A method and system for monitoring the operating status of an audio transformer

Abstract

The present invention relates to the technical field of electrical variable measurement, and specifically to a method and system for monitoring the operating status of an audio transformer, including: drawing a voltage signal curve; dividing the voltage signal curve into several time periods; selecting the time period closest to the ideal operating state as a reference period, and obtaining the reference difference degree of each time period according to the similarity between each time period and the reference period; obtaining the voltage anomaly factor of each time period according to the difference between each time period and the adjacent time period and the stability of the audio data of each time period; obtaining the degree to which the voltage of each time period is affected by external factors, and combining the reference difference degree and the voltage anomaly factor to obtain a fault evaluation, and realize the monitoring of the operating status of the audio transformer. The present invention obtains the accurate probability of the audio transformer having an operating abnormality, and improves the accuracy of the monitoring of the operating status of the audio transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical variable measurement, and in particular to a method and system for monitoring the operating status of an audio transformer. Background Art

[0002] The audio transformer is an important component of the audio system. Through functions such as signal transformation, isolation and impedance matching, it effectively supports the normal operation and signal processing requirements of various audio devices. During the operation of the audio transformer, abnormal conditions such as overload, short circuit and overheating may occur. Therefore, it is necessary to monitor the operation status of the audio transformer in real time by collecting parameters such as voltage during the operation of the audio transformer. Once an abnormality is found, the system will immediately send out an alarm signal to remind the staff to deal with it in time to prevent the fault from expanding and ensure the normal operation of the audio transformer.

[0003] The operating status of the audio transformer is monitored based on the voltage signal. Due to energy loss during normal operation and other reasons, the voltage signal of actual normal operation is not a standard sine wave, but has some deviations. Therefore, when the standard sine wave is used as a reference for the voltage signal under normal operating conditions, errors will occur in the operating status monitoring. When the load of the circuit where the audio transformer is located changes, the voltage signal of the audio transformer will also fluctuate briefly, which may lead to misjudgment of abnormal operating status, affecting the accuracy of the audio transformer operating status monitoring. Summary of the invention

[0004] The present invention provides a method and system for monitoring the operating state of an audio transformer, so as to solve the problem that misjudgment occurs when monitoring the operating state of an audio transformer based on a voltage signal, thus affecting the accuracy of monitoring the operating state of the audio transformer.

[0005] The present invention provides an audio transformer operating status monitoring method and system using the following technical solutions:

[0006] An embodiment of the present invention provides a method for monitoring the operating status of an audio transformer, the method comprising the following steps:

[0007] Obtain the voltage data at all times to form a voltage signal curve, as well as the sound frequency at each moment;

[0008] The voltage signal curve is divided into several time periods according to the signal cycle; a standard sine wave segment with a length of one cycle is recorded as a standard segment, and the standard difference degree and the benchmark difference degree of each time period are obtained according to the similarity between each time period and the standard segment; the voltage abnormality factor of each time period is obtained according to the benchmark difference degree of adjacent time periods of each time period and the stability of the audio data of each time period;

[0009] Decompose the voltage signal curve to obtain several components; obtain the environmental stability of each time period based on the distribution of voltage data of each component in each time period; obtain the fault evaluation of each time period by combining the environmental stability of each time period with the benchmark difference and the voltage anomaly factor;

[0010] Based on the fault evaluation in each time period, the operating status of the audio transformer is monitored.

[0011] Furthermore, dividing the voltage signal curve into several time periods according to the signal cycle includes:

[0012] Perform fast Fourier transform on the voltage signal curve to obtain the frequency spectrum of the voltage signal curve, take the inverse of the frequency of the peak with the largest amplitude in the frequency spectrum as the signal period, and divide the voltage signal curve into several time periods with the signal period as the interval.

[0013] Furthermore, obtaining the standard difference degree and the benchmark difference degree for each time period includes:

[0014] All voltage data of the standard segment constitute a standard voltage sequence, and the voltage data of each time period constitutes a voltage sequence of each time period. The DTW distance between the voltage sequence of each time period and the standard voltage sequence is obtained, which is recorded as the DTW distance between each time period and the standard segment.

