Device cascade process system oscillation detection and diagnosis method and device based on multivariate nonlinear frequency modulation mode decomposition, and storage medium

Abstract

The invention discloses a device cascade process system oscillation detection and diagnosis method based on multivariate nonlinear frequency modulation mode decomposition, and the method comprises the following steps: S1, data preparation and preprocessing; step S2, an oscillation detection stage: performing advanced multivariate signal decomposition on process data through a multivariate nonlinear frequency modulation mode decomposition technology, effectively extracting mode signals with time-varying and time-invariant characteristics, and performing oscillation detection by using a correlation coefficient and an auto-covariance function zero crossing regularity index; and S3, an oscillation diagnosis stage: after existence of oscillation is confirmed, grouping multivariate Granger causal analysis and phase correlation analysis are combined. The multivariate nonlinear frequency modulation mode decomposition method can effectively extract the time-invariant oscillation mode and the time-varying oscillation mode in the device cascading process and achieve alignment between the modes, and is particularly suitable for complex multi-system oscillation diagnosis in the industrial process.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial process data analysis, and in particular to an oscillation detection and diagnosis method for a device cascade process system based on multivariate nonlinear frequency modulation mode decomposition. Background Art

[0002] In the context of the increasing complexity of modern process industries, a typical production process often consists of thousands of units and control loops. Due to the close coupling between process equipment, the oscillation of a local single loop can quickly propagate to the entire system loop, leading to widespread instability of the control system and then forming system oscillation. System oscillation is not only the main manifestation of controller performance deterioration, but also one of the key factors threatening the safe operation of industrial processes. Therefore, as an early warning signal of potential system failures, the detection and diagnosis of system oscillations are crucial for preventive maintenance and fault prediction.

[0003] System oscillation often occurs during the device cascade process due to changes in external input conditions and valve sticking. The device cascade process involves multiple equipment units and control loops, which are easily affected by changes in equipment characteristics or external interference, resulting in single loop oscillation or even system-wide oscillation, thereby increasing the instability of the control system. Oscillation is common when system control performance is reduced or potential failures occur.

[0004] However, traditional oscillation diagnosis methods usually focus on the detection and diagnosis of single-loop oscillations, and are difficult to adapt to time-varying, nonlinear and non-stationary oscillation phenomena, resulting in challenges in efficiency and effectiveness in system-level oscillation detection and diagnosis. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention proposes a method for detecting and diagnosing oscillation of a device cascade process system based on multivariate nonlinear frequency modulation modal decomposition, so as to effectively improve the accuracy and practicality of system oscillation diagnosis.

[0006] Based on this, the present invention proposes a method for detecting and diagnosing oscillation of a device cascade process system based on multivariate nonlinear frequency modulation mode decomposition, comprising the following steps:

[0007] Step S1, data preparation and preprocessing:

[0008] Collect the online data x of the device cascade process and use Min-Max normalization preprocessing;

[0009] Step S2, oscillation detection:

[0010] The multivariate nonlinear frequency modulation mode decomposition method is used to decompose the normalized online data x, extract the time-invariant and time-varying oscillation modes, and calculate the correlation coefficient θ between each mode and the original signal.q,m , remove false modes with low correlation, and calculate the zero-crossing regularity index r of the autocovariance function q,m , identify the true oscillation mode;

[0011] Step S3, oscillation diagnosis:

[0012] After the oscillation modes are identified, the oscillation modes with similar frequencies are grouped. For each oscillation mode group G q , performs oscillation diagnosis based on multivariate Granger causality analysis, based on phase correlation information The results of multivariate Granger causality analysis are optimized to eliminate irrelevant causal relationships and accurately locate the oscillation source.

[0013] In a possible design, the signal decomposition described in step S2 to extract the time-invariant and time-varying oscillation modes is specifically:

[0014] A multivariate AM / FM signal with M channels is expressed as follows:

[0015]

[0016] Among them, a m (t), f m and φ m Represent the instantaneous amplitude, instantaneous frequency and initial phase of the mth component respectively;

[0017] The simplified analytical expression of the signal g(t), that is, the analytical form of the multivariate nonlinear frequency modulation mode, can be expressed as:

[0018]

[0019] Among them, f(t) and a(t) are the instantaneous frequency and instantaneous amplitude of the current multivariate nonlinear frequency modulation mode, respectively, and j represents the imaginary unit in complex algebra. The core of multivariate nonlinear frequency modulation mode decomposition is to find a smooth function To estimate the instantaneous frequency of the multivariate nonlinear frequency modulation mode and ensure the narrowest bandwidth of the baseband signal;

[0020] According to the properties of trigonometric functions, the multivariate nonlinear frequency modulation mode can be reconstructed by the following formula:

[0021]

[0022] Among them, u(t) and v(t) are two demodulated signals, which can be expressed as:

[0023]

[0024] Assuming the number of modes is Q and the number of channels is M, the multivariate nonlinear frequency modulation mode decomposition aims to extract Decompose the combination of Q multivariate nonlinear frequency modulation modes so that the sum of their bandwidths is minimized. The objective function of multivariate nonlinear frequency modulation mode decomposition can be expressed as:

[0025]

[0026] Wherein, q and m represent the modality index and channel index respectively, q=1,2,...,Q, m=1,2,...,M; To reconstruct the input signal x of the mth channel m The signal of the multivariate nonlinear frequency modulation mode combination of the qth mode; where u' q ' ,m (t),v' q ' ,m (t) are two demodulated signals, smoothing function Used to estimate the instantaneous frequency of multivariate nonlinear frequency modulation modes. m is a preset minimum value.

