METHOD FOR MONITORING ACOUSTIC EMISSIONS

Abstract: A method for monitoring the operating conditions of process plant or machinery comprises the steps of (a) detecting the sound intensity of the plant or machinery by means of a sound transducer, e.g., a microphone, the output from which is an analogue signal, the intensity of which is proportional to the intensity of the sound in the audio frequency range, (b) converting the analogue signal to a digital signal, (c) passing the digital signal through a digital band-pass filter to select at least two frequency bands, (d) utilising the signal power in these bands as signal descriptors and putting them into a multivariate pattern recognition procedure, (e) utilising the pattern recognition procedure to develop soft models to characterise the signals recorded and to assign them to a particular category of known operating conditions, (f) again detecting the sound intensity of the process or machinery by means of the sound transducer, (g) converting the analogue signal to a digital signal, (h) passing the digital signal through a ditigal band-pass filter to select the same frequency bands as in (c), (i) utilising the signal power in these bands as signal descriptors and putting them into a multivariate pattern recognition procedure, and (j) using the soft models as templates, such that when operating conditions repeat themselves they may be recognised, or a change in conditions may be detected.

Description: This invention relates to a method for monitoring the operating conditions of process plant and machinery by the analysis of naturally occurring acoustic emissions. Industrial process plant and machinery emit sound during their normal or abnormal operation. A change in the operating conditions, e.g. a cyclone separator ceases to function properly or a drill bit becomes blunt, often results in a change in the frequency distribution of the sounds. Other examples where this effect may be noticed include mills, crushers, grinders, fluidised beds, powder transport systems, compressors, pumps, blenders, propellers, turbines, engines, and valves. We have now discovered a method for monitoring the frequency of the sound emitted during an operation and classifying in the sounds produced under different conditions so that the sounds may be related to the conditions causing them. Soviet Journal of Nondestructive Testing Vol.21 No.10 October 1985 reveals an apparatus for the vibroacoustic diagnosis of pumping machinery in which acoustic emission temperature kinetic curves and acoustic emission spectra are obtained and used. 5 th Int. Conf. on pattern recognition Vol.1 4 December 1980 at pp 108-111 reveals a method by which pattern recognition techniques are used in the identification of acoustic emission signals. Thus according to the present invention there is provided a method for monitoring the operating conditions of process plant or machinery which method comprises the steps of: (a) detecting the sound intensity of the plant or machinery by means of a sound transducer, e.g., a microphone, the output from which is an analogue signal, the intensity of which is proportional to the intensity of the sound in the audio frequency range, (b) converting the analogue signal to a digital signal, (c) passing the digital signal through a digital band-pass filter to select at least two frequency bands, (d) utilising the signal power in these bands as signal descriptors and putting them into a multivariate pattern recognition procedure, (e) utilising the pattern recognition procedure to develop soft models to characterise the signals recorded and to assign them to a particular category of known operating conditions, (f) again detecting the sound intensity of the process or machinery by means of the sound transducer, (g) converting the analogue signal to a digital signal, (h) passing the digital signal through a digital band-pass filter to select the same frequency bands as in (c), (i) utilising the signal power in these bands as signal descriptors and putting them into a multivariate pattern recognition procedure, and (j) using the soft models as templates, such that when operating conditions repeat themselves they may be recognised, or a change in conditions may be detected. Steps (a) - (e) are known as the training mode and steps (f) - (j) as the operational mode. If necessary, the analogue electrical signal can be amplified and/or passed through analogue band-pass filter to remove frequencies above, say 10 KHz to prevent aliasing, and below say, 50 Hz to remove spurious low frequency signals. The digital band-pass filter is preferably capable of selecting from two to twenty-four desired frequency bands. Suitable multivariate statistical techniques are the methods of principal components analysis (PC) and partial least squares (PLS). PC analysis is a numerical technique which allows multivariate data to be displayed in two dimensions for ease of interpretation. The PC's are vectors which are linear combinations of the original features and take the form:- Y j =A 1j X₁+A 2j X₂..........+A nj X n where Y j = jth principal component X n = feature An j = coefficient The PC's are all orthogonal and equal in number to the measured features. The coefficients of the vectors are calculated so that most of the variance in the data is expressed in the first few PC's whilst the relative magnitude of the coefficient reflects the discriminatory power of the original features. By transforming the data using equations of the above type the data may be plotted in a low dimensional form. PLS analysis allows the relationship between sets of data (which are related or where it is expected that a relationship exists) to be empirically modelled. Two "training sets" of data (in this case, machinery operating under different conditions and AE (Acoustic Emission) frequency distribution) are used to generate the model. Having developed the model, validation is carried out. "Test sets" of data, acquired under the same conditions but not used to build the model, are then input. Where actual values are known, the errors between these and the values generated are used to validate the accuracy of the model's predictions. If one set is unknown PLS is able to predict the values from knowledge of the other, using the model. A suitable multivariate pattern recognition technique for 'soft modelling" the frequency distribution is SIMCA (a disjoint principal component analysis technique). For more details of the statistical methods which may be used, see for example the textbooks; "MULTIVARIATE ANALYSIS," Wold,H,. Academic Press, New York, 1966, Krishnaiah, P.R. (Ed.) and Sharaf, M.A, Illman, D.L and Kowalski, B.R, "CHEMOMETRICS", John Wiley and Sons Inc, New York, 1986, (Elving, P.J., Winefordener, J.D, and Kolthoff, I.M., eds). The method is applicable to any type of machinery or process plant that generates sound during its operation. It provides the means for determining the operating conditions or change in the operating conditions on-line and non-invasively in near "real-time". The invention is illustrated by the following Examples 1 and 2 and Figures 1-3 of the accompanying drawings wherein Figure 1 is a schematic diagram of apparatus for use in the method according to the present invention, Figure 2 is a plot of the PC analysis for Example 1, and Figure 3 is a plot of the PC analysis for Example 2. Example 1 In Example 1, the sound from a small electrical pump was monitored. Acoustic data was acquired while water was being pumped, while the pump was allowed to run dry and while the pump was stopped. The equipment used is described with reference to Figure 1. The pump 1 was driven by an electric motor 2. The sound intensity of pumping was picked up by a flat frequency response audio-microphone 3. This was an Archer condenser microphone and was mounted in a fixed position close to the pump. Electrical signals from the microphone were amplified using a Rhomicron Instruments MAPS 1 preamplifier 4. The amplified signals were then analysed by a third octave analyser 6 (Data Beta, DBDTO-10). This comprises an analogue-to-digital converter 7 front-ended with a 12.5 KHz low pass anti-aliasing filter 5, a digital band-pass filter 8, a central processing unit (CPU) 9 and an RS-232 serial line interface 10. The band-pass filter uses a series of digital filters to divide the region 50 Hz to 10 KHz into 24 bands. The acoustic powers in these bands are listed in Table 1. The analyser averages the power in each of these bands for a specified period. In all cases 15 seconds averaging times were used. The system is controlled by an IBM PC-AT computer 11 linked to the serial line interface 10 by means of a cable 12. Frequency data was analysed using PC analysis implemented on the microcomputer 11. Two or three samples from each category were then removed to form a test set. The remaining samples were used to form a training set for SIMCA (a pattern recognition technique). The test set was then used to validate the SIMCA models. The Principal Component Analysis illustrated graphically in Figure 2 shows that the sound of the pump in normal operation (pumping water), running dry and stopped (just background noise) are easily distinguishable. SIMCA was 100% successful in classifying the seven members of the test set, see Table 3A. Example 2 Example 1 was repeated with the difference that a small industrial blender was substituted for the pump. Acoustic data was acquired when the blender contained a minimum quantity of water, when it was full of water and when it was full of a viscous polyalkylene glycol. The acoustic powers in the 24 frequency bands are listed in Table 2. The data was analysed and treated as in Example 1. The Principal Component Analysis illustrated graphically in Figure 3 shows that the three operating conditions are easily distinguishable. As before, SIMCA was 100% successful in classifying the six members of the text set, see Table 3B.

