A load fault detection method, a load fault detection system, and an electronic device.

Abstract

This invention provides a load fault detection method, a load fault detection system, and an electronic device. The method includes: First, spreading the original signal using a pseudo-random sequence to obtain a driving signal. Since the bandwidth of the pseudo-random sequence is greater than that of the original signal, the signal bandwidth of the driving signal is widened, thereby improving the anti-interference capability of the driving signal. Furthermore, since the amplitude of the pseudo-random sequence is attenuated by a preset decibel compared to the original signal, interference from the pseudo-random sequence to the original signal is avoided. Next, the driving signal is output to the load, and the return signal from the load is acquired. Finally, correlation analysis is performed on the return signal and the pseudo-random sequence to obtain a despread signal, and the energy magnitude of the despread signal is extracted. The fault type of the load is determined based on the energy magnitude of the despread signal. Because the signal-to-noise ratio of the despread signal has a significantly higher gain than that of the driving signal, the accuracy of load detection is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of power electronics, and more particularly to a load fault detection method, a load fault detection system, and an electronic device. Background Technology

[0002] Real-time load diagnostics is primarily used in audio systems (such as speakers and amplifiers) or motor drive systems. It aims to monitor load conditions (such as short circuits, open circuits, or anomalies) online to prevent equipment damage, ensure audio quality, and maintain system safety. These methods are typically performed during audio playback without interrupting normal operation.

[0003] However, existing technologies are often subject to electromagnetic interference or nonlinear load environments during real-time load diagnostics, which affects the accuracy of load fault detection. Summary of the Invention

[0004] This invention provides a load fault detection method, a load fault detection system, and an electronic device to solve the above-mentioned technical problems, thereby greatly improving the accuracy of load fault detection.

[0005] According to a first aspect of the present invention, a load fault detection method is provided, the method comprising:

[0006] Generate pseudo-random sequences;

[0007] The original signal is spread using the pseudo-random sequence to obtain a driving signal. The bandwidth of the pseudo-random sequence is greater than the bandwidth of the original signal, the bandwidth of the driving signal is equal to the bandwidth of the pseudo-random sequence, and the amplitude of the pseudo-random sequence is less than or equal to the amplitude of the noise.

[0008] The drive signal is output to the load, and the return signal from the load is acquired.

[0009] Correlation analysis is performed on the returned signal and the pseudo-random sequence to obtain the despread signal, and the energy level of the despread signal is extracted.

[0010] If the energy of the despread signal is less than the open-circuit preset energy, then the load is determined to be in an open-circuit state.

[0011] If the energy of the despread signal is greater than the short-circuit preset energy, then the load is determined to be in a short-circuit state;

[0012] If the energy of the despread signal is between the open-circuit preset energy and the short-circuit preset energy, then the load is determined to be in a normal state.

[0013] Optionally, spreading the original signal using the pseudo-random sequence specifically includes: performing XOR modulation or multiplication modulation on the pseudo-random sequence and the original signal.

[0014] Optionally, the amplitude of the pseudo-random sequence is attenuated by 100 dB compared to the amplitude of the original signal.

[0015] Optionally, the formula for the correlation analysis gain is:

[0016] PG = 10log10(Bc / Bs) (dB);

[0017] Where PG is the correlation analysis gain, Bc is the bandwidth of the pseudo-random sequence, and Bs is the bandwidth of the original signal.

[0018] Optionally, the formula for the despread signal is:

[0019] ;

[0020] Among them, R ( ) is the despread signal, N is the length of the pseudo-random sequence, s(n) is the pseudo-random sequence, r(n+ ) is the return signal.

[0021] Optionally, the energy formula of the despread signal is:

[0022] E = ∑τ[R(τ)]2;

[0023] Where E is the energy of the despread signal.

[0024] According to a second invention of the present invention, a load fault detection system is provided, comprising:

[0025] A pseudo-random sequence generation module, which is used to generate and output the pseudo-random sequence;

[0026] A signal superposition module is used to superimpose the pseudo-random sequence and the original signal to obtain a driving signal and output it to the load. The bandwidth of the pseudo-random sequence is much larger than the bandwidth of the original signal, the bandwidth of the driving signal is equal to the bandwidth of the pseudo-random sequence, and the amplitude of the pseudo-random sequence is attenuated by a preset decibel compared with the original signal.