[0027] In a possible design, the correlation coefficient θ between each mode and the original signal is calculated as described in step S2. q,m , remove the false modes with low correlation, specifically: remove the false modes that may be generated by multivariate nonlinear frequency modulation mode decomposition, the false modes g q,m With the original signal x m The correlation between them is usually low, and the correlation between them is evaluated by calculating the correlation coefficient between them. The correlation coefficient can be calculated as follows:

[0028]

[0029] Among them, cov(·,·) and std(·) represent covariance and standard deviation respectively; the normalized correlation coefficient can be calculated by the following formula:

[0030]

[0031] max q (·) represents the maximum; let the threshold of the normalized correlation coefficient be T θ , that is, to determine θ q,m ≤T θ g q,m False mode.

[0032] In a possible design, the calculation of the autocovariance function zero crossing regularity index r in step S2 is q,m, identify the true oscillation mode, specifically: the zero-crossing regularity index of the autocovariance function can be used to identify time-invariant and time-varying oscillation modes. For mode g q,m The zero-crossing sequence If the standard deviation of the sequence σ t,q,m Less than its average value μ t,q,m One third of , it can be determined that the mode has oscillation characteristics. Based on this criterion, the zero crossing regularity index of the autocovariance function can be defined as:

[0033]

[0034] This indicator reveals the variability of the zero-crossing sequence relative to its mean, and the threshold is set to T r , when r is satisfied q,m >T r When the mode g q,m Oscillating nature.

[0035] In a possible design, in step S2, the threshold value T of the normalized correlation coefficient is θ is 0.4; the threshold T in the zero-crossing regularity index of the autocovariance function r is 1.

[0036] In a possible design, the oscillation diagnosis in step S3 includes the following steps:

[0037] Step S3.1, the oscillation detection result can be expressed by the following oscillation matrix:

[0038]

[0039] Among them, q,m If and only if the event ((θ q,m >T θ )&(r q,m >T r )) is true, which means that g q,m Neither is it a spurious mode nor does it actually exhibit oscillatory behavior;

[0040] Step S3.2, grouping oscillation modes with similar frequencies, the specific process is as follows:

[0041] G q = {g q,m}(o q,m =1)

[0042] For each oscillation mode group G with similar frequency q Perform independent oscillation diagnosis based on multivariate Granger causality analysis to accurately obtain the root cause analysis results of multiple oscillation phenomena. Assume that p q,i,j Represents the oscillation signal gq,i and g q,j The p-value obtained by the Granger causality F test is considered to be q,j For g q,i Granger causality:

[0043] p q,i,j <1-α

[0044] Here, α represents the confidence level.

[0045] Step S3.3, by evaluating the phase correlation between different modes in the same mode group, the relative time delay between the signals is identified to optimize the diagnosis result; given two oscillating signals g q,i and g q,j , these two signals are transformed by Fourier transform FFT to obtain their spectral representation:

[0046] X q,i (f) = FFT(g q,i )

[0047] X q,j (f) = FFT(g q,j )

[0048] The phase correlation of these two signals is calculated using the following formula:

[0049]

[0050] in, Represents g q,j The conjugate of the FFT result of ; IFFT(·) stands for inverse Fourier transform. Finally, by finding the maximum value in the phase correlation result, the signal g can be determined q,i Relative to g q,j The time delay is expressed as follows:

[0051]

[0052] in, Represents the index where the maximum phase correlation is located, which can be used to estimate the time delay between two signals, and thus indirectly estimate the phase difference; set the threshold to When satisfied When g q,j Prior to g in time q,i , i.e. g q,i For g q,j There is no temporal causal action;

[0053] For two oscillating signals g q,i and g q,j,event If it is true, then g q,j For g q,i There is a causal effect, that is, g q,i The oscillation of g q,j cause.

[0054] In one possible design, the threshold is set in step S3 is 5.

[0055] A device, characterized in that it includes a memory, a control processor and a computer program stored in the memory and executable on the control processor, wherein the control processor executes the program to implement the device cascade process system oscillation detection and diagnosis method based on multivariate nonlinear frequency modulation modal decomposition as described in any one of claims 1 to 7.

[0056] The present invention also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to implement the device cascade process system oscillation detection and diagnosis method based on multivariate nonlinear frequency modulation modal decomposition as described above.

[0057] In summary, the present invention uses multivariate nonlinear frequency modulation modal decomposition technology to perform advanced multivariate signal decomposition on process data, effectively extracting modal signals with time-varying and time-invariant characteristics. Oscillation detection is performed using correlation coefficients and autocovariance function zero-crossing regularity indicators. After confirming the existence of oscillation, combined with group multivariate Granger causality analysis and phase correlation analysis, the accuracy and practicality of system oscillation diagnosis are effectively improved.