[0015] The calculation method of the waveform difference between the i-th time period and the standard period is:

[0016]

[0017] Where: B i Indicates the degree of difference between the waveform of the i-th time period and the standard period, D i represents the DTW distance between the i-th time period and the standard period, N represents the number of characteristic points of the voltage signal curve in the i-th time period, n represents the n-th characteristic point, and C i,n represents the slope of the tangent line at the nth feature point in the i-th time period, C i ′ ,n It represents the slope of the tangent line of the corresponding point of the nth feature point in the i-th time period in the standard segment; Sigmoid() represents the Sigmoid function, and || represents the absolute value function;

[0018] The waveform difference degree between all time periods and the standard segment is obtained, and the time period with the minimum value of the waveform difference degree between all time periods and the standard segment is recorded as the reference segment; the waveform difference degree between each time period and the reference segment is obtained, and the calculation method of the waveform difference degree between each time period and the reference segment is the same as the calculation method of the waveform difference degree between each time period and the standard segment; the waveform difference degree between each time period and the standard segment is recorded as the standard difference degree, and the waveform difference degree between each time period and the reference segment is recorded as the reference difference degree.

[0019] Furthermore, the specific method for obtaining the feature points is:

[0020] All extreme points and zero crossing points of the standard segment and all time periods are recorded as feature points.

[0021] Furthermore, the specific method for obtaining the corresponding points is:

[0022] In the standard segment, the feature point with the shortest time distance to the corresponding time of each feature point in each time period is recorded as the corresponding point of each feature point in each time period in the standard segment.

[0023] Furthermore, obtaining the voltage anomaly factor in each time period includes:

[0024] The voltage abnormality factor Y in the i-th time period in the voltage signal curve i The calculation method is:

[0025] Y i =(ΔZ i,i-1 +ΔZ i,i+1 )×V i

[0026] Where: ΔZ i,i-1 Indicates the absolute value of the difference between the benchmark difference between the i-th time period and the previous i-th time period, ΔZ i,i+1 V represents the absolute value of the difference between the benchmark difference between the i-th time period and the next i-th time period. i Represents the variance of all sound frequencies in the i-th time period.

[0027] Furthermore, obtaining the environmental stability level in each time period includes:

[0028] The environmental stability level S in the i-th time period i The calculation method is:

[0029]

[0030] Where: M represents the number of IMF components, d i,mIt represents the variance of the voltage data of the mth IMF component in the i-th time period; exp[] represents an exponential function with a natural constant as the base.

[0031] Further, obtaining the fault evaluation in each time period includes:

[0032] The product of the environmental stability degree of the i-th time period and the benchmark difference degree of the i-th time period is used as the confidence coefficient of the voltage anomaly factor of the i-th time period, and the product of the confidence coefficient and the voltage anomaly factor of the i-th time period is normalized by the Sigmoid function and recorded as the fault evaluation of the i-th time period.

[0033] Furthermore, the operation status monitoring of the audio transformer is realized according to the fault evaluation in each time period, including:

[0034] A fault threshold is preset. When there is a time period in the voltage signal curve where the fault evaluation is greater than the fault threshold, a fault warning is issued to monitor the operating status of the audio transformer.

[0035] A system for monitoring the operating status of an audio transformer comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a method for monitoring the operating status of an audio transformer are implemented.

