Abstract:
An arrangement and method for assessing and diagnosing the operating state of a device under test in the presence of a disturbing ambient noise and for detecting, localizing and classifying defects of the device which affect its operational reliability and quality. At least two sensors monitor signals at arbitrary locations which are affected by signals emitted by defects and by ambient noise sources. A source analyzer receives the monitored signals, identifies the number and location of the sources, separates defect and noise sources, and analyzes the deterministic and stochastic signal components emitted by each source. Defect and noise vectors at the outputs of the source analyzer are supplied to a defect classificator which detects invalid parts of the measurements corrupted by ambient noise, accumulates the valid parts, assesses the quality of the system under test and identifies the physical causes and location of the defects.

Description:
FIELD OF THE INVENTION 
       [0001]    The invention generally relates to an arrangement and a method for assessing and diagnosing the operating state of a device under test in the presence of ambient noise, and for detecting, localizing and classifying defects of the device which affect its operational reliability and quality. The arrangement is useful with electrical, mechanical or other systems having an input which receives an excitation signal; transducers (such as loudspeakers) are a primary application. 
       DESCRIPTION OF THE RELATED ART 
       [0002]    A device under test (e.g., a loudspeaker) is excited by a stimulus u(t), and the state of the system or the output signal (e.g., the sound pressure p) is measured at a particular location r i . The measured signal p(t,r i ) is given by: 
         [0000]        p ( t,r   i )= p   lin ( t,r   i ) p   reg ( t )+ p   rb ( t )+ p   stoch ( t )+ p   n ( t )  (1)
 
         [0000]    This equation comprises a linear component p lin (t,r i ) which is coherent with the input signal u(t), and a regular distortion component p reg (t,r i ), an irregular deterministic distortion component p rb (t,r i ) and a stochastic component p stoch (t,r i ) which are incoherent with the input signal u(t). For example, the regular distortion component p reg (t,r i ) is generated by motor and suspension nonlinearities inherent in loudspeakers. The irregular deterministic distortion component p rb (t,r i ) is generated by loudspeaker defects which are directly coupled with mechanical vibration such as hard limiting of the mechanical suspension system, beating of the wire at the diaphragm and buzzing parts. The stochastic distortion component p stoch (t,r i ) is generated by loose particles, a rubbing coil and by turbulent air flow generated in enclosure leaks. The measured signal p(t,r i ) is also corrupted by ambient noise p n (t) generated in the production environment. 
         [0003]    Many defect detection techniques are known. For example, Zaschel shows in European patent EU 413 845 that the separation of deterministic and stochastic components is beneficial for the early identification of defects. Klippel suggested an adaptive filter in German patent DE 102 14407 to separate the regular distortion component p reg (t,r i ) from irregular deterministic distortion component p rb (t,r i ). 
         [0004]    The stochastic distortion component p stoch (t,r i ) generates a dense amplitude spectrum which goes up to ultra-sonic frequencies. G. Moshier exploits this property for leak detection in U.S. Pat. No. 4,096,736. H. Yonak suggests a photo-acoustic leak detection and localization system and method based on photo-acoustic sound emission initiated by a carbon dioxide (CO 2 ) laser in U.S. Pat. No. 6,227,036. A microphone array technique is suggested by Greene in U.S. Pat. No. 5,533,383 for detecting acoustic leaks. 
         [0005]    Those methods developed for defect diagnostics and quality control generate an invalid result if the ambient noise p n (t) becomes dominant in the measured signal p(t,r i ). In the Japanese patent application JP 61191868, N. Tomoyasu suggested the use of a second microphone which measures the sound pressure p(t,r n ) of the ambient noise source. If this sound pressure p(t,r n ) exceeds a predefined level, the signal p(t,r i ) measured at the device under test is not reliable and may be corrupted by noise. In “Loudspeaker Testing at the Production Line, Proceedings of the 120 th  Convention of the Audio Eng. Soc.”, Paris (France) September 2006, Klippel et al. suggested that a corrupted measurement should be repeated until the ambient noise p(t,r n ) is below the allowed limit. This technique increases the measurement time significantly, and a valid measurement cannot be assured within a given production cycle time. The technique also requires that the ambient noise source r n  be far away from the device under test, and the second noise microphone should be placed closer to the ambient noise source than the measurement microphone. However, in many practical applications the position r n , of the ambient noise source is not known or the ambient noise source is moving. 