[0058] The present invention has the following advantages:

[0059] (1) The multivariate nonlinear frequency modulation modal decomposition method of the present invention can effectively extract the time-invariant and time-varying oscillation modes in the device cascade process and achieve inter-modal alignment, which is particularly suitable for complex multi-system oscillation diagnosis in industrial processes;

[0060] (2) The present invention effectively eliminates misleading modes that may be generated by the multivariate nonlinear frequency modulation mode decomposition method by calculating the correlation coefficient between the false mode and the original signal and the zero-crossing regularity index of the autocovariance function of each mode, thereby ensuring the accuracy and reliability of the oscillation analysis;

[0061] (3) The present invention optimizes the traditional multivariate Granger causality analysis results by introducing an adjustment method based on trigonometric function phase information, fully utilizes the modal features extracted by the multivariate nonlinear frequency modulation modal decomposition method, and improves the accuracy of system oscillation diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The features and advantages of the present invention will be more clearly understood by referring to the accompanying drawings, which are schematic and should not be construed as limiting the present invention in any way. In the accompanying drawings:

[0063] Figure 1 This is a schematic diagram of the cascading process of the device according to the embodiment of the present invention;

[0064] Figure 2 It is a flow chart of the oscillation detection and diagnosis method of the device cascade process system based on multivariate nonlinear frequency modulation modal decomposition according to the present invention;

[0065] Figure 3 It is a decomposition result diagram of multivariate nonlinear frequency modulation mode decomposition of variables No. 30, No. 31, and No. 32 in sub-block 4 of an embodiment of the present invention;

[0066] Figure 4 It is a decomposition result diagram of multivariate nonlinear frequency modulation mode decomposition of variables No. 33, No. 34, and No. 35 in sub-block 4 of an embodiment of the present invention;

[0067] Figure 5 It is a decomposition result diagram of multivariate nonlinear frequency modulation mode decomposition of variables No. 36, No. 37, and No. 38 in sub-block 4 of an embodiment of the present invention;

[0068] Figure 6 This is a visualization diagram of the oscillation detection result of sub-block 4 of an embodiment of the present invention;

[0069] Figure 7 The causal analysis comparison diagram of sub-block 4 of the embodiment of the present invention, wherein (a) is multivariate Granger causal analysis, and (b) is multivariate Granger causal analysis + phase correlation analysis;

[0070] Figure 8 It is the oscillation diagnosis directed graph of sub-block 4 according to the embodiment of the present invention. DETAILED DESCRIPTION

[0071] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0072] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0073] At present, data-driven system oscillation diagnosis methods are mainly divided into two categories: frequency domain analysis methods and time-frequency analysis methods. System oscillation diagnosis based on frequency domain analysis methods mainly include spectral principal component analysis (SPCA), spectral independent component analysis (SICA), spectral non-negative matrix factorization (SNMF), spectral genetic algorithm (SGA), etc. The system oscillation diagnosis method based on SPCA has the problem of modal aliasing. To address this problem, a system oscillation diagnosis method based on SICA is proposed, but this method is not robust enough in the face of interference and is difficult to adapt to the actual industrial environment. The system oscillation diagnosis method based on SNMF has improved calculation speed and processing ability for strong noise interference compared with the previous two, and is more suitable for system oscillation diagnosis in complex noise environments. Subsequently, many scholars proposed a system oscillation diagnosis method based on SGA. Due to the advantages of SGA in the ability to search the solution space globally, the error is minimized when reconstructing the original power spectrum matrix, showing better diagnostic performance than SICA and SNMF.

[0074] System oscillation diagnosis based on time-frequency analysis methods mainly includes multivariate empirical mode decomposition (MEMD), multivariate intrinsic time scale decomposition (MITD), multivariate variational mode decomposition algorithm (MVMD), multivariate nonlinear frequency modulation mode decomposition (MNCMD), etc. Multivariate time-frequency analysis methods, with their significant advantages in processing and analyzing nonlinear and non-stationary signals, are gradually gaining wider application and recognition in the field of system oscillation diagnosis. Introducing MEMD into the field of system oscillation diagnosis, combined with MEMD, correlation coefficient matrix and sparsity index, system oscillation diagnosis can be achieved. Subsequently, a system oscillation diagnosis method based on auxiliary noise MEMD effectively avoids the mode aliasing problem of MEMD. The system oscillation diagnosis method based on MITD can reduce the time complexity of multivariate signal decomposition. Unlike previous empirical signal decomposition methods, MVMD is based on a sound mathematical theory. Although MVMD performs well in the decomposition of narrowband signals, its ability to process broadband or time-varying signals is relatively limited. A system oscillation diagnosis algorithm based on multivariate nonlinear frequency modulation mode decomposition overcomes this limitation. Furthermore, by combining the grouped multivariate Granger causality test, a more effective oscillation diagnosis is achieved.

[0075] Frequency domain analysis methods have been widely used in many fields due to their long history and remarkable effects. However, time-frequency analysis methods, by comprehensively considering the time and frequency characteristics of signals, provide new perspectives and tools for the analysis and resolution of nonlinear, non-stationary signals and multi-frequency component signals, indicating their important potential and role in addressing practical industrial challenges.