[0036] The beneficial effects of the technical solution of the present invention are as follows: the period of the voltage signal curve is obtained, and the voltage signal curve is divided into several time periods according to the period. Since the voltage signal curve has a certain periodicity, analyzing the voltage signal curve in units of period can more accurately reflect the characteristics of the voltage signal curve, and the stability of the operating state of the audio transformer can be reflected by the similarity between different periods; the voltage signal waveform under normal operating conditions is reflected by taking the time period most similar to the waveform of the standard sine wave band as the reference section. Compared with directly taking the standard sine wave band as the reference section, this method is more in line with the actual operating conditions and can more accurately reflect the voltage abnormality of each time period; The difference between the interval segment and the reference segment reflects the degree of deviation of the operating status of each time period from the normal operating status; combining the difference between each time period and the reference segment and the stability of the sound frequency, the voltage anomaly factor of each time period is obtained to reflect the abnormal degree of voltage in each time period; the voltage signal curve is decomposed into several components, and the degree to which the voltage anomaly is affected by external factors rather than the operating status is analyzed under different components, so as to adjust the voltage anomaly factor to obtain fault evaluation. The fault evaluation reduces the impact of external factors on voltage anomalies, more accurately reflects the probability of abnormal operation of the audio transformer, can avoid misjudgment of some abnormal operating status, and improves the accuracy of audio transformer operating status monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 The present invention is a flowchart of the steps of a method for monitoring the operating status of an audio transformer. DETAILED DESCRIPTION

[0039] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the operating status monitoring method and system of an audio transformer proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0040] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0041] The specific scheme of the method and system for monitoring the operating status of an audio transformer provided by the present invention is described in detail below with reference to the accompanying drawings.

[0042] First, see Figure 1 , which shows a flow chart of a method for monitoring the operating status of an audio transformer provided by an embodiment of the present invention, the method comprising the following steps:

[0043] Step S001: Obtain voltage data at all times to form a voltage signal curve, and the sound frequency at each time.

[0044] The purpose of this embodiment is to monitor the operating status of the audio transformer through voltage data, so the voltage data is first obtained. Specifically, the collection interval and collection duration are preset, and the voltage sensor is used to obtain the voltage data and the sound frequency at each moment every collection interval as a moment; this embodiment is described by taking the collection interval of 1 second and the collection duration of 3 hours as an example, and other embodiments can be set to other values, which are not specifically limited in this embodiment.

[0045] Step S002: divide the voltage signal curve into several time periods according to the signal cycle; obtain the standard difference degree and the benchmark difference degree of each time period; and obtain the voltage abnormality factor of each time period.

[0046] It should be noted that the voltage signal curve is drawn based on the voltage data at all times. Since the voltage signal of the audio transformer is periodic during operation, the main frequency component of the voltage signal curve is shown as the frequency point with the largest amplitude in the spectrum diagram of the voltage signal curve, and the period of the voltage signal curve is obtained through the frequency point.

[0047] The voltage signal curve of the audio transformer in the ideal operating state is a standard sine waveform, but in actual conditions there will be a certain deviation from the standard sine waveform. The time period in the voltage signal curve that is most similar to the waveform of the standard sine wave is taken as the reference period, and the reference period is used to reflect the voltage performance of the audio transformer in the normal operating state. The similarity between each time period and the reference period is analyzed to reflect the difference between the waveform of each time period and the waveform under the normal operating state. The lower the similarity between each time period and the reference period, the more each time period deviates from the normal operating state, and the more likely it is that an operating abnormality will occur.

[0048] Specifically, with time as the horizontal axis and voltage as the vertical axis, a voltage signal curve is drawn according to voltage data at all times; a fast Fourier transform is performed on the voltage signal curve to obtain a frequency spectrum of the voltage signal curve, and the inverse of the frequency of the peak with the largest amplitude in the frequency spectrum is recorded as the signal period;

[0049] The voltage signal curve is divided into several time periods at intervals of the signal cycle; a standard sine wave segment with a length of one cycle is preset as a standard segment, and all extreme value points and zero crossing points of the standard segment and all time periods are recorded as characteristic points, wherein the extreme value points are the maximum value points and minimum value points of the voltage signal curve, and the zero crossing point is the point where the amplitude of the voltage signal curve is 0; in the standard segment, the characteristic point with the closest time distance to the corresponding time of each characteristic point of each time period is recorded as the corresponding point of each characteristic point of each time period in the standard segment;

