Method and arrangement for detecting, localizing and classifying defects of a device under test

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.

FIELD OF THE INVENTION

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

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 ri. The measured signal p(t,ri) is given by:
p(t,ri)=plin(t,ri)preg(t)+prb(t)+pstoch(t)+pn(t)  (1)
This equation comprises a linear component plin(t,ri) which is coherent with the input signal u(t), and a regular distortion component preg(t,ri), an irregular deterministic distortion component prb(t,ri) and a stochastic component pstoch(t,ri) which are incoherent with the input signal u(t). For example, the regular distortion component preg(t,ri) is generated by motor and suspension nonlinearities inherent in loudspeakers. The irregular deterministic distortion component prb(t,ri) 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 pstoch(t,ri) is generated by loose particles, a rubbing coil and by turbulent air flow generated in enclosure leaks. The measured signal p(t,ri) is also corrupted by ambient noise pn(t) generated in the production environment.

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 preg(t,ri) from irregular deterministic distortion component prb(t,ri).

The stochastic distortion component pstoch(t,ri) 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 (CO2) 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.

Those methods developed for defect diagnostics and quality control generate an invalid result if the ambient noise pn(t) becomes dominant in the measured signal p(t,ri). In the Japanese patent application JP 61191868, N. Tomoyasu suggested the use of a second microphone which measures the sound pressure p(t,rn) of the ambient noise source. If this sound pressure p(t,rn) exceeds a predefined level, the signal p(t,ri) 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 120thConvention 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,rn) 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 rnbe 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 rn, of the ambient noise source is not known or the ambient noise source is moving.

All of the known techniques fail in the detection and separation of a defect when the ambient noise pn(t) is smaller than the linear measured signals plin(t,ri) but larger than the regular, stochastic or deterministic distortion component preg(t,ri), pstoch(t ri) and prb(t,ri), respectively.

OBJECTS OF THE INVENTION

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,ri). A further object is to use a minimum of hardware elements to keep the cost of the system low.

SUMMARY OF THE INVENTION

According to the present invention, the present diagnostic system monitors signals p(t,ri) at multiple measurement points ri(with 1≦i≦I) which are affected by defect sources q(t, rd,j) (with 1≦j≦J) of the device under test at position rd,jand by ambient noise sources q(t, rn,k) at position rn,k(with 1≦k≦K). In contrast to prior art, a source analyzer separates the signals emitted by the defect sources q(t, rd,j) and noise sources q(t, rn,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 plin(t,ri), 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,rd,j) and noise vector N(t,rn,k) which comprise deterministic components pdet(t,rd,j) and pdet(t,rn,k), stochastic components pstoch(t,rd,j) and pstoch(t, rn,k) and information about the position of each identified source τd,jand τn,kcorresponding 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 pdet(t,rd,j) and stochastic signal components pstoch(t,rd,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)≠T0, such as a sinusoidal sweep, the deterministic components are determined by transforming the measured signal to a constant period length T0and averaging the transformed signals in the phase space.

The orthogonal features in the defect vector D((t,rd,j) and noise vector N(t,rn,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,rd,j) may be corrupted by the noise source. In this case, the classificator detects invalid parts of the defect vector D((t,rd,j) automatically by comparing stochastic and deterministic components of the defect vector D((t,rd,j) and of the noise vector N(t,rn,k) with each other and/or with predefined thresholds. According to the invention, the valid parts of the defect vector D((t,rd,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,rd,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,rd,j) to an expert system which creates a knowledge base for the automatic classification of the defects.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1is a general block diagram showing an arrangement for diagnosing the operating state of a device under test system37in accordance with the invention, coping with an ambient noise source90emitting a noise signal q(t, rn,k) with k=1, which is superimposed with defect signal q(t,rd,j) with j=1.2 emitted by defects39,263on the device under test. The device under test37, which is, for example, a loudspeaker, has an input41which is provided with a stimulus u(t) generated by a generator43. At least two sensors45,47located at arbitrary positions r1, r2generate output signals p(t, ri) with i=1.2. Each signal p(t, ri) is supplied via a controllable highpass51,81as a filtered signal p′(t, ri) to inputs63,69of a source analyzer65. The source analyzer65generates at least one defect vector D(t,rd,j) at outputs259,257which corresponds with the defects39and263, and a noise vector N(t,rn,k) with k>1 at an output303which corresponds with the detected noise source90. All vector outputs259,257,303of the source analyzer65are connected to vector inputs269,271,272of a classificator273which provides information on the location of the source, and relevant features of the deterministic component pdet(t) and statistic component pstoch(t) which are relevant for diagnostics. The classificator273assesses the quality of the system, identifies the cause and location of the defect and gives those results via an output85to a display87. Auralization signals derived from the defect vectors are provided via an output86to a loudspeaker274to support a subjective evaluation of the defects by a human ear. A frequency detector280either receives the stimulus u(t) from generator43via an input283, or a measured signal p(t, ri) from sensor45via input285, and detects the instantaneous period length T(t) and frequency f(t) of the excitation signal and supplies this information via an output281to the control inputs275,83,57and53of the defect classificator273, source analyzer65and high-passes51,81, respectively.