[0076] In order to solve the problems in the oscillation detection and diagnosis of the device cascade process system, the present invention proposes a device cascade process system oscillation detection and diagnosis method based on multivariate nonlinear frequency modulation modal decomposition. The multivariate nonlinear frequency modulation modal decomposition technology is used to perform advanced multivariate signal decomposition on the process data to effectively extract modal signals with time-varying and time-invariant characteristics. Oscillation detection is performed using the correlation coefficient and the zero-crossing regularity index of the autocovariance function. After confirming the existence of oscillation, combined with group multivariate Granger causality analysis and phase correlation analysis, the accuracy and practicality of system oscillation diagnosis are effectively improved.

[0077] In order to solve the above problems in the oscillation detection and diagnosis of the device cascade process system, the present invention proposes a device cascade process system oscillation detection and diagnosis method based on multivariate nonlinear frequency modulation mode decomposition, comprising the following steps:

[0078] S1. Data preparation and preprocessing: Collect online data of the device cascade process and use Min-Max normalization preprocessing;

[0079] S2, Oscillation detection stage: Apply multivariate nonlinear frequency modulation mode decomposition technology to decompose the normalized online data x to extract time-invariant and time-varying oscillation modes, and calculate the correlation coefficient θ between each mode and the original signal q,m , remove false modes with low correlation to ensure the accuracy of analysis, and calculate the zero-crossing regularity index r of the autocovariance function q,m , identify the true oscillation mode and lay the foundation for further diagnostic work;

[0080] S3, Oscillation diagnosis stage: After identifying the oscillation mode, the oscillation modes with similar frequencies are grouped. For each oscillation mode group G q , performs oscillation diagnosis based on multivariate Granger causality analysis, based on phase correlation information The results of multivariate Granger causality analysis are optimized (pruned) to remove irrelevant causal relationships and accurately locate the oscillation source.

[0081] Furthermore, the step S2 includes the following steps:

[0082] S2.1. First, a multi-channel AM / FM signal can be expressed as follows:

[0083] The formula means:

[0084]

[0085] Among them, a m (t), f m and φ mRepresent the instantaneous amplitude, instantaneous frequency and initial phase of the mth component respectively.

[0086] S2.2. The simplified analytical expression of the signal g(t), that is, the analytical form of the multivariate nonlinear frequency modulation mode (multivariate nonlinear frequency modulation mode), can be expressed as:

[0087]

[0088] Among them, f(t) is the instantaneous frequency of the current multivariate nonlinear frequency modulation mode, a(t) is the instantaneous amplitude of the current multivariate nonlinear frequency modulation mode, and j represents the imaginary unit in complex algebra. The core of multivariate nonlinear frequency modulation mode decomposition is to find a smooth function To estimate the instantaneous frequency of the multivariate nonlinear FM mode and ensure that the bandwidth of the baseband signal is the narrowest.

[0089] S2.3. According to the properties of trigonometric functions, the multivariate nonlinear frequency modulation modes can be expressed by the following formula:

[0090] Reconstruct the formula to obtain:

[0091]

[0092] Among them, u(t) and v(t) are two demodulated signals, which can be expressed as:

[0093]

[0094] S2.4, set the number of modes to Q, the number of channels to M, and the multivariate nonlinear frequency modulation mode decomposition to obtain the input signal Decompose the combination of Q multivariate nonlinear frequency modulation modes in such a way that the sum of their bandwidths is minimized. In order to achieve this goal, the objective function of multivariate nonlinear frequency modulation mode decomposition can be expressed as:

[0095]

[0096] Wherein, q and m represent the modal index and channel index respectively, q=1,2,...,Q, m=1,2,...,M. To reconstruct the input signal x of the mth channel m The signal of the multivariate nonlinear frequency modulation mode combination of the qth mode. q ' ,m (t),v' q ' ,m (t) are two demodulated signals, smoothing function Used to estimate the instantaneous frequency of multivariate nonlinear frequency modulation modes. m is a preset minimum value.

[0097] S2.5. In order to ensure the accuracy and reliability of oscillation analysis, it is necessary to eliminate the false modes that may be generated by multivariate nonlinear frequency modulation mode decomposition. q,m With the original signal x m The correlation between them is usually low, so it is an effective method to evaluate the correlation between them by calculating the correlation coefficient between them. The correlation coefficient can be calculated as follows:

[0098]

[0099] Among them, cov(·,·) and std(·) represent covariance and standard deviation respectively. Accordingly, the normalized correlation coefficient can be calculated by the following formula:

[0100]

[0101] max q (·) represents the maximum value. The threshold value T of the normalized correlation coefficient is θ Set it to 0.4, that is, determine θ q,m ≤T θ g q,m This threshold setting is designed to effectively retain key oscillation modes while preventing erroneous removal of key components in control loop signals with multiple oscillation characteristics.

[0102] S2.6, the zero-crossing regularity index of the autocovariance function can be used to identify time-invariant and time-varying oscillation modes. q,m The zero-crossing sequence If the standard deviation of the sequence σ t,q,m Less than its average value μ t,q,m If the mode is one third of the original mode, it can be determined that the mode has oscillation characteristics. Based on this criterion, the zero crossing regularity index of the autocovariance function can be defined as:

[0103]

[0104] This indicator reveals the variability of the zero-crossing sequence relative to its mean. Set the threshold T r is 1, when r q,m >T r When the mode g q,m Oscillating nature.

