Patent Abstract:
A method for detecting the presence of a noise signal within a noise measurement field, where the noise measurement field includes a noise signal emanating from a noise source, and where the noise signal is mixed with extraneous noise existing within the noise measurement field. The method involves using a plurality of acoustic transducers arranged in a plurality of arrays to monitor the noise measurement field at a plurality of spatially separated locations. Outputs of the transducers are sampled to generate time series data. The time series data is processed to identify whether the noise signal is present.

Full Description:
FIELD 
       [0001]    The present disclosure relates to systems and method for analyzing noise emitted from one or more noise sources, and more particularly to a system and method for processing time series data obtained from a plurality of acoustic arrays to analyze one complex noise source, or a plurality of complex, spatially separated and/or distributed noise sources. 
       BACKGROUND 
       [0002]    The statements in this section merely provide background information related to the present disclosure and may not constitute prior art. 
         [0003]    In noise detection and analysis systems, acoustic transducers such as microphones are employed to collect noise signals emanating from one or more noise sources. However, single, omni-directional microphone measurements are incapable of discriminating between different noise signals emanating from multiple, and typically spatially separated and/or distributed, noise sources. The presence of multiple noise sources severely complicates the analysis of correlation data from the microphones. 
         [0004]    An example of a difficult noise correlation problem is the noise radiated simultaneously from the inlet and exhaust nozzles of a jet aircraft engine, that is, two spatially separated noise sources. The inlet/exhaust noise sources separately radiate outwards to the external measurement field. If the inlet noise and the exhaust noise contain a noise signal emanating from a common source, for example a particular component or surface within the jet engine, then they can have a measurable degree of correlation in the external measurement field. The first challenge is thus determining whether or not the inlet and exhaust noise sources are correlated. This is complicated by noise from other various components of the engine that emanate from the engine or the downstream exhaust flow and are picked up by the microphones, as well as extraneous noise sources (e.g., vehicles operating in the area; aircraft flying overhead; construction work) existing in the measurement environment that is picked up by the microphones. These forms of extraneous noise, both coming from within and external to the engine and from sources remote from the engine, are picked up by the microphones and operate to “mask” the existence of any noise signal having a correlation that is picked up by the microphones. 
         [0005]    In the above described example, even if a correlation between two noise signals, picked up by two spatially separated microphones, is determined to exist, then the next challenge is to determine the locations in the external measurement field at which the correlation values are a maximum (or of meaningful high level). Still another challenge is the determination of the spatial extent (in the measurement field) of the correlation which arises from spatially distributed noise sources. An example of such spatially distributed noise sources might be correlated noise sources along a wing flap trailing edge; correlated noise sources within the jet mixing region downstream of the jet engine exhaust nozzle; etc. 
         [0006]    From the foregoing, it will be appreciated that determining when a correlation exists between two spatially separated noise sources presents significant challenges. Determining the locations within the measurement field where the correlations are a maximum, as well as the spatial extent within the measurement field where the maximum correlation exists, represent even further significant challenges with presently available noise monitoring/measuring systems and methods. 
       SUMMARY 
       [0007]    In one aspect the present disclosure relates to a method for detecting the presence of a noise signal within a noise measurement field, where the noise measurement field includes a noise signal emanating from a noise source, and where the noise signal is mixed with extraneous noise existing within the noise measurement field. The method may comprise: using a plurality of acoustic transducers arranged in a plurality of arrays to monitor the noise measurement field at a plurality of spatially separated locations; sampling outputs from said acoustic transducers to generate time series data; and processing said time series data to identify whether said noise signal is present. 
         [0008]    In another aspect a method is disclosed for determining a relationship between acoustic noise signals originating from acoustic waves radiating from multiple sources, where the multiple noise sources are located within a noise measurement field. The method may comprise: using a plurality of acoustic transducers arranged in first and second arrays to monitor the noise measurement field at a plurality of spatially separate locations within the noise measurement field; sampling electrical signals output from said acoustic transducers and generating time varying signals therefrom; processing said time varying signals by aligning the signals originating at the same time from a given spatial location into a delayed-time representation data set and generating an averaged time varying signal from each respective delayed-time representation data set from each of said first and second arrays; and analyzing said averaged time varying signals to determine a correlation between noise signals originating from said first and second arrays. 
