AE mapping of engines for spatially located time series

doi:10.1016/j.ymssp.2004.10.008

Mechanical Systems and Signal Processing

Volume 19, Issue 5, September 2005, Pages 1034-1054

https://doi.org/10.1016/j.ymssp.2004.10.008 Get rights and content

Abstract

This paper represents the first step towards using multiple acoustic emission (AE) sensors to produce spatially located time series signals for a running engine. By this it is meant the decomposition of a multi-source signal by acquiring it with an array of sensors and using source location to reconstitute the individual time series attributable to some or all of these signals. Internal combustion engines are a group of monitoring targets which would benefit from such an approach. A series of experiments has been carried out where AE from a standard source has been mapped for a large number of source–sensor pairs on a small diesel engine and on various cast iron blocks of simple geometry. The wave propagation on a typical diesel engine cylinder head or block is complex because of the heterogeneity of the cast iron and the complex geometry with variations in wall-thickness, boundaries and discontinuities. The AE signal distortion for a range of source–sensor pairs has been estimated using time–frequency analysis, and using a reference sensor placed close to the source. At this stage, the emphasis has been on determining a suitable processing scheme to recover a measure of the signal energy, which depends only on the distance of the source and not upon the path. Tentative recommendations are made on a suitable approach to sensor positioning and signal processing with reference to a limited set of data acquired from the running engine.

Introduction

Acoustic emission (AE) has proven to be very useful in monitoring reciprocating machines both for faults and for running conditions. For such machines, the excellent signal-to-noise ratio afforded in the AE frequency range allows discrimination of the fine detail in the individual mechanical and fluid-mechanical events during each cycle [1]. These AE signals have been used to monitor faults such as exhaust valve leakage [2], fuel injection behaviour [3], and various aspects of the combustion process [4]. A common observation of these studies is that AE is very useful in machinery monitoring because, at high frequencies (0.1–1 MHz), the signal is unaffected by whole-body or other vibrations so that relative movements of mechanical components or fluids can be monitored. AE offers distinct advantages over other types of dynamic monitoring, such as acceleration, in that the signal analysis can be focussed onto certain processes, such as impacts associated with valve actions and flow through injectors. So far, most work in this area has been confined to the interpretation of a single sensor output and its application to fault diagnosis. On many machines, there can be some overlaps of source events (such as those coming from exhaust valves, injectors, combustion, bearings and ancillary equipment), and so the use of a sensor array could help overcome problems associated with signal contamination/interference. To optimise the sensor array, work is required to find the best sensor positions and to establish the effects of the source–sensor path on transmission of the generated disturbance. This work concentrates on the propagation of AE in cast iron, which is commonly used in IC engines.

Source location using AE has been used in many applications such as leak detection, crack detection or structural monitoring [5] where the geometry is such that some simplifying assumptions can be made about wave propagation. As far as the authors are aware, however, there is little or no work on source location for machinery monitoring. In the engine under study here, the propagation distances are small and the potential path lengths can be complex. In addition, the material involved is mainly cast iron, which shows very high internal damping due to the presence of graphite particles in the microstructure. The causes of attenuation of elastic waves have been identified, by Kolsky [6] and Pollock [7] for example, as geometric spreading of the wave-front, internal friction, dissipation of energy and velocity dispersion. Generally, the typical diesel engine block has a complex 3D geometry with variations in wall-thickness and including boundaries and discontinuities. Reflection, refraction and transmission from boundaries will occur and this makes wave propagation more complicated than on shell-like structures. Therefore, a study of wave propagation on an engine block and some simpler cast iron objects has been carried out with a view to identifying ways of dealing with attenuation of AE waves.

Section snippets

AE energy

This study is concerned with tracing the energy of an acoustic emission source as it propagates towards a sensor. The energy of the signals, whether ‘raw AE’ or preprocessed has been estimated simply by taking a mean square value over a time, t, as follows: $E = \int_{0}^{t} v^{2} (t) d t,$ where $v (t)$ is the amplitude of AE waveform in voltage (V), $t$ is time in second (s), and $E$ is acoustic emission energy in $V^{2} s$ .

The preprocessing applied and the choice of the time window are, of course, crucial in determining the

Experiment

A series of experiments were carried out on a variety of cast iron samples of varying geometrical complexity. The five test objects are shown in Fig. 1 including: a roughly slab shaped engine base plate of approximate dimensions $100 \times 140 \times 12 {cm}^{3}$ ; a surface table of approximate dimensions $122 \times 186 \times 2.5 {cm}^{3}$ ; a strip cut from the cylinder head of a scrap engine of dimensions $6 \times 40 \times 0.5 {cm}^{3}$ ; a cylinder block of a small diesel engine; and a flat panel forming part of one of the cylinder boxes of a large

Results and discussion

An estimate of the signal energy for each source–sensor pair was calculated by integrating the entire record using Eq. (1), and averaging the result for each of the five raw signals. The average and range were then plotted and the best-fit exponential decay curve, according to Eq. (2), was determined. Fig. 3 shows four example decay curves for the AE energy level, $\ln (E)$ , versus source–sensor distance (the engine base, the strip, the surface table and the flat panel). The four curves in Fig. 3

Conclusions

Studies of the propagation of AE waves generated from a simulated source on the surface of cast iron test blocks of varying geometry have been presented. It is generally observed that geometric complexity can lead to enhanced or reduced attenuation, apparently depending upon the relative effects of multiple reflections or leakage along other structural components.

A time–frequency filtering technique has been presented as a means of elucidating the relative effects of dispersion and reflection.

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