[0105] Furthermore, the step S3 includes the following steps:

[0106] S3.1, the oscillation detection result can be expressed by the following oscillation matrix:

[0107]

[0108] Among them,q,m If and only if the event ((θ q,m >T θ )&(r q,m >T r )) is true, which means that g q,m Neither is it a spurious mode nor does it actually exhibit oscillatory behavior.

[0109] S3.2. To facilitate subsequent root cause diagnosis, oscillation modes with similar frequencies need to be grouped. The specific process is as follows:

[0110] G q = {g q,m}(o q,m =1)

[0111] S3.3, for each oscillation mode group G with similar frequency q Perform independent oscillation diagnosis based on multivariate Granger causality analysis to accurately obtain root cause analysis results involving multiple oscillation phenomena. Assume that p q,i,j Represents the oscillation signal g q,i and g q,j The p-value obtained by the Granger causality F test is: q,j For g q,i Granger causality:

[0112] p q,i,j <1-α

[0113] Here, α represents the confidence level.

[0114] S3.4. By evaluating the phase correlation between different modes in the same modal group, the relative time delay between the signals can be identified to optimize the diagnostic results. Given two oscillating signals g q,i and g q,j , first, transform the two signals by Fourier transform (FFT) to obtain their spectral representation:

[0115] X q,i (f) = FFT(g q,i )

[0116] X q,j (f) = FFT(g q,j )

[0117] The phase correlation of these two signals is calculated using the following formula:

[0118]

[0119] in, Represents gq,j The conjugate of the FFT result of ; IFFT(·) stands for inverse Fourier transform. Finally, by finding the maximum value in the phase correlation result, the signal g can be determined q,i Relative to g q,j The time delay is expressed as follows:

[0120]

[0121] in, Indicates the index where the maximum phase correlation is located, which can be used to estimate the time delay between two signals, and thus indirectly estimate the phase difference. Set the threshold is 5, when it satisfies When g q,j Prior to g in time q,i , i.e. g q,i For g q,j There is no time causality. The whole process uses the spectral information of the signals to estimate the phase difference between them. By calculating the Fourier transform of the two signals, then multiplying the FFT of one signal by the conjugate FFT of the other signal, and finally obtaining the phase correlation through the inverse Fourier transform, the relative delay between the signals is determined from its maximum value. By considering the time-related phase difference information, the multivariate Granger causality analysis can be effectively "pruned", thereby improving the accuracy of oscillation diagnosis.

[0122] S3.5, for two oscillating signals g q,i and g q,j ,event If it is true, then g q,j For g q,i There is a causal effect, that is, g q,i The oscillation of g q,j cause.

[0123] (III) Beneficial effects

[0124] The above-mentioned device cascade process system oscillation detection and diagnosis method of the present invention has the following advantages:

[0125] (1) Multivariate nonlinear frequency modulation mode decomposition can effectively extract the time-invariant and time-varying oscillation modes in the device cascade process and achieve inter-modal alignment, which is particularly suitable for complex multi-system oscillation diagnosis in industrial processes;

[0126] (2) By calculating the correlation coefficient between the false mode and the original signal and the zero-crossing regularity index of the autocovariance function of each mode, the misleading modes that may be generated by the multivariate nonlinear frequency modulation mode decomposition can be effectively eliminated to ensure the accuracy and reliability of the oscillation analysis;

[0127] (3) By introducing an adjustment method based on the phase information of trigonometric functions, the traditional multivariate Granger causality analysis results are optimized, and the modal features extracted by multivariate nonlinear frequency modulation modal decomposition are fully utilized to improve the accuracy of system oscillation diagnosis.

[0128] In at least one embodiment, Figure 1 The aluminum oxide evaporation process shown is a typical cascade process. Figure 1 The specific process flow of the process shown adopts the seven-effect five-flash two-stage method. The evaporation process consists of 7 evaporators, 7 preheaters, 6 flash evaporators (including 1 raw liquid flash evaporator) and several condensed water tanks. By supplying steam to the outside of the heating tube of the evaporator, the mother liquor inside the heating tube is indirectly heated to boiling, thereby achieving concentration. The concentrated mother liquor is discharged through the discharge port at the bottom of the evaporation chamber, and the separated secondary steam is released through the steam port to provide a heat source for the next-effect evaporator. According to the direction of material flow, the evaporation process can be subdivided into material flow, steam flow and water flow. The following mainly introduces the first two: ① Material flow: With the help of the feed pump, a part of the evaporated raw liquid first enters the five-effect evaporator, and then passes through the four-to-one-effect evaporator and the one-to-five-stage flash evaporator in turn. The last part of the high-concentration mother liquor is directly discharged, and the remaining part of the mother liquor flows into the blending tank; the other part of the evaporated raw liquid first enters the raw liquid flash evaporator, then passes through the seven-to-six-effect evaporators in turn, and finally flows into the blending tank, and is blended with part of the raw liquid and alkali solution to form a qualified mother liquor. ② Steam process: New steam enters the heating chamber of the first-effect evaporator to heat the material and promote its evaporation, and the secondary steam generated by the first-effect evaporator is then transported to the heating chamber of the second-effect evaporator to further heat the material and achieve evaporation; similarly, the secondary steam generated by the second-effect evaporator is introduced into the third-effect heating chamber; the secondary steam generated by the third-effect evaporator is introduced into the fourth-effect heating chamber; the secondary steam generated by the fourth-effect evaporator is introduced into the fifth-effect heating chamber; the secondary steam generated by the fifth-effect evaporator is introduced into the sixth-effect heating chamber; the secondary steam generated by the sixth-effect evaporator is introduced into the seventh-effect heating chamber; the secondary steam generated by the seventh-effect evaporator enters the water cooler for condensation; the secondary steam generated by the first to fifth-stage flash evaporators enter the third to seventh-effect heating chambers respectively for final exhaust steam utilization.