         [0009]    In another aspect the present disclosure provides a system for determining a relationship between acoustic noise signals originating from acoustic waves radiating from multiple sources, where the multiple noise sources are located within a noise measurement field. The system may comprise: a plurality of acoustic transducers arranged as acoustic phased array antennas in first and second arrays to monitor the noise measurement field at a plurality of spatially separate locations within the noise measurement field, the acoustic transducers adapted to generate electrical signals in response to reception of acoustic signals present within the noise measurement field; an array processing subsystem including beamforming algorithms to generate time series data therefrom; and a signal processing subsystem adapted to process said time series data and to generate a first averaged time varying signal associated with an output said first array, and a second time varying signal associated with an output from said second array, and further adapted to analyze said first and second averaged time varying signals to determine a correlation between noise radiating from said multiple sources. 
         [0010]    Further areas of applicability will become apparent from the description provided herein. It should be understood that the description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]    The drawings described herein are for illustration purposes only and are not intended to limit the scope of the present disclosure in any way. 
           [0012]      FIG. 1  is a perspective view of a system in accordance with one embodiment of the present disclosure set up to monitor noise emanating from two spatially separated locations of a jet engine; 
           [0013]      FIG. 1A  is a diagram illustrating how the transducers that make up the two arrays may be selected; 
           [0014]      FIG. 2  is a flowchart setting forth basic operations performed by the system shown in  FIG. 1  in analyzing noise emanating from two spatially separated locations; and 
           [0015]      FIG. 3  is a perspective view of another implementation of a system in accordance with the present disclosure in which a plurality of beam steered phased arrays, each having randomly distributed acoustic spiral arm transducers, are used to monitor noise signals emanating from a moving mobile platform, in this example a jet aircraft. 
       
    
    
     DETAILED DESCRIPTION 
       [0016]    The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses. 
         [0017]    Referring to  FIG. 1 , a system  10  for analyzing one noise source, or a plurality of spatially separated noise sources, is illustrated. In this example two spatially separated noise sources  12  and  14  are shown being at the primary exhaust nozzle  16  and fan exhaust nozzle  18 , respectively, of a jet engine  20  while the engine is operating. The noise sources  12  and  14  can be thought of as being positioned within a noise field shown in dashed lines  22 . The noise field  22  forms a complex noise field that includes not only the noise emanating from the first and second noise sources  12  and  14  but also from extraneous noise sources such as the engine generated noise in the downstream exhaust region, wind, other vehicles operating in a vicinity of the noise field  22 , aircraft and/or rotorcraft flying over the noise field, just to name a few. Thus, it will be appreciated that while two primary noise sources  12  and  14  are present in the noise field  22 , typically noise will exist from one or more additional sources as well. 
         [0018]    It will also be appreciated that the present system  10  is not limited to use in analyzing only two distinct, spatially separated noise sources, but rather is equally well suited to analyzing one, three, four or more distinct and/or spatially distributed noise sources. Furthermore, the noise sources do not need to be stationary, as illustrated in  FIG. 1 , but rather could involve moving mobile platforms, as will be discussed in connection with  FIG. 3 . The system  10  and method of the present disclosure is expected to find particular utility in analyzing noise emitted and/or emanating from various areas or components of a jet aircraft. However, the system  10  and method of the present disclosure is equally well suited to analyzing noise associated with the operation of any form of fixed structure, or any form of mobile platform such as a land vehicle (car, truck, or bus) or a train. Still other potential applications may include analyzing noise associated with the operation of other forms of airborne mobile platforms such as rotorcraft, spacecraft and various forms of unmanned airborne vehicles. Still other potential applications may include analyzing noise associated with the operation of surface and underwater marine vessels. 