[0027] A signal acquisition module, which is used to acquire and output the return signal output by the load;

[0028] A correlation analysis module is used to perform correlation analysis on the returned signal and the pseudo-random sequence, and to extract the energy level of the despread signal;

[0029] The load fault determination module compares the energy of the despread signal with a preset open-circuit energy and a preset short-circuit energy, respectively. If the energy of the despread signal is less than the preset open-circuit energy, the load is determined to be in an open-circuit state; if the energy of the despread signal is greater than the preset short-circuit energy, the load is determined to be in a short-circuit state; if the energy of the despread signal is between the preset open-circuit energy and the preset short-circuit energy, the load is determined to be in a normal state.

[0030] Optionally, the signal acquisition module is an analog-to-digital converter.

[0031] Optionally, the pseudo-random sequence generation module is a linear feedback shift register.

[0032] According to a third aspect of the present invention, an electronic device is provided, including the aforementioned load fault detection system.

[0033] Compared with the prior art, the technical solution of the present invention has the following beneficial effects:

[0034] In the load fault detection method provided by this invention, firstly, the original signal is spread using a pseudo-random sequence to obtain a driving signal. Since the bandwidth of the pseudo-random sequence is greater than that of the original signal, the signal bandwidth of the driving signal is broadened, thereby reducing the power spectral density of the driving signal and uniformly distributing the signal energy across a wide frequency band, thus improving the anti-interference capability of the driving signal. Furthermore, since the amplitude of the pseudo-random sequence is less than or equal to the amplitude of the noise, interference from the pseudo-random sequence to the original signal is avoided. Next, the driving signal is output to the load, and the return signal from the load is acquired. Finally, correlation analysis is performed on the return signal and the pseudo-random sequence to obtain a despread signal, and the energy magnitude of the despread signal is extracted. The fault type of the load is determined based on the energy magnitude of the despread signal. Because the despread signal re-aggregates the energy in the original signal, the signal-to-noise ratio of the despread signal has a significant gain compared to that of the driving signal, thereby greatly improving the accuracy of the despread signal while enhancing the anti-interference capability of the driving signal, thus significantly improving the accuracy of load detection.

[0035] In addition to the beneficial effects of the above-mentioned method, the load fault detection system provided by this invention reduces the circuit cost of the system because it eliminates the need for an additional specific signal transmitting device for load detection. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a flowchart illustrating the load fault detection method provided in an embodiment of the present invention;

[0038] Figure 2 This is a schematic diagram of the load fault detection system provided in an embodiment of the present invention. Detailed Implementation

[0039] As described in the background section, existing technologies are often subject to electromagnetic interference or nonlinear load environments during real-time load diagnostics, which affects the accuracy of load fault detection.

[0040] In view of this, the technical solution of the present invention provides a novel load fault detection method, which spreads the original signal using a pseudo-random sequence to obtain a driving signal. Since the bandwidth of the pseudo-random sequence is greater than that of the original signal, the signal bandwidth of the driving signal is broadened, thereby reducing the power spectral density of the driving signal and uniformly distributing the signal energy across a wide frequency band, thus improving the anti-interference capability of the driving signal. Furthermore, since the amplitude of the pseudo-random sequence is attenuated by a preset decibel compared to the original signal, interference from the pseudo-random sequence to the original signal is avoided. Next, the driving signal is output to the load, and the return signal from the load is acquired. Finally, correlation analysis is performed on the return signal and the pseudo-random sequence to obtain a despread signal, and the energy magnitude of the despread signal is extracted. The fault type of the load is determined based on the energy magnitude of the despread signal. Because the despread signal refocuses the energy in the original signal, the signal-to-noise ratio of the despread signal has a significant gain compared to that of the driving signal, thereby greatly improving the accuracy of the despread signal while enhancing the anti-interference capability of the driving signal, thus significantly improving the accuracy of load detection.

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0043] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0044] Figure 1 This is a flowchart illustrating the load fault detection method provided by the present invention.

[0045] Please refer to Figure 1 The load fault detection method provided by this invention includes the following specific steps:

[0046] S1: Generate a pseudo-random sequence.

