CN111538309A - Industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition - Google Patents

Industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition Download PDF

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CN111538309A
CN111538309A CN202010258014.9A CN202010258014A CN111538309A CN 111538309 A CN111538309 A CN 111538309A CN 202010258014 A CN202010258014 A CN 202010258014A CN 111538309 A CN111538309 A CN 111538309A
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谢磊
陈启明
费新怡
吴小菲
苏宏业
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Zhejiang University ZJU
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Abstract

The invention discloses an industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition, which comprises the following steps: (1) collecting output signals of a plurality of industrial process control loops to be detected; (2) decomposing the acquired signals by using a multivariate nonlinear frequency modulation modal decomposition method; (3) calculating a normalized correlation coefficient of the mode obtained by decomposition, and reserving the mode with a larger normalized correlation coefficient; (4) and estimating the oscillation period of the reserved mode by using the zero crossing point to analyze the plant-level oscillation condition of the industrial process. The invention can improve the accuracy and reliability of industrial process plant-level oscillation detection, provide data support for performance evaluation and fault diagnosis and lay a foundation for subsequent oscillation tracing work.

Description

Industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition
Technical Field
The invention belongs to the field of performance evaluation and fault diagnosis in an industrial control system, and particularly relates to an industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition.
Background
With the rapid development of fault diagnosis and performance evaluation techniques for industrial control loops, oscillation detection is one of the main tasks of fault diagnosis for control loops.
Due to the connection and coupling between the control system loops, the oscillations generated in one loop will generally propagate to the other loops, forming plant-level oscillations. These oscillations can cause product quality fluctuations, energy and raw material waste, and even threaten the safe and stable operation of the whole project. Therefore, the method has important significance for improving the production safety and the economic benefit by timely and accurately detecting the plant-level oscillation in the industrial process production control system.
The traditional plant-level oscillation detection method, such as a spectrum principal component analysis method, a Fourier spectrum method and the like, requires that data is performed under the stable and linear condition. However, in the actual industrial production, strictly linear and stable data does not exist, and the acquired data is often accompanied by nonlinearity, non-stability, strong noise and the like. These conventional methods are therefore of limited utility.
In recent years, signal decomposition techniques have been developed rapidly, and research for applying these new signal processing techniques to processing data of an industrial control system has been underway. For example, Bahji et al first detected and diagnosed nonlinear oscillations in industrial process control system data using empirical mode decomposition. However, most of the methods are limited to processing single variable signals at present, are suitable for detection of single-loop oscillation signals, do not consider the relationship among multi-loop signals, and are therefore not suitable for plant-level oscillation detection. Recently, Aftab et al introduced multivariate empirical mode decomposition into the field of oscillation detection. However, the multivariate empirical mode decomposition method is easily interfered by sampling rate and noise, and the decomposition performance is unstable.
Therefore, it is necessary to design a new multivariate signal decomposition technology to detect the plant-level oscillation more accurately.
Disclosure of Invention
The invention provides an industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition, which has high detection precision, only needs to obtain conventional operation data, and does not need process mechanism knowledge.
An industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition comprises the following steps:
(1) collecting output signals of a plurality of industrial process control loops to be detected;
(2) decomposing the acquired signals by a multivariate nonlinear frequency modulation modal decomposition method;
(3) calculating a normalized correlation coefficient of the mode obtained by decomposition, and reserving the mode with the normalized correlation coefficient larger than a preset value;