         [0006]    All of the known techniques fail in the detection and separation of a defect when the ambient noise p n (t) is smaller than the linear measured signals p lin (t,r i ) but larger than the regular, stochastic or deterministic distortion component p reg (t,r i ), p stoch  (t r i ) and p rb (t,r i ), respectively. 
       OBJECTS OF THE INVENTION 
       [0007]    Thus, there is a need for a diagnostic system which detects defects of devices under test, identifies their physical causes and localizes the positions of the defects. This measurement should be performed with high accuracy within a short time while the device under test is operated in a normal (production) environment and ambient noise emitted by unknown sources may affect the measured signal p(t,r i ). A further object is to use a minimum of hardware elements to keep the cost of the system low. 
       SUMMARY OF THE INVENTION 
       [0008]    According to the present invention, the present diagnostic system monitors signals p(t,r i ) at multiple measurement points r i  (with 1≦i≦I) which are affected by defect sources q(t, r d,j ) (with 1≦j≦J) of the device under test at position r d,j  and by ambient noise sources q(t, r n,k ) at position r n,k  (with 1≦k≦K). In contrast to prior art, a source analyzer separates the signals emitted by the defect sources q(t, r d,j ) and noise sources q(t, r n,k ) by combining spatial analysis and signal analysis to exploit information about the location of the sources and properties of stochastic and deterministic distortion components emitted by the sources. The linear part p lin (t,r i ), which is coherent with the stimulus u(t) may be suppressed by filtering because this part contains no significant clues about some defects of the device under test. The spatial analysis performed by the source analyzer includes the identification of the number of sources, the classification into defect and noise sources and localization of the sources. The source analyzer generates defect vectors D((t,r d,j ) and noise vector N(t,r n,k ) which comprise deterministic components p det (t,r d,j ) and p det (t,r n,k ), stochastic components n r and p stoch (t, r n,k ) and information about the position of each identified source τ d,j  and τ n,k  corresponding with the separated defect and noise sources, respectively. The signal analysis applied to the separated source signals increases the sensitivity of the diagnostic system to defects of a device under test which have less energy and similar spectral properties as ambient noise. The separation of the deterministic components p det (t,r d,j ) and stochastic signal components p stoch (t,r d,j ) allows the system to perform an averaging of properties of incoherent signals. Thus, a novel demodulation technique provides the envelope of modulated stochastic signals as generated by air leaks, and the direction of the source. The signal-to-noise ratio can be improved by increasing the measurement time and averaging the envelope signal over an increased number of periods. Using a periodic stimulus with a time varying period length T(t)≠T 0 , such as a sinusoidal sweep, the deterministic components are determined by transforming the measured signal to a constant period length T 0  and averaging the transformed signals in the phase space. 
         [0009]    The orthogonal features in the defect vector D((t,r d,j ) and noise vector N(t,r n,k ) are transferred to a defect classificator which determines the quality of the device under test and identifies the physical causes of the defects. The system stays operative if the positions of the sensors, defect and noise sources change. Contrary to known beam steering techniques, the system requires a low number of sensors and can remain operative with only two sensors. The angle of the incident wave can be detected with sufficient accuracy because the deterministic and stochastic signal components emitted by the defects comprise many spectral components which cover a wide frequency band and which are incoherent with the stimulus. However, an array comprising only two sensors has a low directivity characteristic and cannot separate the defect and noise sources completely, and the measured defect vector D((t,r d,j ) may be corrupted by the noise source. In this case, the classificator detects invalid parts of the defect vector D((t,r d,j ) automatically by comparing stochastic and deterministic components of the defect vector D((t,r d,j ) and of the noise vector N(t,r n,k ) with each other and/or with predefined thresholds. According to the invention, the valid parts of the defect vector D((t,r d,j ) are stored in an accumulator and are merged with valid parts from repeated measurements using the same stimulus, eventually giving a complete valid data set. Since most of the ambient noise is a random signal, the accumulation of valid data gives full noise immunity while keeping the measurement time much shorter than traditional techniques using extensive averaging. The diagnostic system transforms the analyzed data in the defect vector D((t,r d,j ) into a lower frequency range where the symptoms of the defects can be analyzed more easily by a human ear. This auralization technique improves subjective assessment of the defect by a human expert and gives clues for finding the physical cause of the defect. The results of the subjective classification may be provided together with the objective data in the defect vector D((t,r d,j ) to an expert system which creates a knowledge base for the automatic classification of the defects. 