[0047] Specifically, the pseudo-random sequence is generated by an n-bit linear feedback shift register, therefore the length of the pseudo-random sequence is 2n-1. Since the length of the pseudo-random sequence can be adjusted according to requirements, the number of bits in the linear feedback shift register is not limited here.

[0048] S2: Spread the original signal using the pseudo-random sequence to obtain the driving signal.

[0049] Specifically, the bandwidth of the pseudo-random sequence is greater than the bandwidth of the original signal, the bandwidth of the driving signal is equal to the bandwidth of the pseudo-random sequence, and the amplitude of the pseudo-random sequence is less than or equal to the amplitude of the noise.

[0050] Furthermore, in this embodiment, the bandwidth of the pseudo-random sequence is 10kHz, while the bandwidth of the original signal is only 100Hz. Therefore, the bandwidth of the driving signal obtained after spread spectrum is equal to 100Hz. Since the amplitude of the noise is equivalent to -100dB of the original signal, the amplitude of the pseudo-random sequence is attenuated by 100dB compared to the amplitude of the original signal. This means that the signal strength of the pseudo-random sequence is only one ten-thousandth of the original signal, and the signal power of the pseudo-random sequence is only one ten-billionth of the original signal. Therefore, the pseudo-random sequence will not interfere with the original signal. Of course, the bandwidth of the pseudo-random sequence, the bandwidth of the original signal, and the preset decibel can all be adjusted according to actual needs, and are not limited here.

[0051] Furthermore, spreading the original signal using the pseudo-random sequence specifically includes: performing XOR modulation or multiplication modulation on the pseudo-random sequence and the original signal.

[0052] Since pseudo-random sequences have a white noise-like spectrum, spreading the original signal with a pseudo-random sequence can disperse the energy of the original signal into the wide bandwidth of the pseudo-random sequence, so that the power spectral density of the driving signal is much lower than the power spectral density of the noise, making the driving signal difficult to detect directly.

[0053] Furthermore, since pseudo-random sequences also have low cross-correlation, the cross-correlation values ​​between different sequences are extremely low, thereby greatly reducing the impact of multipath interference and external noise.

[0054] Meanwhile, the broadband characteristics of the pseudo-random sequence also provide natural immunity to narrowband interference (e.g., electromagnetic noise in motor drive), thereby further improving the anti-interference capability of the drive signal.

[0055] It should be added that, before step S2, the method further includes: amplitude control of the pseudo-random sequence and reducing the amplitude of the pseudo-random sequence by a preset decibel.

[0056] S3: Output the drive signal to the load and collect the return signal from the load.

[0057] Specifically, the returned signal is acquired through an analog-to-digital converter.

[0058] S4: Perform correlation analysis on the returned signal and the pseudo-random sequence to obtain the despread signal, and extract the energy magnitude of the despread signal.

[0059] Specifically, the correlation analysis between the returned signal and the pseudo-random sequence in S4 includes: performing a correlation operation on the returned signal and the pseudo-random sequence to obtain the despread signal. The formula for the despread signal is:

[0060] ;

[0061] Among them, R ( ) is the despread signal, N is the length of the pseudo-random sequence, s(n) is the pseudo-random sequence, r(n+ ) is the return signal.

[0062] After obtaining the despread signal, the integral of the despread signal is squared to extract its energy. The energy formula for the despread signal is:

[0063] E = ∑τ[R(τ)]2;

[0064] Where E is the energy of the despread signal.

[0065] Because the pseudo-random sequence has strong autocorrelation, when performing correlation operations on the pseudo-random sequence and the returned signal, the energy in the pseudo-random sequence will be coherently superimposed to form a high-amplitude energy peak, thereby extracting the energy from the original signal. Therefore, the signal-to-noise ratio (SNR) of the despread signal at the receiving end is much greater than that of the driving signal at the transmitting end, greatly improving the correlation analysis gain. This can be understood as the SNR at the receiving end increasing at a multiple compared to the SNR at the transmitting end. The formula for the correlation analysis gain is as follows:

[0066] PG = 10log10(Bc / Bs) (dB);

[0067] Where PG is the correlation analysis gain, Bc is the bandwidth of the pseudo-random sequence, and Bs is the bandwidth of the original signal.