FIG. 2is a block diagram showing an embodiment of the source analyzer65comprising a source estimator101, at least one defect analyzer93,94and at least one noise analyzer309. The source estimator101has two inputs103and106receiving the filtered signals p′(t,ri) with i>1 from inputs63,69, has at least one defect location output105,321and at least one noise location output319providing information describing the distance between the positions rd,j, rn,kof sources39,263,90and measurement positions riof the sensors45,47. This information is, for example, given by the transfer function:

Hd,j⁡(f)=rd,j-r2⁢exp⁡(j⁢⁢k⁢rd,j-r1)rd,j-r1⁢exp⁡(j⁢⁢k⁢rd,j-r2),(2)
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:

τd,j=rd,j-r1-rd,j-r2c0,(3)
or the attenuation ratio:

Dd,j=rd,j-r1rd,j-r2,(4)
using wavenumber k and the speed of sound c0.

The location outputs321,105,319are connected with the location inputs301,305,311of the corresponding defect analyzer94,93and noise analyzer309. Each of the analyzers94,93and309has got an output299,99,315providing vectors D(t,rd,j), N(t,rn) at the outputs259,257,303of the source analyzer65.

FIG. 3shows an embodiment of the source estimator101. The signal p′(t,r1) at the first input103is supplied to an input305of a stochastic correlator339and to an input147of a deterministic correlator151. The signal p′(t,r2) at the second input106is supplied via a controllable filter137to inputs341and145of correlators339and151, respectively. The outputs153,154of the correlators are supplied to the input159of a maximum detector157, which generates a variable control parameter (e.g. time delay τ) at a control output155supplied to the control input133of controllable filter137. The maximum detector157detects the values of the control parameter (e.g., time delay estimates τj) where the signals at outputs153,154have global or local maxima. The output of the maximum detector157is supplied to two comparators161and162which compare the identified parameter τjwith a predefined limit τTto classify them as local information τd,jand τd,kof defect and noise sources, respectively. The source identification, for example, exploits the relationship between the angle:

αd,j=arccos⁡(c0⁢τd,jr2-r1)(5)
of the incident wave emitted by the source at position rd,jand time delay τd,jand the distance between the two sensors.

FIG. 4shows an embodiment of the stochastic correlator339. The input signals p′(t,r1) and p′(t−τ, r1) at inputs305and341are transformed by pre-filters323and325into stochastic components p′stoch(t,r1) and p′stoch(t−τ, r1) supplied to the inputs143and141of a multiplier147. For a steady-state excitation signal with the period length T the filters323and325with the transfer function:

Hstoch⁡(jω)=∏k=1K⁢⁢(1-δ⁡(2⁢⁢π⁢⁢k/T-ω))(6)
attenuate all components which are multiples of the fundamental frequency f0=1/T.

The output145of the multiplier147, filtered by the post-filter158with the transfer function:

Hdet⁡(jω)=∏k=LK⁢δ⁢(2⁢⁢π⁢⁢k/T-ω),(7)
provides a demodulated squared envelope:
e(t)2=F−1{Hdet(jω)}*(p′stoch(ri,t)p′stoch(rj,t+τ))  (8)
of the modulated noise signal to the output154of the correlator339.

The block diagram inFIG. 4also describes the general structure of the deterministic correlator151. Contrary to the stochastic correlator, the pre-filters323and325enhance the deterministic components by having a transfer function Hdet(jω) according to Eq. (7), and the post-filter158selects the dc-component only.