[0050] All voltage data of the standard segment constitute a standard voltage sequence, and the voltage data of each time period constitutes a voltage sequence of each time period. The DTW distance between the voltage sequence of each time period and the standard voltage sequence is obtained, and recorded as the DTW distance between each time period and the standard segment. The DTW (Dynamic Time Warping) distance is a well-known technology and is not described in detail in this embodiment. The calculation method of the waveform difference between the i-th time period and the standard segment is:

[0051]

[0052] Where: B iIndicates the degree of difference between the waveform of the i-th time period and the standard period, D i represents the DTW distance between the i-th time period and the standard period, N represents the number of characteristic points of the voltage signal curve in the i-th time period, n represents the n-th characteristic point, and C i,n represents the slope of the tangent line of the nth feature point in the i-th time period, C i ′ ,n It represents the slope of the tangent line of the corresponding point of the nth feature point in the i-th time period in the standard segment; Sigmoid() represents the Sigmoid function, and || represents the absolute value function;

[0053] It should be noted that D i The DTW distance represented reflects the similarity measure between the i-th time period and the standard period. The larger the value, the smaller the similarity between the i-th time period and the standard period, and the greater the waveform difference. It represents the average value of the difference between the tangent slope at the corresponding characteristic point of the i-th time period and the standard period. The larger the value, the greater the waveform difference.

[0054] The waveform difference degree between all time periods and the standard segment is obtained, and the time period with the minimum value of the waveform difference degree between all time periods and the standard segment is recorded as the reference segment; the waveform difference degree between each time period and the reference segment is obtained, and the calculation method of the waveform difference degree between each time period and the reference segment is the same as the calculation method of the waveform difference degree between each time period and the standard segment; the waveform difference degree between each time period and the standard segment is recorded as the standard difference degree, and the waveform difference degree between each time period and the reference segment is recorded as the reference difference degree.

[0055] It should be further explained that when the operating voltage of the audio transformer is unstable, it will affect the output audio quality and may cause short-term high-frequency noise. Therefore, the stability of the audio output during the operation of the audio transformer is reflected according to the change in sound frequency. The more unstable the audio is, the more abnormal the voltage is. During normal operation, the waveform of each time period is similar. The difference between the benchmark difference degree of each time period and the benchmark difference degree of its adjacent time period reflects the waveform difference between each time period and its two adjacent time periods. The greater the waveform difference between each time period and its two adjacent time periods, the greater the voltage abnormality factor of each time period.

[0056] Specifically, the voltage anomaly factor Y in the i-th time period in the voltage signal curve is i The calculation method is:

[0057] Y i =(ΔZ i,i-1 +ΔZ i,i+1 )×V i

[0058] Where: ΔZ i,i-1 Indicates the absolute value of the difference between the benchmark difference between the i-th time period and the previous i-th time period, ΔZ i,i+1 V represents the absolute value of the difference between the benchmark difference between the i-th time period and the next i-th time period. i represents the variance of all sound frequencies in the i-th time period; in particular, when the i-th time period is the boundary, ΔZ i,i-1 or ΔZ i,i+1 Take 0;

[0059] It should be noted that (ΔZ i,i-1 +ΔZ i,i+1 ) value is larger, indicating that the waveform difference between the i-th time period and its two adjacent time periods is greater, and the voltage anomaly factor of the i-th time period is greater; V i The larger the value of is, the worse the stability of the audio output in the i-th time period is, and the larger the voltage abnormality factor in the i-th time period is;

[0060] Similarly, the voltage anomaly factor of each time period is obtained.

[0061] Step S003, decomposing the voltage signal curve to obtain several components; obtaining the environmental stability of each time period; and obtaining the fault evaluation of each time period.