(4) and for the reserved mode, estimating the oscillation period of the mode by adopting a zero crossing point, and analyzing the plant-level oscillation condition of the industrial process according to the oscillation period.
The invention fully utilizes the advantages of the non-linear frequency modulation modal decomposition processing of non-stable and non-linear multivariable data, can improve the plant-level oscillation detection accuracy and reliability of the control loop of the industrial process, provides data support for performance evaluation and fault diagnosis, and lays a foundation for subsequent oscillation tracing work.
The invention directly adopts measurable variables of the process control loop as process output signals, and all process output signals to be detected are acquired in real time on site.
The specific steps of the step (1) are as follows: the process data in the control loop to be detected is recorded in each preset sampling interval, and the process data collected in each sampling interval is added to the tail end of the process data collected previously.
The specific steps of the step (2) are as follows:
(2-1) constructing an objective function of multivariate nonlinear frequency modulation modal decomposition:
Figure BDA0002438176760000031
Figure BDA0002438176760000032
wherein x ism＝[xm(t0),...,xm(tN-1)]TRepresents the m-th loop acquiredThe obtained signals are collected by M loops in total; i represents the ith layer mode, and Q layer modes are provided in total;mrepresents the reconstruction error of the mth loop; t is t0,...,tN-1Represents a sampling instant;
Figure BDA0002438176760000033
Figure BDA0002438176760000034
Figure BDA0002438176760000035
is the estimated instantaneous frequency of the i-th layer mode,
Figure BDA0002438176760000036
Figure BDA0002438176760000037
ai,m(t) is the instantaneous amplitude of the mth loop, i layer mode, fiIs the instantaneous frequency of the true i-th layer mode, phi denotes the initial phase, j denotes the complex unit,
Figure BDA0002438176760000038
is a second order difference matrix;
(2-2) iterative solution of the objective function in (2-1) by using an alternating direction multiplier method, and iteration is carried out until the final step
Figure BDA0002438176760000039
As the true instantaneous frequency fiWhile obtaining Ai，Bi，ui,mAnd vi,mWherein i 1,.. Q, M1,. M;
(2-3) the m-th loop, i-th layer mode is gi,m＝Aiui,m+Bivi,mWherein i 1,.. Q, M1,.. M.
In the step (3), the calculation formula of the normalized correlation coefficient of the mode obtained by decomposition is as follows:
Figure BDA0002438176760000041
therein, ζi,mA normalized correlation coefficient representing the mth loop, the ith layer mode; rhoi,mRepresents a mode g obtained by decompositioni,mAnd the collected loop output signal xmA correlation coefficient between;
Figure BDA0002438176760000042
wherein cov (-) represents covariance, std (-) represents standard deviation.
In the step (3), the preset value is 0.35, if ζ isi,mIf the amplitude is more than 0.35, the m-th loop and the i-th layer mode g are reservedi,m。
In the step (4), the formula of the oscillation period of the zero crossing point estimation mode is as follows:
Figure BDA0002438176760000043
where T is the oscillation period, Δ T is the interval of two consecutive zero crossings of the mode, and n is generally set to 11 by default.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, external additional signal excitation is not needed when the signal is collected, additional disturbance is not introduced to the control system, and non-invasive detection can be realized.
2. The multivariate nonlinear frequency modulation modal decomposition adopted by the invention is suitable for nonlinear and non-stable multivariate signal processing.
3. Compared with the plant-level oscillation detection method based on Fourier spectrum, multivariate empirical mode decomposition, multivariate intrinsic time scale decomposition and the like, the plant-level oscillation detection method based on the multivariate nonlinear frequency modulation modal decomposition in the industrial process has the advantages of complete theoretical basis and accurate detection.
4. The invention can carry out quantitative index detection on the oscillation behavior of each loop in the industrial process, and provides abundant data support for the evaluation of the performance of the loop to be detected and the diagnosis of a fault source.
5. The invention completely adopts a data driving type method, does not need prior process knowledge and does not need manual intervention.
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FIG. 1 is a schematic flow chart of an industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition according to the present invention;
FIG. 2 is a diagram of a plant-level oscillation output signal of an industrial process to be detected, which is collected according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating a mode decomposed by multivariate nonlinear frequency modulation mode in an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings and examples, which are intended to facilitate the understanding of the invention without limiting it in any way.