         [0010]    These and other features, aspects and advantages of the present invention will become better understood with reference to the following drawings, description and claims. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0011]      FIG. 1  is a general block diagram showing an arrangement for diagnosing the operating state of a device under test in accordance with the present invention. 
           [0012]      FIG. 2  shows an embodiment of a source analyzer using two sensors as might be used with a diagnostic system in accordance with the present invention. 
           [0013]      FIG. 3  shows an embodiment of a source estimator as might be used with a diagnostic system in accordance with the present invention. 
           [0014]      FIG. 4  shows an embodiment of a cross-correlator as might be used with a diagnostic system in accordance with the present invention. 
           [0015]      FIG. 5  shows an embodiment of a defect analyzer as might be used with a diagnostic system in accordance with the present invention. 
           [0016]      FIG. 6  shows an embodiment of a deterministic signal processor as might be used with a diagnostic system in accordance with the present invention. 
           [0017]      FIG. 7  shows an embodiment of a classificator as might be used with a diagnostic system in accordance with the present invention. 
           [0018]      FIG. 8  shows an embodiment of a noise remover as might be used with a diagnostic system in accordance with the present invention. 
           [0019]      FIG. 9  shows an embodiment of an accumulator as might be used with a diagnostic system in accordance with the present invention. 
       
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       [0020]      FIG. 1  is a general block diagram showing an arrangement for diagnosing the operating state of a device under test system  37  in accordance with the invention, coping with an ambient noise source  90  emitting a noise signal q(t, r n,k ) with k=1, which is superimposed with defect signal q(t,r d,j ) with j=1.2 emitted by defects  39 ,  263  on the device under test. The device under test  37 , which is, for example, a loudspeaker, has an input  41  which is provided with a stimulus u(t) generated by a generator  43 . At least two sensors  45 ,  47  located at arbitrary positions r 1 , r 2  generate output signals p(t, r i ) with i=1.2. Each signal p(t, r i ) is supplied via a controllable highpass  51 ,  81  as a filtered signal p′(t, r i ) to inputs  63 ,  69  of a source analyzer  65 . The source analyzer  65  generates at least one defect vector D(t,r d,j ) at outputs  259 ,  257  which corresponds with the defects  39  and  263 , and a noise vector N(t,r n,k ) with k&gt;1 at an output  303  which corresponds with the detected noise source  90 . All vector outputs  259 ,  257 ,  303  of the source analyzer  65  are connected to vector inputs  269 ,  271 ,  272  of a classificator  273  which provides information on the location of the source, and relevant features of the deterministic component p det (t) and statistic component p stoch (t) which are relevant for diagnostics. The classificator  273  assesses the quality of the system, identifies the cause and location of the defect and gives those results via an output  85  to a display  87 . Auralization signals derived from the defect vectors are provided via an output  86  to a loudspeaker  274  to support a subjective evaluation of the defects by a human ear. A frequency detector  280  either receives the stimulus u(t) from generator  43  via an input  283 , or a measured signal p(t, r i ) from sensor  45  via input  285 , and detects the instantaneous period length T(t) and frequency f(t) of the excitation signal and supplies this information via an output  281  to the control inputs  275 ,  83 ,  57  and  53  of the defect classificator  273 , source analyzer  65  and high-passes  51 ,  81 , respectively. 
         [0021]      FIG. 2  is a block diagram showing an embodiment of the source analyzer  65  comprising a source estimator  101 , at least one defect analyzer  93 ,  94  and at least one noise analyzer  309 . The source estimator  101  has two inputs  103  and  106  receiving the filtered signals p′(t,r i ) with i&gt;1 from inputs  63 ,  69 , has at least one defect location output  105 ,  321  and at least one noise location output  319  providing information describing the distance between the positions r d,j , r n,k  of sources  39 ,  263 ,  90  and measurement positions r i  of the sensors  45 ,  47 . This information is, for example, given by the transfer function: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
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         [0000]    assuming free field propagation between the sources and the sensors. In practice, it is completely sufficient to identify the difference in the time delay as follows: 
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         [0000]    or the attenuation ratio: 
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         [0000]    using wavenumber k and the speed of sound c 0 . 