[0062] It should be noted that, in addition to the fluctuation of the voltage signal due to the abnormal operation of the audio transformer, when the load of the circuit in which the audio transformer is located changes, the voltage signal of the audio transformer will also fluctuate briefly, resulting in an increase in the degree of abnormality in the time period near the moment when the load changes in the voltage signal curve, thereby leading to a misjudgment of the abnormal operation of the audio transformer;

[0063] The variances of several IMF components obtained by the EMD decomposition algorithm reflect whether there are short-term voltage fluctuations caused by circuit load changes. Since short-term voltage fluctuations caused by circuit load changes appear as high-frequency noise, as the number of IMF decompositions increases, if the high-frequency noise exists, the variance of the voltage data of the IMF component is large, and then the voltage anomaly factor of each time period is corrected according to whether there are short-term voltage fluctuations caused by circuit load changes to obtain a fault evaluation of each time period. The larger the fault evaluation of each time period, the more likely it is that the audio transformer will have a voltage anomaly and the voltage anomaly is caused by an abnormal operating state of the audio transformer.

[0064] Specifically, the EMD algorithm is used to decompose the voltage signal curve to obtain several IMF components; the environmental stability level S in the i-th time period i The calculation method is:

[0065]

[0066] Where: M represents the number of IMF components, d i,m represents the variance of the voltage data of the mth IMF component in the i-th time period; exp[] represents an exponential function with a natural constant as the base;

[0067] It should be noted that (m×d i,m ) is larger, the more unstable the voltage data of the mth IMF component is in the i-th time period. m is used as the weight. The larger the m is, the more times the IMF is decomposed, and the more stable the corresponding IMF component is. i,m The larger the value of , the more likely the factor causing the instability of the mth IMF component in the i-th time period is the short-term fluctuation of the voltage caused by the change of the circuit load; the EMD (Empirical Mode Decomposition) algorithm and the IMF (Intrinsic Mode Function) component are well-known technologies and will not be described in detail in this embodiment;

[0068] Fault evaluation P in the i-th time period i The calculation method is:

[0069] P i =Sigmoid(S i ×B i ′ ×Y i )

[0070] Where: S i Indicates the environmental stability of the i-th time period, B i ′ Indicates the degree of baseline difference in the i-th time period, Y i represents the voltage anomaly factor in the i-th time period;

[0071] Similarly, the fault evaluation of each time period is obtained.

[0072] Step S004: Based on the fault evaluation in each time period, the operating status of the audio transformer is monitored.

[0073] A fault threshold is preset. When there is a time period in the voltage signal curve in which the fault evaluation is greater than the fault threshold, the audio transformer operates abnormally and a fault warning is issued to monitor the operating status of the audio transformer. The preset fault threshold in this embodiment is 0.5, and other embodiments may be set to other values, which are not specifically limited in this embodiment.

[0074] It should be noted that the exp(-x) model used in this embodiment only indicates that the result of the negative correlation and constraint model output is in the interval (0,1], where x is the input of this model. It can be replaced by other models with the same purpose during specific implementation. This embodiment only takes the exp(-x) model as an example for description without specific limitation.

[0075] In a second aspect, an embodiment of the present invention provides an audio transformer operation status monitoring system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above steps S001 to S004 when executing the computer program.

