Elsevier

Measurement

Volume 43, Issue 10, December 2010, Pages 1376-1386
Measurement

Diesel engine fuel injection monitoring using acoustic measurements and independent component analysis

https://doi.org/10.1016/j.measurement.2010.08.003Get rights and content

Abstract

Air-borne acoustic based condition monitoring is a promising technique because of its intrusive nature and the rich information contained within the acoustic signals including all sources. However, the back ground noise contamination, interferences and the number of Internal Combustion Engine ICE vibro-acoustic sources preclude the extraction of condition information using this technique. Therefore, lower energy events; such as fuel injection, are buried within higher energy events and/or corrupted by background noise.

This work firstly investigates diesel engine air-borne acoustic signals characteristics and the benefits of joint time–frequency domain analysis. Secondly, the air-borne acoustic signals in the vicinity of injector head were recorded using three microphones around the fuel injector (120° apart from each other) and an independent component analysis (ICA) based scheme was developed to decompose these acoustic signals. The fuel injection process characteristics were thus revealed in the time–frequency domain using Wigner–Ville distribution (WVD) technique. Consequently the energy levels around the injection process period between 11° and 5° before the top dead centre and of frequency band 9–15 kHz are calculated. The developed technique was validated by simulated signals and empirical measurements at different injection pressure levels from 250 to 210 bars in steps of 10 bars. The recovered energy levels in the tested conditions were found to be affected by the injector pressure settings.

Introduction

The increasing interest in environmental problems has necessitated the improvement of engine performance, and reduction of noise and pollutant emissions. One of the key components, which determine engine torque, emissions, noise quality and fuel consumption, are the fuel injection equipment and the intake management system. The effects of the injection timing on the engine emissions and exhaust gas have been studied in [1]. Injection pressure, fuel quantity, injector opening and closing timings are the keys to the ideal injection process condition monitoring system. These parameters should be kept at their optimum values to reduce the fuel consumption and pollutant emissions, and increase the output power. However, measuring these parameters could not be done without fixing sensors and introducing a permanent damage into the system which may influence these parameters, e.g. needle lift and injection pressure measurement. Injection process induced noise and air-borne acoustic signals have been sporadically studied for many years. However, previous work has focused on topics other than the noise radiated from the injector itself [2], [3], [4]. The moving mass inside the injector is a small in the order of 15 g, and this mass takes a very short time, in the order of 1–3 ms, from the fully open to fully closed position. There is a distinct opening and closing vibrations and acoustic signals for most injectors. The opening vibrations and induced acoustic signals are due to the moving mass hitting the upper stop and the closing ones are due to the moving mass hitting the seat. As a result, the acoustic signals induced by the diesel injector are very short click, with broad frequency content, and radiated from the surface of the injector itself or transmitted through the fuel system or the engine block. Unfortunately, these diagnostic signatures are dominated by the other energy sources and corrupted by background and interference noises.

Most of the work in the literature on diesel fuel injection has been devoted to describing the noise and vibration generated by the fuel pumps and fuel lines. Injector dynamics, needle movements and their resulting vibrations were studied by Gu and Ball [5], [6]. They described the vibration characteristics of injectors by three series of transients during an injection cycle; fluid excitation commencing prior to needle impact, needle opening impact and needle closing impact. The injection process was also studied by Gill et al. [7] using acoustic emission and has achieved better results than vibration signatures in detecting the pressure build up activities prior the opening of the needle valve. The injection and exhaust valves opening and closing events were also studied using acoustic emissions in [8]. In this work the injection induced air-borne acoustic signals measurements were recorded remotely and ICA based scheme was developed to monitor injector operation parameters. Fig. 1a shows engine (four cylinders, direct injection) air-borne acoustic signals measured using a microphone located 1 m above the floor and 1 m away from the cylinder manifold with the engine running at an average speed of 1000 rpm (16.7 Hz) and with no load [9], [10], [11].

The air-borne acoustic signal shown in Fig. 1a was enhanced by averaging. In practice, it is frequently the case that with a repeated signal, the signal to noise ratio can be improved by averaging, particularly where the corruption of the signal is due to unwanted noise occurring as a result of random events. Time-domain averaging is a way to reduce the content of undesired components in a signal.

The main feature could be observed from the acoustic waveform, shown in Fig. 1a, is four peaks corresponding to the engine firing sequence and these represent combustion events in the cylinders 3, 1, 2 and 4 respectively. What makes the waveform complicated and difficult to extract information from is the numerous frequency components superimposed on each other. In the associated power spectrum, shown in Fig. 1b, four peaks can be seen; the first at twice the frequency of revolution (33.4 Hz), the second at four times the frequency of revolution (66.8 Hz), the third at 100 Hz and the fourth at 470 Hz. The amplitudes of any higher harmonics can be ignored because they contain considerably less energy than the first four leading terms. Each cylinder experiences fuel injection and combustion once for every two complete revolutions of the crankshaft. Thus the number of ‘combustions’ per single revolution of the camshaft will be equal to (number of cylinders)/2. Here there are four cylinders, so there will be two combustion processes during each complete revolution of the camshaft, and the corresponding noise peak will occur at twice the fundamental frequency (2 × 16.7  33 Hz). The amplitude of this peak is highly dependent on the combustion conditions. The second peak (67 Hz) is probably due to closing knock of the valves, which occur twice per crankshaft revolution, every second revolution in each cylinder. By increasing the load the amplitude of both of these peaks increase. The third peak at 100 Hz, which corresponds to the third harmonic of the firing frequency, also increases in amplitude at higher loads. It has been shown that the peaks at 100 and 470 Hz are due to the vibration of the oil sump and front timing gear cover [12].