         [0022]    The location outputs  321 ,  105 ,  319  are connected with the location inputs  301 ,  305 ,  311  of the corresponding defect analyzer  94 ,  93  and noise analyzer  309 . Each of the analyzers  94 ,  93  and  309  has got an output  299 ,  99 ,  315  providing vectors D(t,r d,j ), N(t,r n ) at the outputs  259 ,  257 ,  303  of the source analyzer  65 . 
         [0023]      FIG. 3  shows an embodiment of the source estimator  101 . The signal p′(t,r 1 ) at the first input  103  is supplied to an input  305  of a stochastic correlator  339  and to an input  147  of a deterministic correlator  151 . The signal p′(t,r 2 ) at the second input  106  is supplied via a controllable filter  137  to inputs  341  and  145  of correlators  339  and  151 , respectively. The outputs  153 ,  154  of the correlators are supplied to the input  159  of a maximum detector  157 , which generates a variable control parameter (e.g. time delay τ) at a control output  155  supplied to the control input  133  of controllable filter  137 . The maximum detector  157  detects the values of the control parameter (e.g., time delay estimates τ j ) where the signals at outputs  153 ,  154  have global or local maxima. The output of the maximum detector  157  is supplied to two comparators  161  and  162  which compare the identified parameter τ j  with a predefined limit τ T  to classify them as local information τ d,j  and τ d,k  of defect and noise sources, respectively. The source identification, for example, exploits the relationship between the angle: 
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         [0000]    of the incident wave emitted by the source at position r d,j  and time delay τ d,j  and the distance between the two sensors. 
         [0024]      FIG. 4  shows an embodiment of the stochastic correlator  339 . The input signals p′(t,r 1 ) and p′(t−τ, r 1 ) at inputs  305  and  341  are transformed by pre-filters  323  and  325  into stochastic components p′ stoch (t,r 1 ) and p′ stoch (t−τ, r 1 ) supplied to the inputs  143  and  141  of a multiplier  147 . For a steady-state excitation signal with the period length T the filters  323  and  325  with the transfer function: 
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         [0000]    attenuate all components which are multiples of the fundamental frequency f 0 =1/T. 
         [0025]    The output  145  of the multiplier  147 , filtered by the post-filter  158  with the transfer function: 
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         [0000]    provides a demodulated squared envelope: 
         [0000]        e ( t ) 2   =F   −1   {H   det ( jω )}*( p′   stoch ( r   i   ,t ) p′   stoch ( r   j   ,t+τ ))  (8)
 
         [0000]    of the modulated noise signal to the output  154  of the correlator  339 . 
         [0026]    The block diagram in  FIG. 4  also describes the general structure of the deterministic correlator  151 . Contrary to the stochastic correlator, the pre-filters  323  and  325  enhance the deterministic components by having a transfer function H det (jω) according to Eq. (7), and the post-filter  158  selects the dc-component only. 
         [0027]      FIG. 5  shows an embodiment of defect analyzer  93 , but the general structure of this block diagram is also valid for the other analyzers  94 ,  309 . The signal p′(t,r 1 ) at input  95  is supplied to the input  327  of a deterministic signal processor  351  and the input  435  of a stochastic signal processor  439 . The signal p′(t,r 2 ) at input  307  is connected via a correction filter  343  to the other inputs  329  and  441  of processors  351  and  439 , respectively. The correction filter  343  can be realized as a delay unit receiving a control signal τ d,1  via a control input  347  which generates a time delayed signal p′(t+τ d,1 , r 2 ). The stochastic signal processor  439  can be realized by using the same embodiment as was used for the cross-correlator  339  shown in  FIG. 4 . The envelope signal e(t) 2  generated at output  437  is an important feature for the detection of modulated noise as generated by air leaks. The envelope signal e(t) 2  comprises the fundamental frequency f o =1/T supplied to the device under test via the stimulus u(t), as well as harmonics of f 0 . The signal-to-noise ratio of the detected envelope signal e(t) 2  can be increased by extending the measurement time. The outputs  437 ,  331  of the stochastic and deterministic signal processors  439  and  351 , respectively, and the time delay control signal τ d,1  are summarized to the defect vector D(t,r d,1 ) at output  99 . The processors  439  and  351  have a control input  453  and  333 , respectively, which receive the instantaneous frequency f(t) or period length T via an input  97 , which is received from frequency detector  280  via input  83  of source analyzer  65 . 