Claims: A method for monitoring the operating conditions of process plant or machinery characterised by the fact that the method comprises the steps of: (a) detecting the sound intensity of the plant or machinery by means of a sound transducer, the output from which is an analogue signal, the intensity of which is proportional to the intensity of the sound in the audio frequency range, (b) converting the analogue signal to a digital signal, (c) passing the digital signal through a digital band-pass filter to select at least two frequency bands, (d) utilising the signal power in these bands as signal descriptors and putting them into a multivariate pattern recognition procedure, (e) utilising the pattern recognition procedure to develop soft models to characterise the signals recorded and to assign them to a particular category of known operating conditions, (f) again detecting the sound intensity of the process or machinery by means of the sound transducer, (g) converting the analogue signal to a digital signal, (h) passing the digital signal through a digital band-pass filter to select the same frequency bands as in (c), (i) utilising the signal power in these bands as signal descriptors and putting them into a multivariate pattern recognition procedure, and (j) using the soft models as templates, such that when operating conditions repeat themselves they may be recognised, or a change in conditions may be detected. A method according to claim 1 wherein the sound transducer is a microphone. A method according to either of the preceding claims wherein the analogue electrical signal is amplified. A method according to any of the preceding claims wherein the analogue electrical signal is passed through an analogue band-pass filter. A method according to claim 4 wherein frequencies above 10 KHz are removed. A method according to any of claims 4 to 5 wherein frequencies below 50 Hz are removed. A method according to any of claims 4 to 6 wherein the digital band-pass filter is capable of selecting from 2 to 24 desired frequency bands. A method according to any of the preceding claims wherein the multivariate statistical technique is the method of principal components analysis. A method according to any of the preceding claims wherein the pattern recognition technique is the SIMCA technique.

Inventors: BELCHAMBER, RONALD MALCOLM, COLLINS, MICHAEL PATRICK
Applicants: THE BRITISH PETROLEUM COMPANY P.L.C.
Application year: 1988
Main classification: G01N29
IPC codes: G01H3, G01N29, G01H17