         [0019]    In  FIG. 1  the system  10  includes a plurality of acoustic transducers  24  that can be selected by the system  10  to form one, two or more acoustic phased array antennas. For convenience, the transducers  24  in  FIG. 1  are shown forming two arrays, i.e., a first acoustic phased array antenna  26  (hereinafter simply the “first array”  26 ) and a second acoustic phased array antenna  30  (hereinafter “second array”  30 ). The first and second arrays  26  and  30  are further arranged in a desired orientation, for example along a longitudinal line or path  32 . However, the arrays  26  and  30  do not necessarily need to be arranged along a longitudinal path, as will be discussed further in the following paragraphs. Instead, the first and second arrays  26  and  30  could be located at any position within the noise field  22 , or possibly to form a dome or sphere around the noise source(s). The distance from each acoustic transducer  24  to the noise source is further known or assumed. 
         [0020]    In the general case, beamforming measurements can first be made over a volume in space in order to determine the spatial extent and locations of noise sources. The beamforming spatial region may be restricted to, for example, a plane (e.g., a plane which cuts through the engine axis) or to a single line of interest (e.g., the engine axis centerline/rotation axis). 
         [0021]    The acoustic transducers  24  are also preferably spatially separated from one another to provide non-redundant acoustic transducer-to-acoustic transducer spacing between any two of the acoustic transducers  24 . This non-redundant spacing technique inhibits spatial aliasing (i.e., false images) as is well known in the art. 
         [0022]    It will be appreciated that while the arrays  26  and  30  are shown adjacent to one another, that they could also be arranged to overlap one another by a desired amount. This would allow for the determination of the relationship between the beamformed time series outputs of each of the arrays  26  and  30  when the relative spatial location of each array is changed. For example, with reference to  FIG. 1A , the locations of transducers  24  of array  26  could be fixed, while the transducers  24  that are selected to form array  30  are varied. As an illustration of this, in  FIG. 1A  the initial selection of transducers  24  for array  30  is selected to be the exact same transducers as those that are designated to form array  26 , as indicated by dashed line A. Thus, the 9 transducers  24  (represented by squares) within dashed line A will be considered as all belonging to both array  26  and array  30 . In this case the outputs of both arrays  26 , 30  will be identical and there will be a perfect relationship between the beamformed time series output of each array. 
         [0023]    The next selection of transducers  24  for array  30  could then include that group of transducers shown in  FIG. 1A  as being circled by dashed line B. This new selection of transducers  24  for array  26  is similar to the initial selection but now only overlaps a portion of array  26  (now designated by dashed line A), and thus shares only a portion of the transducers used for array  26 . In general, the relationship (i.e., magnitude of correlation) between the beamformed time series output of array  30  (represented by dashed lines B) and the output of the transducers  24  forming array  26  will typically now be less than in the preceding case. The spatial distance D 1  represents the spatial distance between the phase centers of the two arrays  26  and  30 . It will be appreciated, however, that any subset of microphones can be used to define an array. Both the number of microphones and length of the array subsets can be varied. 
         [0024]    Subsequently, a new group of transducers  24  may be selected to form array  30 , as indicated by dashed line C, and will have spatial distance from array  26  represented by line D 2 . The output from array  30  (represented by dashed lines C) will have a correlation with the output from array  26  that may be even smaller yet than the previously selected transducers  24  (i.e., those indicated by dashed lines B). Finally, another group of transducers  24  within dashed lines D may be selected to form the array  30 . This group will have a spatial distance D 3  separating the phase centers of array  30  and array  26 . The magnitude of the correlation between the outputs of array  30  and array  26  may be even less than the previous selection of transducers  24  for the array  30  (i.e., those represented by dashed line C). This process thus yields the variation in space of the relationship between the array  30  output beamformed time series and the stationary (fixed location) array  26  beamform output time series. It will be appreciated that while in this example the transducers  24  selected for the array  26  have not changed, that the locations and selection of the transducers used for array  26  could also be varied. 
         [0025]    In  FIG. 1  the system  10  also includes a first array processing subsystem  34  and a second array processing subsystem  36 . The first array processing subsystem  34  includes suitable array processing algorithms that incorporate beamforming algorithms to generate beamformed time series data from the electrical output signals provided by each of the transducers  24  that form the first array  26 . Similarly, the second array processing subsystem  36  also includes suitable array processing algorithms that incorporate beamforming algorithms to generate beamformed time series data from the electrical output signals generated by the transducers  24  that are forming the second acoustic array  30 . 
