PCCP (prestressed concrete cylinder pipe) broken wire monitoring and pipe explosion early warning method and system

Abstract

The invention discloses a PCCP (prestressed concrete cylinder pipe) broken wire monitoring and pipe explosion early warning method and system, and belongs to the technical field of PCCPs. The PCCP broken wire monitoring and pipe explosion early warning method comprises the following steps that S1, sensors are arranged on the PCCPs; s2, the sensor monitors the PCCP pipe in real time and transmits data to the signal transmission module; s3, the signal transmission module transmits the signal to a signal processing module for data preprocessing; s4, a feature extraction unit extracts frequency domain features and time domain features and extracts the change rate and the strain gradient of the strain; s5, inputting the features into a broken wire recognition model to judge whether a broken wire phenomenon exists or not and determine the number and positions of broken wires; s6, the pipe explosion risk evaluation unit evaluates the pipe explosion risk by using a fuzzy comprehensive evaluation method to obtain a risk level; and S7, when the pipe explosion risk level exceeds a preset threshold value, an early warning unit gives out early warning. According to the PCCP broken wire monitoring and pipe burst early warning method and system, the real-time performance, accuracy and intelligent level of monitoring are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of PCCP pipelines, and in particular to a method and system for monitoring wire breakage and early warning of pipe burst in PCCP pipelines. Background Art

[0002] PCCP (prestressed concrete cylinder pipe) is a vital pipeline used to transport fluids such as water, widely used in water conservancy projects and urban water supply. The safe operation of PCCP pipelines is crucial, and prestressed steel wire breakage is a key factor affecting pipeline safety. If a wire break is not detected and addressed promptly, it can lead to a pipe burst, resulting in serious economic losses and safety accidents.

[0003] Currently, methods for monitoring broken wires in PCCP pipelines are relatively limited, relying primarily on regular manual inspections. This method is inefficient and lacks real-time performance, making it difficult to detect broken wires and provide early warnings. Furthermore, some existing monitoring technologies suffer from low accuracy and reliability, making them unable to meet the practical needs of PCCP pipeline safety monitoring and early warning. Therefore, an efficient and accurate method and system for monitoring broken wires and warning of pipe bursts in PCCP pipelines is urgently needed. Summary of the Invention

[0004] The purpose of the present invention is to provide a PCCP pipeline broken wire monitoring and pipe burst early warning method and system, which improves the real-time, accuracy and intelligence level of monitoring, reduces manual intervention and reduces monitoring costs.

[0005] To achieve the above objectives, the present invention provides a PCCP pipeline broken wire monitoring and pipe burst warning method, comprising the following steps:

[0006] S1. Distributed optical fiber sensors are deployed on the PCCP pipeline, and pressure sensors and temperature sensors are deployed at the inlet and outlet of the PCCP pipeline respectively;

[0007] S2, distributed optical fiber sensors, pressure sensors and temperature sensors monitor the PCCP pipe in real time and transmit the acquired data to the signal transmission module;

[0008] S3, the signal transmission module transmits the signal to the signal processing module for data preprocessing;

[0009] S4, the feature extraction unit performs fast Fourier transform on the preprocessed data, extracts frequency domain features, calculates the peak value, mean value and variance of the vibration signal as time domain features, and extracts the strain change rate and strain gradient of the strain data;

[0010] S5. Input the extracted features into a broken wire recognition model trained based on a support vector machine algorithm to determine whether there is a broken wire phenomenon and determine the number and location of the broken wires;

[0011] S6. The burst risk assessment unit uses the analytic hierarchy process to determine the weight of each factor based on the number and location of broken wires and the internal pressure and temperature of the PCCP pipeline. It then uses the fuzzy comprehensive evaluation method to assess the burst risk and determine the risk level.

[0012] S7. When the pipe burst risk level exceeds a preset threshold, the warning unit issues a sound warning and a light warning.

[0013] Preferably, the data preprocessing operation in S3 is bandpass filtering, and the strain data is amplified.