[0129] For the aluminum oxide evaporation process, a device cascade process system oscillation detection and diagnosis method based on multivariate nonlinear frequency modulation mode decomposition is provided, specifically, Figure 2 As shown, the following steps are included:

[0130] S1. Data preparation and preprocessing: Online data of alumina evaporation process is collected and preprocessed by Min-Max normalization. Considering the effect and practical operability of fault diagnosis, process variables are initially divided into three categories: output variables, input variables and intermediate variables. Specifically, the output variables of the first sub-block include variables 1, 2, 3, 4, 13, 44, and 45; the input variables of the second sub-block include variables 5, 6, 7, 8, 9, 10, 11, 12, and 29. After determining the two sub-blocks of output variables and input variables, the remaining 29 intermediate variables are subdivided into three sub-blocks by mutual information K-means clustering method, namely, the third sub-block includes variables 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28; the fourth sub-block includes variables 30, 31, 32, 33, 34, 35, 36, 37, 38; the fifth sub-block includes variables 39, 40, 41, 42, 43. The fourth sub-block is selected to apply the system oscillation diagnosis method based on multivariate nonlinear frequency modulation mode decomposition. The sub-block 4 data set contains 400 samples and 9 variables, and the sampling interval is 2 minutes.

[0131] S2, oscillation detection stage: The result of performing multivariate signal decomposition on sub-block 4 by multivariate nonlinear frequency modulation mode decomposition method is as follows Figure 3 , Figure 4 and Figure 5 Then, the correlation coefficient and the autocovariance function zero-crossing regularity index are used to detect the oscillation. Figure 6 The analysis shows that there is an obvious oscillation mode g2 in the data. However, mode g1 reflects the trend of the original variable, and mode g3 is a false mode or a noise mode, which are not oscillation modes. In addition, the variable x representing the steam chamber pressure of the six-effect and seven-effect evaporators 31 and x 38 No oscillation phenomenon is shown, indicating that these pressure variables relatively close to the new steam input maintain a relatively stable state;

[0132] S3, Oscillation diagnosis stage: Figure 7 As shown in the figure, based on the grouping, the single multivariate Granger causality analysis leads to many false causal relationships because it does not consider time-related information. According to the background knowledge of the material flow of the alumina evaporation process, a part of the evaporation stock first enters the stock flash evaporator, then passes through the seventh effect to the sixth effect evaporator in turn, and finally flows to the blending tank. Based on this, the relevant variable x of the sixth effect evaporator 30 and x 32 The influence on other variables is extremely limited. However, the simple multivariate Granger causality analysis misjudged the temperature x before the heat exchange of the six-effect raw liquid. 32 Will affect the raw liquid flash evaporator feed flow x33 、The discharge temperature of the raw liquid flash evaporator x 34 And the steam pressure of the raw liquid flash evaporator x 35 , which is contrary to the actual material flow. This type of misjudgment is mainly due to the fact that multivariate Granger causality analysis only starts from the perspective of prediction error and ignores the key time-related phase difference information. After introducing phase correlation analysis, these unreasonable causal relationships can be effectively eliminated, and the wrong causal inference can be greatly reduced, thereby significantly improving the accuracy of oscillation diagnosis.

[0133] The final oscillation diagnosis result is as follows: Figure 8 As shown, the feed flow rate of the raw liquid flash evaporator x 33 、The temperature of the seven-effect raw liquid before heat exchange x 36 And the steam temperature of the seven-effect evaporator x 37 It is the root cause of system oscillation. In addition, the steam temperature of the six-effect evaporator x 30 、6-effect stock solution temperature before heat exchange x 32 The variables such as temperature and pressure fluctuate due to the oscillation of the above-mentioned root causes, which is consistent with the actual material and energy flow characteristics of the alumina evaporation process. By locating the oscillation source at a specific process link and equipment, not only the starting point of the oscillation phenomenon is accurately captured, but also its propagation path and influence in the entire evaporation system can be understood, providing a strong basis for further optimization of operations and fault prevention.