         [0026]    The electrical output signals from all transducers  24  are sampled simultaneously and recorded to computer hard disk. The first array processing subsystem  34  selects the electrical output signals from all of the transducers  24  of the first array  26  to generate the acoustic transducers  24  time series data (i.e., time varying electrical signals) from the first array  26 , while the second array processing subsystem  36  samples the electrical output signals (i.e., time varying electrical signals) from the second array  30 . In effect, the time varying signals from the acoustic transducers  24  of each array  26 ,  30  are delayed in time in such a manner that an acoustic wave (i.e., noise wave) propagating from a given assumed noise source location will be registered at the same relative instant in the delayed-time representation of the data from the transducers  24  of array  26 , and at the same instant in the delayed-time representation of the data from the transducers  24  forming array  30 . 
         [0027]    Thus, it will be understood that typically, the data is acquired simultaneously from all of the transducers  24  and then the data is saved to a computer disk. After the data has been written to disk, then one can then select whatever subsets of transducers one would like to employ for the first and second array correlation analyses. As noted later herein, though, one could imagine a “real-time system” for which the first and second arrays are mobile arrays and where the location of one of the arrays is continually changed in order to probe the noise field in order to determine the two array locations at which a desired correlation exists between the beamformed time series data output by the two arrays. 
         [0028]    The delayed-time representation signals from all transducers  24  of array  26  are then summed together, and the signals from the transducers  24  of array  30  are separately also summed together. Since the signal of interest (the propagating noise wave) occurs at the same time instant in the delayed-time representation, the summation will produce a summed (reinforced) representation of the signal of interest, whereas all other signal components which are not in phase with the signal of interest will be suppressed. The values of the summed signals are then divided by the number of transducers used in the process (i.e., for each array  26  or  30 ) in order to provide the average (beamformed) time series representation of the signal arriving from the assumed source location. 
         [0029]    By averaging the time series data from the first array processing subsystem  34 , the first averaged time varying signal effectively provides a spatially filtered representation of the noise signal emanating from first noise source location  12  with noise from other sources being suppressed. Also, this serves to reinforce the acoustic waves radiating from the first noise source location  12  that are picked up by the acoustic transducers  24  and used to provide the data that forms the first averaged time varying signal (i.e., beamformed time series) from array  26 . Similarly, the second averaged time varying signal from array  30  provides a spatially filtered representation of the noise signal emanating from the second noise source location  14  and suppresses noise from other sources besides the second noise source  14 . 
         [0030]    This operation effectively serves to “beam-steer” the time series data being generated by each array processing subsystem  34  and  36  in the time domain, which results in beamformed time series data (having amplitude and a phase) in which noise sources other than the noise source of interest (i.e., noise sources  12  and  14 ) have been removed. This is a well-known method familiar to those skilled in the art and is referred to as “delay-and-sum” beamforming. However, the system and method of the present disclosure is not limited to the time-domain delay-and-sum beamforming methodology, but may just as well include frequency-domain beamforming methods and techniques. These methods allow for measurements of the noise originating at a location of interest to be reinforced while simultaneously suppressing the contributions from noise sources originating at locations other than the location of interest. It will be appreciated also that this beam-steering operation may instead be performed in the frequency domain through the vector product of the complex conjugate of the frequency domain steering vector with the vector of microphone spectral levels (both amplitude and phase) at a given frequency. 
         [0031]    It will also be appreciated that the transducers  24  used to form the arrays  26  and  30  preferably all remain physically fixed in location (relative to the ground surface) during the entire test. This enables beamforming to be accomplished by the transducers  24 . Beamforming is a significant advantage because the transducers  24  do not need to be physically moved around (i.e., physically “steered”) during tests (although such could be done). 
         [0032]    It will also be appreciated that in the industry, hand held/portable phased array systems are being developed and used for quick and easy localization of noise sources for a wide variety of applications (automobile engine/tire/sideview mirror noise, office room noise, etc.). The present system and method could easily be used with such devices. For example, one could place one of the portable arrays at one location and then use a hand held second array to move about the noise field until a desired correlation is achieved between the beamformed time series being output by the two arrays. 