         [0028]      FIG. 6  shows an embodiment of the deterministic signal processor  351 . The signals at the inputs  327  and  329  are supplied to an adder  357 , and the summed signal p sum (t) at output  359  is averaged in the phase space according to the invention, to generate the deterministic component: 
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                   ) 
                 
               
             
           
         
       
     
         [0029]    The averaging in the phase space requires a frequency converter  367  having an input  361  connected to adder output  359 ; and the frequency converter transforms the summed signal p sum (t) having a time varying period length T(t) to a signal p′ sum (t) at output  365  having a constant frequency period length T 0 . The frequency converter may also consider an additional phase shift ∠H lin (jω) generated by the linear transfer response between the stimulus u(t) at generator  43  and the defect source  263 . A conventional averager  371  having an input  369  connected with frequency converter output  365  generates the deterministic component at an output  373 , which is provided at processor output  331 . 
         [0030]      FIG. 7  shows an embodiment of defect classificator  273 . The defect vectors D(t, r d,j ) and noise vectors N(t,r n,k ) received at inputs  269 ,  271 ,  272  are supplied to the inputs  261 ,  255 ,  253  of an ambient noise remover  251  generating valid defect vectors D′(t, r d,j ) at outputs  267 ,  265  which are not corrupted by the ambient noise source  90 . Those outputs are connected to the inputs  213  and  217  of a comparator  215  which compares the properties of the deterministic and stochastic signal components with predefined thresholds and generates a quality assessment (grading or a pass/fail decision) for device under test  37 , which is supplied to the classificator output  85 . The classificator contains also a defect identifier  205 , realized with fuzzy logic and having multiple inputs  203 ,  405 ,  209  connected with outputs  267 ,  265  and  219 . The defect identifier  205  receives information about the physical cause of the defect via an input  206 , and generates an internal knowledge base which is used for the automatic classification. The results of the classification, provided at an output  207 , are also supplied to output  85 . The valid defect vectors D′(t, r d,j ) are also supplied to the inputs  417 ,  421  of a selector  411  which selects the deterministic component d det (t,r d,j ) or stochastic component d stoch (t,r d,j ) of the dominant defect at position r d,j  by exploiting the data received from fuzzy logic output  207  and comparator output  219  provided via inputs  415  and  419 , respectively. The selected signal at output  413  is transformed via a frequency converter  407  and provided at output  86  connected to a loudspeaker. The frequency converter  407  transforms the high frequency content to a lower frequency band where the spectral and temporal properties of the defects can be analyzed more easily by a human ear. 
         [0031]      FIG. 8  shows an embodiment of the ambient noise remover  251 . The noise vector N(t,r n,k ) is received at input  253  and is supplied to the input  397  of a noise detector  391 . A comparator  235  compares the elements of the noise vector N(t,r n,k ) with a predefined threshold T i , such that the comparator&#39;s output indicates a possible noise corruption. The noise detector  391  also contains a second comparator  399  which compares the defect vector D(t, r d,j ) received via an input  393  with the noise vector N(t,r n,k ). The output of comparator  399 , which indicates that the defect vector exceeds the noise vector, is combined with output of the comparator  235  in  401  and supplied via output  395  to the control inputs  381 ,  382  of accumulators  375  and  387 , respectively. Each accumulator  387 ,  375  has an input  389 ,  379  receiving the defect vector D(t, r d,j ) from inputs  261  and  255 , respectively. The accumulators  387 ,  375  only store the valid parts of the defect vectors D(t, r d,j ) by using the control signals at inputs  381 ,  382 , and provide a valid defect vector D(t, r d,j ) to outputs  265 ,  267  if the data are complete. 
         [0032]      FIG. 9  shows an embodiment of accumulator  375 . The defect vector D(t, r d,j ) at input  379  is distributed via switch  189  to the inputs  193 ,  194  and  197  of a memory  195  according to the instantaneous frequency f received from the frequency detector  280  via an input  377 , input  287  of ambient noise remover  251 , and input  275  of defect classificator  273 . The memory stores the input data if the control signal at a control input  381  indicates valid data which are not corrupted by ambient noise. If all elements of the memory  195  contain data, the valid defect vector D′(t, r d,j ) is supplied to an output  383 .