[0068] If the bandwidth of the pseudo-random sequence is 10kHz and the bandwidth of the original signal is 100Hz, then the correlation analysis gain, i.e. the signal-to-noise ratio at the signal receiver is increased by 100 times compared to the signal-to-noise ratio at the signal transmitter, can still obtain an accurate despread signal even when the channel noise power is much greater than the power of the driving signal, thereby greatly improving the accuracy of load detection.

[0069] S5: Compare the energy of the despread signal with the open-circuit preset energy and the short-circuit preset energy respectively to determine the load fault.

[0070] Since the returned signal includes a pseudo-random sequence, and the energy of the pseudo-random sequence in the returned signal is affected by the state of the load, the fault type of the load can be determined based on the energy of the despread signal obtained from the returned signal.

[0071] When the load is in a short-circuit state, it absorbs signal energy, thus reducing both the energy of the returned signal and the noise energy. Because the pseudo-random sequence in the returned signal has strong autocorrelation characteristics, and the noise energy is an incoherent superposition, after correlation analysis, the increase in noise energy is much smaller than the increase in the energy of the despread signal, thereby increasing the energy of the despread signal. Therefore, this embodiment sets a short-circuit preset energy, which can be understood as the upper limit of the despread signal energy under normal load conditions. If the energy of the despread signal is greater than the short-circuit preset energy, the load is determined to be in a short-circuit state.

[0072] When the load is in an open-circuit state, the load impedance becomes infinitely large. Therefore, a severe mismatch occurs between the load impedance and the transmission line characteristic impedance, causing the load to completely reflect the driving signal. The returned signal acquired at this time has almost no delay, and its waveform is completely synchronized with the pseudo-random sequence, thus being considered an invalid signal. The energy of the returned signal acquired after delay is almost zero. Therefore, this embodiment sets a preset open-circuit energy, which can be understood as the lower limit of the energy of the despread signal under normal load conditions. If the energy of the despread signal is less than the preset open-circuit energy, the load is determined to be in an open-circuit state.

[0073] If the energy of the despread signal is between the open-circuit preset energy and the short-circuit preset energy, then the load is determined to be in a normal state.

[0074] Please refer to Figure 2 This embodiment also provides a load 40 fault detection system, including a pseudo-random sequence generation module 10, a signal superposition module 20, a signal acquisition module 40, a correlation analysis module 50, and a load fault judgment module 60.

[0075] The pseudo-random sequence generation module 10 is used to generate and output the pseudo-random sequence. Specifically, the pseudo-random sequence generation module 10 is a linear feedback shift register.

[0076] The signal superposition module 20 is used to superimpose the pseudo-random sequence and the original signal to obtain a driving signal and output it to the load 40. The bandwidth of the pseudo-random sequence is much larger than the bandwidth of the original signal. The bandwidth of the driving signal is equal to the bandwidth of the pseudo-random sequence, and the amplitude of the pseudo-random sequence is attenuated by a preset decibel compared to the original signal.

[0077] The signal acquisition module 40 is used to acquire and output the return signal output by the load 40. Specifically, the signal acquisition module 40 is an analog-to-digital converter.

[0078] The correlation analysis module 50 is used to perform correlation analysis on the returned signal and the pseudo-random sequence, and to extract the energy of the despread signal;

[0079] The load fault judgment module 60 is used to compare the energy of the despread signal with the open-circuit preset energy and the short-circuit preset energy respectively. If the energy of the despread signal is less than the open-circuit preset energy, the load 40 is judged to be in an open-circuit state; if the energy of the despread signal is greater than the short-circuit preset energy, the load 40 is judged to be in a short-circuit state; if the energy of the despread signal is between the open-circuit preset energy and the short-circuit preset energy, the load 40 is judged to be in a normal state.