[0134] In summary, the above-mentioned oscillation detection and diagnosis method of the device cascade process system based on multivariate nonlinear frequency modulation mode decomposition has the following advantages:

[0135] (1) Multivariate nonlinear frequency modulation mode decomposition can effectively extract the time-invariant and time-varying oscillation modes in the device cascade process and achieve inter-modal alignment, which is particularly suitable for complex multi-system oscillation diagnosis in industrial processes;

[0136] (2) By calculating the correlation coefficient between the false mode and the original signal and the zero-crossing regularity index of the autocovariance function of each mode, the misleading modes that may be generated by the multivariate nonlinear frequency modulation mode decomposition can be effectively eliminated to ensure the accuracy and reliability of the oscillation analysis;

[0137] (3) By introducing an adjustment method based on the phase information of trigonometric functions, the traditional multivariate Granger causality analysis results are optimized, and the modal features extracted by multivariate nonlinear frequency modulation modal decomposition are fully utilized to improve the accuracy of system oscillation diagnosis.

[0138] The present invention also provides a device, comprising a memory, a control processor, and a computer program stored in the memory and executable on the control processor, wherein the control processor executes the program to implement the aforementioned device cascade process system oscillation detection and diagnosis method based on multivariate nonlinear frequency modulation modal decomposition.

[0139] The present invention also provides a computer-readable storage medium, which stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to implement the aforementioned device cascade process system oscillation detection and diagnosis method based on multivariate nonlinear frequency modulation modal decomposition.

[0140] Although the above methods are illustrated and described as a series of actions for simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of the actions, because according to one or more embodiments, some actions may occur in different orders and / or concurrently with other actions from the illustrations and descriptions herein or not illustrated and described herein but understandable to those skilled in the art. It will be further appreciated by those skilled in the art that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. To clearly explain this interchangeability of hardware and software, various illustrative components, frames, modules, circuits, and steps are generally described in the form of their functionality above. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. The technical staff can implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention. The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in an alternative, the processor may be any conventional processor, a battery compartment control board, a micro-battery compartment control board, or a state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration. The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, the storage medium may be integrated into the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components. In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on a computer-readable medium or transmitted therefrom as one or more instructions or codes.Computer-readable media include both computer storage media and communication media, including any media that facilitates a computer program to be transferred from one place to another. Storage media can be any available media that can be accessed by a computer. As an example and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage devices, or any other media that can be used to carry or store the agreed program code in the form of instructions or data structures and can be accessed by a computer. Any connection is also properly referred to as computer-readable media. For example, if software is transmitted from a web site, a central control computer, or other remote sources using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0141] Although the above methods are illustrated and described as a series of actions for simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of the actions, because according to one or more embodiments, some actions may occur in a different order and / or concurrently with other actions from those illustrated and described herein or not illustrated and described herein but understandable to those skilled in the art.

[0142] 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 spirit and principle of the present invention should be included in the protection scope of the present invention.

[0143] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the embodiments of the present invention are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations shall fall within the scope defined by the appended claims.

Claims

1. A method for oscillation detection and diagnosis of a device cascade process system based on multivariate nonlinear frequency modulation mode decomposition, characterized in that: The following steps are involved: Step S1, data preparation and preprocessing: Collect the online data x of the device cascade process and use Min-Max normalization preprocessing; Step S2, oscillation detection: The multivariate nonlinear frequency modulation mode decomposition method is used to decompose the normalized online data x, extract the time-invariant and time-varying oscillation modes, and calculate the correlation coefficient θ between each mode and the original signal. q,m , remove false modes with low correlation, and calculate the zero-crossing regularity index r of the autocovariance function q,m , identify the true oscillation mode; Step S3, oscillation diagnosis: After the oscillation modes are identified, the oscillation modes with similar frequencies are grouped. For each oscillation mode group G q , performs oscillation diagnosis based on multivariate Granger causality analysis, based on phase correlation information The results of multivariate Granger causality analysis are optimized to eliminate irrelevant causal relationships and accurately locate the oscillation source.

2. The oscillation detection and diagnosis method of the device cascade process system based on multivariate nonlinear frequency modulation mode decomposition according to claim 1 is characterized in that: The signal decomposition described in step S2 extracts the time-invariant and time-varying oscillation modes, specifically: A multivariate AM / FM signal with M channels is expressed as follows: Among them, a m (t), f m and φ m Represent the instantaneous amplitude, instantaneous frequency and initial phase of the mth component respectively; The simplified analytical expression of the signal g(t), that is, the analytical form of the multivariate nonlinear frequency modulation mode, can be expressed as: Among them, f(t) and a(t) are the instantaneous frequency and instantaneous amplitude of the current multivariate nonlinear frequency modulation mode, respectively, and j represents the imaginary unit in complex algebra. The core of multivariate nonlinear frequency modulation mode decomposition is to find a smooth function To estimate the instantaneous frequency of the multivariate nonlinear frequency modulation mode and ensure the narrowest bandwidth of the baseband signal; According to the properties of trigonometric functions, the multivariate nonlinear frequency modulation mode can be reconstructed by the following formula: Among them, u(t) and v(t) are two demodulated signals, which can be expressed as: Assuming the number of modes is Q and the number of channels is M, the multivariate nonlinear frequency modulation mode decomposition aims to extract Decompose the combination of Q multivariate nonlinear frequency modulation modes so that the sum of their bandwidths is minimized. The objective function of multivariate nonlinear frequency modulation mode decomposition can be expressed as: Where q and m represent the modal index and channel index respectively, q = 1, 2, ..., Q, m = 1, 2, ..., M; u" q,m (t),v” q,m (t), To reconstruct the input signal x of the mth channel m The signal of the multivariate nonlinear frequency modulation mode combination of the qth mode; where u” q,m (t),v” q,m (t) are two demodulated signals, smoothing function Used to estimate the instantaneous frequency of multivariate nonlinear frequency modulation modes; ε m is a preset minimum value.