         [0033]    The system  10  further includes a signal processing subsystem  38  that may comprise a digital signal processing (DSP) subsystem. For convenience, this subsystem will be referred to simply as the “DSP subsystem”  38 . The DSP subsystem  38  receives the beamformed time series data from outputs  34   a  and  36   a  of the array processing subsystems  34  and  36 , respectively, and uses standard digital signal processing methods to analyze the correlation between the two averaged, time varying signals (i.e., between the beamformed time series signals). The signal processing methods include standard methods familiar to practitioners of the art, such as cross-correlation, cross-spectra, coherence and phase properties between the time varying signals. Also, it will be appreciated that the signal processing methods employed with the system  10  and method of the present disclosure are not limited to analysis of only two time varying signals (auto- and cross-power spectral analyses). Rather, the signal processing methods chosen for use may, employ polyspectral analyses wherein a mutual relationship among three or more array beamformed time series data are analyzed to determine the higher-order relationships amongst them (which, for three signals, includes calculations of the auto-bispectra, cross-bispectra, bicorrelations, auto-bicoherence spectrum, and so on). This may involve initially identifying if a correlation exists between the two time varying beam-steered signals. 
         [0034]    The identification of the existence of a correlation between the two signals signifies that a common noise component is being received by both of the first and second arrays  26  and  30 . Put differently, this would mean that a noise signal emanating from the first noise source  12  is similar in content to the signal emanating from the second noise source  14 . This is because if the noise emanating from the two noise sources  12  and  14  is originating from a common source, in this example from the same common cause within the engine  20 , then they will have a measureable degree of correlation within the noise measurement field  22 . 
         [0035]    The DSP subsystem  38  may also use suitable signal processing techniques to determine an approximate location within the external noise measurement field  22  at which the correlation between the two beamformed time series signals is at a maximum, as well as the spatial extent within the noise measurement field  22  of the correlation. This can be done (as described above with regard to  FIG. 1A ) by fixing the location and distribution of transducers in one array and then varying the location (and possibly the distribution of transducers) in a second array. It may be that, as the separation distance metric between the two arrays is increased, the correlation between the beam-steered time varying signals from both arrays continuously decreases. 
         [0036]    When the level of correlation decreases below some defined value, the array separation distance at which this occurs can be used to define a maximum correlation length (or correlation distance) between the fixed and the separated arrays over which a relationship is defined to exist. For some combination of transducer distributions and array location (other than the case where the two arrays are identically co-located and the correlation is a maximum), there may be a result for which the correlation between arrays initially decreases as the second array&#39;s separation distance from the first array increases, but then the correlation increases with further increases (to a local maximum in correlation value) at larger array separation distances. For example, the spatial extent in this example might be the determination that the correlation exists along a wing flap trailing edge, or that the correlation exists within the jet mixing region downstream of the exhaust nozzle  16  of the jet engine  20 . In the jet engine example, since it is known that jet noise radiates outwardly with a particular radiation pattern, for example containing two distinct lobes, then the first and second arrays  26  and  30  could be positioned along the known lobe radiation directions to determine the correlation, if any, between noise signals emanating along these paths, as well as to determine the extent of the regions over space for which the correlation exists. 
         [0037]    Referring further to  FIG. 1 , the display system  40  may be used to graphically display correlation information to a user for analysis. The display system may comprise a liquid crystal display (LCD), a cathode ray tube (CRT) display or any form of display that is suitable for displaying a graphic representation of the correlation information. 
         [0038]    Referring to  FIG. 2 , a flowchart  100  is shown that summarizes operation of the system  10 . The transducers  24  are selected that will form each of the first array  26  and the second array  30 , such that the arrays  26  and  30  are initially arranged within the noise field  22  at desired locations relative to the spatially separated noise sources  12  and  14 , as indicated at operation  102 . At operation  103 , with the noise source being active, the outputs of all acoustic transducers  24  are simultaneously sampled and recorded to computer hard disk. However, as noted earlier, “real-time” systems could be used (that is, data would not have to be recorded to disk since the beamforming could be accomplished using a specially built “beamforming chassis”. At operation  104  the first and second array processing subsystems  34  and  36  select the outputs from acoustic transducers  24  of the two spatially separated arrays  26  and  30  which have been recorded to computer disk at operation  103 . At operation  106  the array processing subsystems  34  and  36  each take the signals from the arrays  26  and  30  and align their respectively received signals to produce time series data (i.e., a delayed-time representation data set) representative of the electrical signals being received form their associated arrays  26  and  30 . 