[0080] In summary, the load fault detection method provided in this embodiment firstly spreads the original signal using a pseudo-random sequence to obtain a driving signal. Since the bandwidth of the pseudo-random sequence is greater than that of the original signal, the signal bandwidth of the driving signal is broadened, thereby reducing the power spectral density of the driving signal and uniformly distributing the signal energy across a wide frequency band, thus improving the anti-interference capability of the driving signal. Furthermore, since the amplitude of the pseudo-random sequence is attenuated by a preset decibel compared to the original signal, interference from the pseudo-random sequence to the original signal is avoided. Next, the driving signal is output to the load, and the load's return signal is acquired. Finally, correlation analysis is performed on the return signal and the pseudo-random sequence to obtain a despread signal, and the energy magnitude of the despread signal is extracted. The load fault type is then determined based on the energy magnitude of the despread signal. Because the despread signal refocuses the energy in the original signal, the signal-to-noise ratio of the despread signal has a significant gain compared to that of the driving signal, thereby greatly improving the accuracy of the despread signal while enhancing the anti-interference capability of the driving signal, thus significantly improving the accuracy of load detection.

[0081] Furthermore, in addition to the beneficial effects of the above-mentioned method, the load fault detection system provided by the present invention reduces the circuit cost of the system because it does not require an additional specific signal transmitting device for load detection.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of detecting a load fault, characterized by, The method comprises: generating a pseudo-random sequence; spreading the original signal by the pseudo-random sequence to obtain a driving signal, the bandwidth of the pseudo-random sequence being greater than the bandwidth of the original signal, the bandwidth of the driving signal being equal to the bandwidth of the pseudo-random sequence, and the amplitude of the pseudo-random sequence being less than or equal to the amplitude of noise; outputting the driving signal to a load and collecting a return signal of the load; performing correlation analysis on the return signal and the pseudo-random sequence to obtain a despread signal and extracting the energy of the despread signal; if the energy of the despread signal is less than a preset open-circuit energy, judging that the load is in an open-circuit state; if the energy of the despread signal is greater than a preset short-circuit energy, judging that the load is in a short-circuit state; if the energy of the despread signal is between the preset open-circuit energy and the preset short-circuit energy, judging that the load is in a normal state.

2. The load fault detection method of claim 1, wherein, The spreading of the original signal by the pseudo-random sequence specifically comprises XOR modulation or multiplication modulation of the pseudo-random sequence and the original signal.

3. The load fault detection method of claim 1, wherein, The amplitude of the pseudo-random sequence is attenuated by 100 dB compared with the amplitude of the original signal.

4. The load fault detection method of claim 1, wherein, The formula of the correlation analysis gain is: PG=10log10(Bc / Bs) (dB); wherein, PG is the correlation analysis gain, Bc is the bandwidth of the pseudo-random sequence, and Bs is the bandwidth of the original signal.

5. The load fault detection method of claim 1, wherein, The formula of the despread signal is: ； Among them, R ( ) is the despread signal, N is the length of the pseudo-random sequence, s(n) is the pseudo-random sequence, r(n+ ) is the return signal.

6. The load fault detection method of claim 5, wherein, The energy formula of the despread signal is: E=∑τ[R(τ)]2 wherein, E is the energy of the despread signal.

7. A load fault detection system characterized by, The method comprises: a pseudo-random sequence generation module for generating the pseudo-random sequence and outputting; a signal superposition module for superimposing the pseudo-random sequence and the original signal to obtain a driving signal and outputting to a load, the bandwidth of the pseudo-random sequence being much greater than the bandwidth of the original signal, the bandwidth of the driving signal being equal to the bandwidth of the pseudo-random sequence, and the amplitude of the pseudo-random sequence being attenuated by a preset decibel compared with the original signal; a signal collection module for collecting a return signal output by the load and outputting; a correlation analysis module for performing correlation analysis on the return signal and the pseudo-random sequence and extracting the energy of the despread signal; a load fault judgment module for comparing the energy of the despread signal with a preset open-circuit energy and a preset short-circuit energy, if the energy of the despread signal is less than the preset open-circuit energy, judging that the load is in an open-circuit state, if the energy of the despread signal is greater than the preset short-circuit energy, judging that the load is in a short-circuit state, and if the energy of the despread signal is between the preset open-circuit energy and the preset short-circuit energy, judging that the load is in a normal state.

8. The load fault detection system of claim 7, wherein, The signal collection module is an analog-to-digital converter.

9. The load fault detection system of claim 7, wherein, The pseudo-random sequence generation module is a linear feedback shift register.

10. An electronic device, comprising: The load fault detection system comprises any one of claims 7 to 9.

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