Fig. 1c shows the acoustic waveform of the diesel engine in the time frequency domain using the Wigner–Ville distribution (WVD). From the WVD representation we can see clearly four peaks representing the combustion events of the engine cylinders in the firing order from left to right (3, 1, 2, and 4). The spectral analysis shows that the major part of the energy is located in the lower frequencies (below 5 kHz), this can be seen more clearly in the WVD representation, and also we can see that the peak of the WVD extends to around 35 kHz.

Clearly, no weak events could be observed from Fig. 1; although we know the exact time of some of these events, the occurrence of the event around cylinder#1 is shown in Fig. 2. The features of such weak events are dominated by other higher energy sources and/or buried in a high-level of background noise, so it is necessary to extract them from such background noise to accurately evaluate the injection process condition. Traditional methods seem incapable of solving such problems because the background noise contains not only Gaussian noise but also disturbances from other sources and the adjacent machines; coherent filtering techniques and adaptive noise cancelling (ANC) could be used to improve the signal to noise ratio of a diagnostic signal. The drawback of these two methods is that the first relies on a synchronizing signal and the second needs a reference signal which is not available in this case [13], [14]. BSS and ICA provide a new technique for solving these problems making its application to machine fault diagnosis very attractive.

The application of independent component analysis (ICA) in machine fault diagnosis has been developing rapidly and provides a new technique for the separation of source signals under high-level background noise and, thus, the possibility of enhancing these weak and corrupted features. It should be possible for ICA to be used for: noise cancellation, the extraction of weak signals, the separation of sources, feature extraction and pattern classification.

Gelle et al. [15] have applied the BSS technique to machine fault diagnosis, using BSS as a pre-processing step for the detection and diagnosis of faults in rotating machinery. Gelle’s paper compares temporal and frequency based approaches to the solution of the BSS problem for rotating machine signals. Roam et al. [16] proposed an adaptive BSS algorithm based on information maximisation, and applied this technique to gear tooth failure detection and analysis. Because a signal to noise ratio improvement alone does not sufficiently enhance a machine signal to allow fault evaluation, Knaak et al. [17] proposed an assessment for BSS algorithms to verify their applicability with respect to machine signals.

Ypma et al. [18] suggested a novel approach to fault detection in rotating machinery. He proposed that ICA was used to combine various vibration measurements made on a machine to produce a vector domain description which would be compared with an admissible domain indicating normal machine operation. This approach was successfully applied to the monitoring and diagnosis of a submersible pump.

Tian et al. [19] combined ICA in the frequency domain (ICA-FD) with Morlet wavelet filtering for gearbox fault diagnosis. Vibration signals from a gearbox were separated into two components using ICA-FD. Morlet wavelet filtering was then applied to the separated components. Better diagnosis was obtained with this combination compared to using wavelet filtering alone. ICA techniques were applied to a diesel engine by Li et al. [20] in an attempt to identify diesel engine noise sources.

In the work reported here, an air-borne acoustic signals and ICA algorithm based scheme was developed to extract some of these hidden features and consequently utilise these features for monitoring the injector health. Firstly the dominant harmonics were removed, secondly a proposed ICA algorithm was applied to decompose the residual signal, thirdly the injector operation induced acoustic signals were estimated in the time–frequency domain using the Wigner–Ville distribution (WVD) and finally the energy levels around the injection process event between 11° and 5° before the top dead centre (TDC) were calculated and were found to be directly affected by the injector condition.

It is evident that the proposed procedure has given clearer results to monitor the injector opening and closing induced air-borne acoustic signals. More importantly, signal to noise ratio improvement allows various statistical methods to be successfully used in diagnosing other and compound faults.

Section 2 in this paper introduces the ICA algorithm structure; the application of this algorithm on a modelled engine induced air-borne acoustic signals are also presented. The instrumentation test rig facilities and the exploitation of ICA algorithm on a working engine measured air-borne acoustics are described in Section 3. Fault simulation and the experimental results are presented in Section 4. In Section 5 a summary is given and few conclusions are outlined.

Section snippets

Theory and background

BSS is a technique for separating the independent components of mixed signals recorded by different sensors when the source of the signals and transmission channels are unknown. BSS comprises independent component analysis (ICA) [21]. Here “blind” have two meanings [22]:

  • 1.

    The system inputs cannot be directly observed.

  • 2.

    The mixture form (mixing matrix) of sources is unknown.

Obviously, the mathematical model for the relationship between the sources and the sensors is not well defined, and the BSS may

Air-borne acoustics modelling

Here, we will illustrate with an example the application of the proposed ICA algorithm to the separation of modelled diesel engine air-borne acoustic signals. The air-borne acoustic signals from ICE, e.g., diesel engine are composed of many components emitted from different sources. These sources include combustion, mechanical, and a combination of both. Understanding the components of the air-borne acoustic signal is essential to identify the requirements for acoustic signal analysis. Table 1

Test rig and measurement strategy

The experiments were performed with a four-stroke, four-cylinder, in-line OHV, direct injection, Ford FSD 425 type diesel engine. The schematic diagram for the test rig and instrumentation are shown in Fig. 9.

Three microphones are used to collect air-borne acoustic signals; combustion pressure, injector vibration and injector line pressure are also recorded. The flywheel TDC trigger signal is used to set the start time of data collection so that each data segment is measured at an exact crank

Conclusions

A condition monitoring scheme based on air-borne acoustic signals measurements and independent component analysis has successfully recovered the original sources for simulated diesel engine air-borne acoustic data. The developed scheme separates the real acoustic sources to reduce the affects of the major sources with the stipulation that the sources of the engine air-borne acoustic signal and the transmission channels are unknown. This scheme offers better condition monitoring approaches by

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