In this embodiment, a certain float beneficiation process control system is taken as an example to perform industrial process plant-level oscillation detection based on multivariate nonlinear frequency modulation modal decomposition, and data are derived from Lindner B, Chioua M, Groenewald J W D, and et.
As shown in fig. 1, a method for detecting factory level oscillation of an industrial process based on multivariate nonlinear frequency modulation modal decomposition includes the following steps:
step 1, collecting output signals of a plurality of industrial process control loops to be detected.
The method for acquiring the process output signal comprises the following steps: the process data in the control loop to be detected is recorded in each preset sampling interval, and the process data collected in each sampling interval is added to the tail end of the process data collected previously.
The sampling interval refers to the sampling interval of the performance evaluation system. The process data is continuously updated over time, with new process data added to the end of the previously acquired process data for each length of time that a sampling interval has elapsed. The sampling interval of the performance evaluation system is generally the same as the control period in the industrial control system, and can also be selected as an integral multiple of the control period, and is specifically determined according to the real-time requirements and data storage capacity limitations of performance monitoring and industrial sites.
The raw data of the process output signal collected in this embodiment is shown in fig. 2, where the abscissa in fig. 2 is time, the unit is second, and the ordinate is liquid level.
And 2, decomposing the loop output signal by using a multivariate nonlinear frequency modulation modal decomposition method.
The principle of the decomposition method is as follows:
step 2-1, the objective function of the multivariate nonlinear frequency modulation modal decomposition method is
Figure BDA0002438176760000061
Figure BDA0002438176760000062
Wherein xm＝[xm(t0),...,xm(tN-1)]TRepresenting the signals acquired by the M loop, and acquiring M loops in total, wherein i represents the i layer mode and Q layer mode in total,mrepresents the reconstruction error of the mth loop, t ═ t0,...,tN-1Which is indicative of the time of the sampling,
Figure BDA0002438176760000063
Figure BDA0002438176760000064
Figure BDA0002438176760000065
is the estimated instantaneous frequency of the i-th layer mode,
Figure BDA0002438176760000066
Figure BDA0002438176760000067
ai,m(t) is the instantaneous amplitude of the mth loop, i layer mode, fiIs the instantaneous frequency of the true i-th layer mode, phi denotes the initial phase, j denotes the complex unit,
Figure BDA0002438176760000068
is a second order difference matrix;
step 2-2, the objective function in the step 2-1 is solved by iteration through an alternating direction multiplier method, and the objective function is obtained from the iteration to the last step
Figure BDA0002438176760000069
As the true instantaneous frequency fiWhile obtaining Ai，Bi，ui,mAnd vi,mWherein i 1,.. Q, M1,. M;
step 2-3, the m-th loop and the i-th layer obtained by decomposition have the mode gi,m＝Aiui,m+Bivi,mWherein i 1., Q, M1, …, M.
The resulting mode after final decomposition is shown in fig. 3.
Step 3, calculating the normalized correlation coefficient of the mode obtained by decomposition, and as shown in table 1, reserving the mode with the normalized correlation coefficient larger than 0.35, namely reserving a loop x1-x6First layer mode and loop x of7-x8The second layer mode of (3).
TABLE 1
Loop circuit x1 x2 x3 x4 x5 x6 x7 x8 x9
Layer 1 1 1 1 1 1 1 0.277 0.317 0.238
Layer 2 0.164 0.175 0.217 0.251 0.030 0.157 1 1 1
And 4, estimating the oscillation period of the reserved mode by using the zero crossing point.
As shown in Table 2 (unit: second), it can be observed that loop x is formed1-x6Has a highly uniform oscillation period, loop x7-x8The second layer oscillation period of (a) is also highly uniform. From a priori knowledge, loop x1-x6And x7-x8Station-level oscillation with periods of 465 seconds and 266 seconds respectively exists, and it can be found that the oscillation period obtained by the method is highly consistent with the prior knowledge.
TABLE 2
Loop circuit x1 x2 x3 x4 x5 x6 x7 x8 x9
Layer 1 464 458 460 454 452 454 - - -
Layer 2 - - - - - - 258 258 262
Therefore, the method provided by the invention accurately detects the plant-level oscillation existing in the industrial process, and the detected quantitative result can also provide rich data support for subsequent oscillation diagnosis and oscillation tracing work.
The embodiments described above are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions and equivalents made within the scope of the principles of the present invention should be included in the scope of the present invention.