[0076] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring the operating status of an audio transformer, characterized in that: The method comprises the following steps: Obtain the voltage data at all times to form a voltage signal curve, as well as the sound frequency at each moment; The voltage signal curve is divided into several time periods according to the signal cycle; a standard sine wave segment with a length of one cycle is recorded as a standard segment, and the standard difference degree and the benchmark difference degree of each time period are obtained according to the similarity between each time period and the standard segment; the voltage abnormality factor of each time period is obtained according to the benchmark difference degree of adjacent time periods of each time period and the stability of the audio data of each time period; Decompose the voltage signal curve to obtain several components; obtain the environmental stability of each time period based on the distribution of voltage data of each component in each time period; obtain the fault evaluation of each time period by combining the environmental stability of each time period with the benchmark difference and the voltage anomaly factor; Based on the fault evaluation in each time period, the operating status of the audio transformer can be monitored; The obtaining of the standard difference degree and the benchmark difference degree for each time period includes: All voltage data of the standard segment constitute a standard voltage sequence, and the voltage data of each time period constitutes a voltage sequence of each time period. The DTW distance between the voltage sequence of each time period and the standard voltage sequence is obtained, which is recorded as the DTW distance between each time period and the standard segment. The calculation method of the waveform difference between the i-th time period and the standard period is: Where: B i Indicates the degree of difference between the waveform of the i-th time period and the standard period, D i represents the DTW distance between the i-th time period and the standard period, N represents the number of characteristic points of the voltage signal curve in the i-th time period, n represents the n-th characteristic point, and C i,n represents the slope of the tangent line at the nth feature point in the i-th time period, C i ′ ,n It represents the slope of the tangent line of the corresponding point of the nth feature point in the i-th time period in the standard segment; Sigmoid() represents the Sigmoid function, and || represents the absolute value function; Obtain the waveform difference degree between all time periods and the standard segment, and record the time period with the minimum value of the waveform difference degree between all time periods and the standard segment as the reference segment; obtain the waveform difference degree between each time period and the reference segment, and the calculation method of the waveform difference degree between each time period and the reference segment is the same as the calculation method of the waveform difference degree between each time period and the standard segment; record the waveform difference degree between each time period and the standard segment as the standard difference degree, and record the waveform difference degree between each time period and the reference segment as the reference difference degree; The obtaining of the voltage anomaly factor in each time period includes: The voltage abnormality factor Y in the i-th time period in the voltage signal curve i The calculation method is: Y i =(ΔZ i,i-1 +ΔZ i,i+1 )×V i Where: ΔZ i,i-1 Indicates the absolute value of the difference between the benchmark difference between the i-th time period and the previous i-th time period, ΔZ i,i+1 V represents the absolute value of the difference between the benchmark difference between the i-th time period and the next i-th time period. i represents the variance of all sound frequencies in the i-th time period; The step of obtaining the environmental stability level in each time period includes: The environmental stability level S in the i-th time period i The calculation method is: Where: M represents the number of IMF components, d i,m represents the variance of the voltage data of the mth IMF component in the i-th time period; exp[] represents an exponential function with a natural constant as the base; The obtaining of the fault evaluation in each time period includes: The product of the environmental stability degree of the i-th time period and the benchmark difference degree of the i-th time period is used as the confidence coefficient of the voltage anomaly factor of the i-th time period, and the product of the confidence coefficient and the voltage anomaly factor of the i-th time period is normalized by the Sigmoid function and recorded as the fault evaluation of the i-th time period.

2. The method for monitoring the operating status of an audio transformer according to claim 1, characterized in that: The step of dividing the voltage signal curve into a plurality of time periods according to the signal cycle includes: Perform fast Fourier transform on the voltage signal curve to obtain the frequency spectrum of the voltage signal curve, take the inverse of the frequency of the peak with the largest amplitude in the frequency spectrum as the signal period, and divide the voltage signal curve into several time periods with the signal period as the interval.

3. The method for monitoring the operating status of an audio transformer according to claim 1, characterized in that: The specific method for obtaining the feature points is: All extreme points and zero crossing points of the standard segment and all time periods are recorded as feature points.

4. The method for monitoring the operating status of an audio transformer according to claim 3, characterized in that: The specific method for obtaining the corresponding points is: In the standard segment, the feature point with the shortest corresponding time distance to each feature point in each time period is recorded as the corresponding point of each feature point in each time period in the standard segment.

5. The method for monitoring the operating status of an audio transformer according to claim 1, characterized in that: The method of implementing the operating status monitoring of the audio transformer according to the fault evaluation in each time period includes: A fault threshold is preset. When there is a time period in the voltage signal curve where the fault evaluation is greater than the fault threshold, a fault warning is issued to monitor the operating status of the audio transformer.

6. A system for monitoring the operating status of an audio transformer comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method for monitoring the operating status of an audio transformer as claimed in any one of claims 1 to 5 are implemented.

Images