FIG. 5shows an embodiment of defect analyzer93, but the general structure of this block diagram is also valid for the other analyzers94,309. The signal p′(t,r1) at input95is supplied to the input327of a deterministic signal processor351and the input435of a stochastic signal processor439. The signal p′(t,r2) at input307is connected via a correction filter343to the other inputs329and441of processors351and439, respectively. The correction filter343can be realized as a delay unit receiving a control signal τd,1via a control input347which generates a time delayed signal p′(t+τd,1, r2). The stochastic signal processor439can be realized by using the same embodiment as was used for the cross-correlator339shown inFIG. 4. The envelope signal e(t)2generated at output437is an important feature for the detection of modulated noise as generated by air leaks. The envelope signal e(t)2comprises the fundamental frequency fo=1/T supplied to the device under test via the stimulus u(t), as well as harmonics of f0. The signal-to-noise ratio of the detected envelope signal e(t)2can be increased by extending the measurement time. The outputs437,331of the stochastic and deterministic signal processors439and351, respectively, and the time delay control signal τd,1are summarized to the defect vector D(t,rd,1) at output99. The processors439and351have a control input453and333, respectively, which receive the instantaneous frequency f(t) or period length T via an input97, which is received from frequency detector280via input83of source analyzer65.

FIG. 6shows an embodiment of the deterministic signal processor351. The signals at the inputs327and329are supplied to an adder357, and the summed signal psum(t) at output359is averaged in the phase space according to the invention, to generate the deterministic component:

The averaging in the phase space requires a frequency converter367having an input361connected to adder output359; and the frequency converter transforms the summed signal psum(t) having a time varying period length T(t) to a signal p′sum(t) at output365having a constant frequency period length T0. The frequency converter may also consider an additional phase shift ∠Hlin(jω) generated by the linear transfer response between the stimulus u(t) at generator43and the defect source263. A conventional averager371having an input369connected with frequency converter output365generates the deterministic component at an output373, which is provided at processor output331.

FIG. 7shows an embodiment of defect classificator273. The defect vectors D(t, rd,j) and noise vectors N(t,rn,k) received at inputs269,271,272are supplied to the inputs261,255,253of an ambient noise remover251generating valid defect vectors D′(t, rd,j) at outputs267,265which are not corrupted by the ambient noise source90. Those outputs are connected to the inputs213and217of a comparator215which 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 test37, which is supplied to the classificator output85. The classificator contains also a defect identifier205, realized with fuzzy logic and having multiple inputs203,405,209connected with outputs267,265and219. The defect identifier205receives information about the physical cause of the defect via an input206, and generates an internal knowledge base which is used for the automatic classification. The results of the classification, provided at an output207, are also supplied to output85. The valid defect vectors D′(t, rd,j) are also supplied to the inputs417,421of a selector411which selects the deterministic component ddet(t,rd,j) or stochastic component dstoch(t,rd,j) of the dominant defect at position rd,jby exploiting the data received from fuzzy logic output207and comparator output219provided via inputs415and419, respectively. The selected signal at output413is transformed via a frequency converter407and provided at output86connected to a loudspeaker. The frequency converter407transforms 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.

FIG. 8shows an embodiment of the ambient noise remover251. The noise vector N(t,rn,k) is received at input253and is supplied to the input397of a noise detector391. A comparator235compares the elements of the noise vector N(t,rn,k) with a predefined threshold Ti, such that the comparator's output indicates a possible noise corruption. The noise detector391also contains a second comparator399which compares the defect vector D(t,rd,j) received via an input393with the noise vector N(t,rn,k). The output of comparator399, which indicates that the defect vector exceeds the noise vector, is combined with output of the comparator235in401and supplied via output395to the control inputs381,382of accumulators375and387, respectively. Each accumulator387,375has an input389,379receiving the defect vector D(t,rd,j) from inputs261and255, respectively. The accumulators387,375only store the valid parts of the defect vectors D(t,rd,j) by using the control signals at inputs381,382, and provide a valid defect vector D′(t,rd,j) a to outputs265,267if the data are complete.

FIG. 9shows an embodiment of accumulator375. The defect vector D(t, rd,j) at input379is distributed via switch189to the inputs193,194and197of a memory195according to the instantaneous frequency f received from the frequency detector280via an input377, input287of ambient noise remover251, and input275of defect classificator273. The memory stores the input data if the control signal at a control input381indicates valid data which are not corrupted by ambient noise. If all elements of the memory195contain data, the valid defect vector D′(t, rd,j) is supplied to an output383.