Claims (6)

1. An industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition is characterized by comprising the following steps:
(1) collecting output signals of a plurality of industrial process control loops to be detected;
(2) decomposing the acquired signals by a multivariate nonlinear frequency modulation modal decomposition method;
(3) calculating a normalized correlation coefficient of the mode obtained by decomposition, and reserving the mode with the normalized correlation coefficient larger than a preset value;
(4) and for the reserved mode, estimating the oscillation period of the mode by adopting a zero crossing point, and analyzing the plant-level oscillation condition of the industrial process according to the oscillation period.
2. The industrial process plant-level oscillation detection method based on multivariate nonlinear frequency modulation modal decomposition according to claim 1, characterized in that the specific steps of the step (1) are as follows: the process data in the control loop to be detected is recorded in each preset sampling interval, and the process data collected in each sampling interval is added to the tail end of the process data collected previously.
3. The method for detecting the factory-level oscillation of the industrial process based on the multivariate nonlinear frequency modulation modal decomposition according to claim 1, wherein the specific steps of the step (2) are as follows:
(2-1) constructing an objective function of multivariate nonlinear frequency modulation modal decomposition:
Figure FDA0002438176750000011
wherein x ism＝[xm(t0),…,xm(tN-1)]TRepresenting the signals acquired by the mth loop, and acquiring M loops in total; i represents the ith layer mode, and Q layer modes are provided in total;mrepresents the reconstruction error of the mth loop; t is t0,...,tN-1Represents a sampling instant;
Figure FDA0002438176750000012
Figure FDA0002438176750000021
Figure FDA0002438176750000022
is the estimated instantaneous frequency of the i-th layer mode,
Figure FDA0002438176750000023
Figure FDA0002438176750000024
ai,m(t) is the instantaneous amplitude of the mth loop, i layer mode, fiIs the instantaneous frequency of the true i-th layer mode, phi denotes the initial phase, j denotes the complex unit,
Figure FDA0002438176750000025
is a second order difference matrix;
(2-2) iterative solution of the objective function in (2-1) by using an alternating direction multiplier method, and iteration is carried out until the final step
Figure FDA0002438176750000026
As the true instantaneous frequency fiWhile obtaining Ai，Bi，ui,mAnd vi,mWherein i 1,.. Q, M1,. M;
(2-3) the m-th loop, i-th layer mode is gi,m＝Aiui,m+Bivi,mWherein i 1,.. Q, M1,.. M.
4. The method for detecting factory-level oscillation of industrial process based on multivariate nonlinear frequency modulation modal decomposition according to claim 1, wherein in the step (3), the calculation formula of the normalized correlation coefficient of the modal obtained by decomposition is as follows:
Figure FDA0002438176750000027
therein, ζi,mRepresenting the m-th loop, i-th modeNormalizing the correlation coefficient; rhoi,mRepresents a mode g obtained by decompositioni,mAnd the collected loop output signal xmA correlation coefficient between;
Figure FDA0002438176750000028
wherein cov (-) represents covariance, std (-) represents standard deviation.
5. The method for detecting factory level oscillation of industrial process based on multivariate nonlinear frequency modulation modal decomposition according to claim 4, wherein in the step (3), the preset value is 0.35, if ζ isi,mIf the amplitude is more than 0.35, the m-th loop and the i-th layer mode g are reservedi,m。
6. The method for detecting factory-level oscillation of industrial process based on multivariate nonlinear frequency modulation modal decomposition as claimed in claim 1, wherein in the step (4), the formula of the oscillation period of the zero-crossing point estimation mode is as follows:
Figure FDA0002438176750000031
wherein, T is the oscillation period, Δ T is the interval of two consecutive zero-crossing points of the mode, and n is set to be 11 by default.
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CN112182773A (en) * 2020-10-16 2021-01-05 北京航天自动控制研究所 Online identification method for aircraft steering engine fault based on linear frequency modulation Z transformation
CN112925290A (en) * 2021-01-22 2021-06-08 浙江大学 Plant-level oscillation detection method based on multivariate intrinsic chirp modal decomposition
CN113589795A (en) * 2021-08-02 2021-11-02 湖州师范学院 Multi-oscillation detection method based on intelligent optimization nonlinear chirp modal decomposition algorithm

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