC effects of particle collisions in used oils. Similarly, LEA shows little impacts of two alternative fuels on the tribological behaviours. However, ARWC shows significantly higher amplitudes in several operating conditions when more particles can be produced due to unstable and incomplete combustions of both the biodiesel and FT diesel, compared with pure diesel, indicating they can cause light wear

Identification of lubrication Regimes in Mechanical Seals using Acoustic Emission for Condition Monitoring

The quality of lubrication condition between seal faces directly affects the reliability, operating life and sealing performance of mechanical seals. Thus, the identification of lubrication regimes in face seals i.e. boundary lubrication (BL), mixed lubrication (ML) and hydrodynamic lubrication (HL) is of high importance for developing effective online condition monitoring approaches. This paper investigates the tribological behaviour and frictional characteristics of mechanical seals based on nonintrusive acoustic emission (AE) measurements. Mathematical models for AE generation mechanisms are derived based on the tribological behaviour and operating parameters of mechanical seals. They produce agreeable results with experimental data in explaining the types of AE signals observed in monitoring the face lubrication conditions. Frequency domain analysis of data shows that the viscous friction process generates more low frequency AE signals, whereas the asperity interactions show more high frequency AE. Moreover, the feasibility of using statistical parameters of the time domain data is shown to identify the lubrication regimes in face seals

Characterising the friction and wear between the piston ring and cylinder liner based on acoustic emission analysis

In this paper, an experimental investigation was carried out to evaluate the friction and wear between the cylinder liner and piston ring using acoustic emission (AE) technology. Based on a typical compression ignition (CI) diesel engine, four types of alternative fuels (Fischer-Tropsch fuel, methanol-diesel, emulsified diesel and standard diesel) were tested under dif-ferent operating conditions. AE signals collected from the cylinder block of the testing en-gine. In the meantime, the AE signals in one engine cycle are further segregated into small segments to eliminate the effects of valve events on friction events of cylinder liner. In this way, the resulted AE signals are consistent with the prediction of hydrodynamic lubrication processes. Test results show that there are clear evidences of high AE deviations between dif-ferent fuels. In particular, the methanol-diesel blended fuel produces higher AE energy, which indicates there are more wear between the piston ring and cylinder liner than using standard diesel. On the other hand, the other two alternative fuels have been found little dif-ferences in AE signal from the normal diesel. This paper has shown that AE analysis is an ef-fective technique for on-line assessment of engine friction and wear, which provides a novel approach to support the development of new engine fuels and new lubricants

Journal bearing lubrication monitoring based on spectrum cluster analysis of vibration signals

Journal bearings are critical components for many important machines. Lubrication analysis techniques are often not timely and cost effective for monitoring journal bearings. This research investigates into vibration responses of such bearings using a clustering technique for identifying different lubrication regimes, and consequently for assessing bearing lubrication conditions. It firstly understands that the vibration sources are mainly due to the nonlinear effects including micro asperity collisions and fluid shearing interactions. These excitations together with complicated vibration paths are difficult to be characterized in a linear way for the purpose of condition monitoring. Therefore, a clustering analysis technique is adopted to classify the vibration spectrum in high frequency ranges around 10kHz into different representative responses that corresponds to different bearing modulus values and lubrication characteristics. In particular, the analysis allows sensitive signal components and sensor positions to be determined for monitoring the journal bearing effectively. Test results from self-aligning spherical journal bearings show that it allows different lubricant oils and different lubrication regimes to be identified appropriately, providing feasible ways to online monitoring bearing conditions

Monitoring Oil Levels Of Journal Bearings Based On The Analysis Of Vibration Signals

This paper presents a study of monitoring the oil starvation of a journal bearing based on vibration analysis. A diagnostic model is established by includ-ing asperity ploughs and collisions. These excitations are more significant as the oil level is reduced due to less oil film effect. However, it has been found by modulation signal bispectrum analysis that the instable oil whirls can affect the measured responses in the middle frequency range (3.5kHz to 5.5kHz), leading to a good detection of the instability but an inconsistent diagnosis. However, the structural resonances in the high frequency range (5.5kHz to 11kHz) can better reflect the excitations and result in a more agreeable separation of different levels under wide operating conditions

A validated finite element model for predicting dynamic responses of cylinder liners in an IC engine*

Vibration of cylinder liners affects not only engine combustion performances but also tribological behaviour and noise radiations. However, it is difficult to characterize it experimentally due to multiple sources, strong background noise, and nonlinear transfer paths. Therefore, a finite element model is established in this study to predict the dynamic responses of cylinder liners under respective sources. The model takes into account both the characteristics of structural modes and nonlinearities of assembly constraints when selecting adequate elements for efficient computation of the responses under both the highly nonlinear combustion pressure excitations and subsequent piston slap impacts. The predictions are then evaluated against experimental results under different engine operating conditions. In addition, continuous wavelet analysis is employed to process the complicated responses for key response events and their frequency ranges. The results show agreeable correspondences between the numerical predictions and measured vibration signals, paving the way for investigating its effect on combustion and lubrication processes

A Study of Alternative Fuels Potential Effects on the Combustion Engines using acoustic emission

The increasing interest in alternative fuels for CI engines continues has led to many different alternative fuels which are claimed to be more economical and cleaner. However, the impacts of longterm use of the alternative fuels on the reliability and service life of CI engine have not yet been fully understood.AE technology is now becoming a widely accepted practice in the field of engine. AE signals can be used to obtain information on machine performance, offering the potential to monitor operating conditions such as fuel efficiency, combustion conditions, and lubrication, and also to detect faults. In this paper, the potential impacts of alternative fuels (Fischer-Tropsch fuel, methanol-diesel blended fuel, emulsified diesel and standard diesel) on acoustic emission signals of cylinder have been investigated. The impacts of the different fuels on the wear condition of cylinder were compared and analysed using the acoustic emission monitoring at the cylinder. The results provide real-time evaluation method and effective data support for the development and application of alternative fuels

The Investigation into the Tribological Impact of Alternative Fuels on Engines Based on Acoustic Emission

The wide use of different alternative fuels (AL) has led to challenges to the internal combustion (IC) engine tribology. To avoid any unpredicted damages to lubrication joints by using AL fuels, this study aims to accurately evaluate the influences of alternative fuels on the tribological behavior of IC engines. Recent achievements of the acoustic emission (AE) mechanism in sliding friction provide an opportunity to explain the tribological AE responses on engines. The asperity–asperity–collision (AAC) and fluid–asperity–shearing (FAS) mechanisms were applied to explain the AE responses from the piston ring and cylinder liner system. A new adaptive threshold–wavelet packets transform (WPT) method was developed to extract tribological AE features. Experimental tests were conducted by fueling three fuels: pure diesel (PD), biodiesel (BD), and Fischer–Tropsch (F–T) diesel. The FAS–AE indicators of biodiesel and F–T diesel show a tiny difference compared to the baseline diesel using two types of lubricants. Biodiesel produces more AAC impacts with higher AAC–AE responses than F–T diesel, which occurs at high speeds due to high temperatures and more particles after combustion than diesel. This new algorithm demonstrated the high performance of using AE signals in monitoring the tribological impacts of alternative fuels on engines