3. The oscillation detection and diagnosis method of the device cascade process system based on multivariate nonlinear frequency modulation mode decomposition according to claim 2 is characterized in that: The correlation coefficient θ between each mode and the original signal is calculated as described in step S2 q,m , remove the false modes with low correlation, specifically: remove the false modes that may be generated by multivariate nonlinear frequency modulation mode decomposition, the false modes g q,m With the original signal x m The correlation between them is usually low, and the correlation between them is evaluated by calculating the correlation coefficient between them. The correlation coefficient can be calculated as follows: Among them, cov(·,·) and std(·) represent covariance and standard deviation respectively; normalized correlation coefficient θ q,m It can be calculated by the following formula: max q (·) represents the maximum value; the threshold of the normalized correlation coefficient is set to T θ , that is, to determine θ q,m ≤T θ g q,m False mode.

4. The oscillation detection and diagnosis method of the device cascade process system based on multivariate nonlinear frequency modulation mode decomposition according to claim 3 is characterized in that: The calculation of the autocovariance function zero crossing regularity index r described in step S2 q,m , identify the true oscillation mode, specifically: the zero-crossing regularity index of the autocovariance function can be used to identify time-invariant and time-varying oscillation modes. For mode g q,m The zero-crossing sequence If the standard deviation of the sequence σ t,q,m Less than its average value μ t,q,m One third of , it can be determined that the mode has oscillation characteristics. Based on this criterion, the zero crossing regularity index of the autocovariance function can be defined as: This indicator reveals the variability of the zero-crossing sequence relative to its mean, and the threshold is set to T r , when r is satisfied q,m >T r When the mode g q,m Oscillating nature.

5. The method for detecting and diagnosing oscillation of a cascaded process system based on multivariate nonlinear frequency modulation mode decomposition according to claim 4, characterized in that: In step S2, the threshold value T of the normalized correlation coefficient is θ is 0.4; the threshold T in the zero-crossing regularity index of the autocovariance function r is 1.

6. The oscillation detection and diagnosis method of the device cascade process system based on multivariate nonlinear frequency modulation mode decomposition according to claim 4 or 5, characterized in that: The oscillation diagnosis described in step S3 includes the following steps: Step S3.1, the oscillation detection result can be expressed by the following oscillation matrix: Among them, q,m If and only if the event ((θ q,m >T θ )&(r q,m >T r )) is true, which means that g q,m Neither is it a spurious mode nor does it actually exhibit oscillatory behavior; Step S3.2, grouping oscillation modes with similar frequencies, the specific process is as follows: G q ={g q,m }(o q,m =1) For each oscillation mode group G with similar frequency q Perform independent oscillation diagnosis based on multivariate Granger causality analysis to accurately obtain the root cause analysis results of multiple oscillation phenomena. Assume that p q,i,j Represents the oscillation signal g q,i and g q,j The p-value obtained by the Granger causality F test is considered to be q,j For g q,i Granger causality: p q,i,j ＜1-α Among them, α represents the confidence level; Step S3.3, by evaluating the phase correlation between different modes in the same mode group, the relative time delay between the signals is identified to optimize the diagnosis result; given two oscillating signals g q,i and g q,j , these two signals are transformed by Fourier transform FFT to obtain their spectral representation: X q,i (f)=FFT(g q,i ) X q,j (f)=FFT(g q,j ) The phase correlation of these two signals is calculated using the following formula: in, Represents g q,j The conjugate of the FFT result of ; IFFT(·) stands for inverse Fourier transform. Finally, by finding the maximum value in the phase correlation result, the signal g can be determined q,i Relative to g q,j The time delay is expressed as follows: in, Represents the index where the maximum phase correlation is located, which can be used to estimate the time delay between two signals, and thus indirectly estimate the phase difference; set the threshold to When satisfied When g q,j Prior to g in time q,i , i.e. g q,i For g q,j There is no temporal causal action; For two oscillating signals g q,i and g q,j ,event If it is true, then g q,j For g q,i There is a causal effect, that is, g q,i The oscillation of g q,j cause.

7. The method for detecting and diagnosing oscillation of a cascaded process system based on multivariate nonlinear frequency modulation mode decomposition according to claim 6, characterized in that: In step S3, the threshold is set is 5.

8. A device, characterized in that: It includes a memory, a control processor and a computer program stored in the memory and executable on the control processor, wherein the control processor executes the program to implement the oscillation detection and diagnosis method for the device cascade process system based on multivariate nonlinear frequency modulation modal decomposition as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to implement the oscillation detection and diagnosis method for the device cascade process system based on multivariate nonlinear frequency modulation mode decomposition as described in any one of claims 1-7.

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