         [0039]    At operation  108  the DSP subsystem  38  generates a pair of averaged time varying (beamformed time series) signals from the time series data provided at the outputs  34   a  and  36   a  of the two array processing subsystems  34  and  36 . At operation  110  the DSP subsystem  38  analyzes the two, time varying signals (i.e., the beamformed time series) to determine a correlation, if any, between the two averaged, time varying signals. At operation  112  the DSP subsystem  38  may determine the spatial extent within the noise measurement field  22  where the correlation exists as well as where, within the noise field, that the correlation is at a maximum. At operation  114  the correlation information may be displayed on the display system  40 . Various commercially available software systems, for example MATLAB® offered by Mathworks of Natick, Mass., may be used for this purpose. This process may then be repeated one or more times by selecting different groups of transducers to form either of the first or second arrays  26  and  30 . 
         [0040]    The system  10  can be further enhanced if a pressure transducer is positioned at or near the source region of interest (e.g., at a wing flap edge, near the lip of a jet engine inlet or near the inner edge of the fan/primary exhaust nozzle exits of a jet engine). This would allow for additional levels of correlation analyses in both static and flight test measurements. 
         [0041]    Referring to  FIG. 3 , a system  200  is shown for determining noise correlation but for a traveling mobile platform, in this example a flying jet aircraft  202 . It will be appreciated that the system  200  makes use of the array processing subsystems  34  and  36 , the DSP subsystem  38  and the display system  40 , although these subsystems are not illustrated in  FIG. 3 . In the system  200  three arrays  204 ,  206  and  208  are arranged contiguously on the ground  210  along a known flight path of aircraft  202 . Each array  204 - 208  may be formed by strategically positioned acoustic transducers  204   a,    206   a  and  208   a,  respectively, such that each array forms a multi-arm log spiral phased array antenna or any such distribution of transducers appropriate for the article being tested. The arrays  204 - 208  provide the beamformed time series data necessary for determining the correlation, if any, between the noise signals radiating from and associated with the aircraft and measured by the arrays  206 - 208 . 
         [0042]    The configuration of the arrays  204 - 208  would provide the added benefit of obtaining multiple measurements of flight test aircraft noise for statistical analysis as opposed to a single measurement from arrays having co-located phase centers, since multiple, spatially separated (i.e., statistically independent) measurements would be acquired. In this embodiment the system  200  can also be used to determine array aperture size effects on the outputs of the arrays  204 - 208 . Each of the arrays  204 - 208  may be further decomposed into smaller sets of subarrays. Correlations of the output between these additional subarrays of, for example, array  206   a,  can be used to study the effects of acoustic wave decorrelation across the arrays as the aperture size is varied. 
         [0043]    It will also be appreciated that it is possible to create a “dome” or “sphere” surrounding the noise source, with transducers (i.e., microphones) “peppering” the inner surface so that correlations can be measured between any two array locations on the sphere. 
         [0044]    The system  10  and method of the present disclosure thus enables noise correlation information to be obtained from a single or from spatially separated and/or distributed noise sources. A significant advantage of the present system  10  and method is that extraneous noise is filtered from the beamformed output time series from the array processing subsystems  34  and  36 . This enables a correlation between noise signals from spatially separated sensors to be much more easily detected and analyzed by the DSP subsystem  38 . 
         [0045]    The present disclosure is inclusive of frequency domain beamforming/beam-steering and array signal processing methods for providing the averaged time varying signals from the first and second arrays. Mutual correlations among three (or more) arrays can be determined using polyspectral methods. 
         [0046]    While various embodiments have been described, those skilled in the art will recognize modifications or variations which might be made without departing from the present disclosure. The examples illustrate the various embodiments and are not intended to limit the present disclosure. Therefore, the description and claims should be interpreted liberally with only such limitation as is necessary in view of the pertinent prior art.

Technology Classification (CPC): 7