[0014] Preferably, the fast Fourier transform FFT in S4 specifically includes the following steps:

[0015] S4.1.1. The continuous vibration signal of the PCCP pipeline must first be sampled and quantized to obtain a discrete time-domain signal sequence {x(n)}, where n = 0, 1, ..., N-1, and N is the number of sampling points;

[0016] S4.1.2. Select the radix-2 FFT algorithm to decompose the discrete Fourier transform (DFT) of N points into multiple DFTs of shorter points for calculation; extract the DFT by time. The specific operation of the radix-2 FFT algorithm is as follows:

[0017] The original N-point discrete time domain signal {x(n)} is divided into two groups of subsequences according to the odd or even value of n, namely the even group x even (r) = x(2r) and odd number x odd (r) = x(2r+1), where

[0018] For the two groups of subsequences, DFT calculation of point X even (k) and X odd (k),

[0019] According to the properties of DFT, the DFT results of the two subsequences are combined into the N-point DFT result X(k) of the original sequence using the following formula:

[0020]

[0021] in, is called the twiddle factor, j is the imaginary unit;

[0022] Repeat the above operation in S4.1.2, halving the length of the subsequence each time, until the subsequence length is 1, and finally obtain the complete N-point DFT result, that is, the frequency domain signal {X(k)}, k = 0, 1, ..., N-1;

[0023] S4.1.3. After FFT transformation, the frequency resolution of the frequency domain signal is Among them, f s is the sampling frequency.

[0024] Preferably, the calculation process of the time domain feature in S4 is as follows:

[0025] In the discrete vibration signal sequence {x v (n)}, n=0,1,…,A-1, the element x with the largest value max That is the peak value of the discrete vibration signal. The calculation formula of the mean is as follows:

[0026]

[0027] Where A is the length of the discrete vibration signal sequence, x v (n) is the signal value at the nth moment in the sequence;

[0028] The formula for calculating variance is as follows:

[0029]

[0030] Preferably, the discrete strain data sequence {ε(n)} in S4, n=0, 1, ..., N-1, where N is the number of sampling points, ε(n) represents the strain value at the nth sampling moment, and the strain change rate is calculated as follows:

[0031]

[0032] Among them, Δt is the sampling time interval and the sampling frequency is f s ,but

[0033] The strain gradient is used to describe the change of strain in spatial position and reflects the unevenness of strain distribution. Multiple strain sensors are arranged along the axial or circumferential direction on the PCCP pipeline. The distance between adjacent sensors is Δx. The strain values ​​collected by each sensor are ε1, ε2, …, ε n , the strain gradient calculation formula at the mth position is as follows:

[0034]

[0035] Preferably, the specific operation steps of the fuzzy comprehensive evaluation method in S6 are as follows:

[0036] S6.1. Determine the set of evaluation factors;

[0037] Let U={u1,u2,…,u n} is a set of factors that affect the risk of PCCP pipeline burst, corresponding to the number of broken wires, broken wire location, pipeline internal pressure, and temperature;

[0038] S6.2, determine the evaluation level set;

[0039] Let V = {v1,v2,…,v m} is the level set of evaluation results;

[0040] S6.3, construct fuzzy relationship matrix;

[0041] Through expert scoring or actual data statistics, for each factor u i , i=0,1,…,n, determine its evaluation level v j , the membership degree r of i=0,1,…,m ij , thus constructing the fuzzy relationship matrix R=(r ij ) n×m , membership degree r ij Representation factor u i Belongs to evaluation level v j The degree of , the value range is [0,1];

[0042] S6.4, use the analytic hierarchy process (AHP) to calculate the factors u i The weight vector W relative to the target layer is (w1, w2, ..., w n ), and satisfy

[0043] S6.5, perform fuzzy synthesis operation;

[0044] The fuzzy synthesis operator is used to synthesize the weight vector W and the fuzzy relationship matrix R to obtain the comprehensive evaluation vector B. The calculation formula is: in is the fuzzy composition operator;

[0045] S6.6, Processing of evaluation results;

[0046] The maximum membership principle or weighted average method is used to evaluate the comprehensive evaluation vector B=(b1,b2,…,b m ) for processing,

[0047] Maximum membership principle:

[0048] The membership degree of the kth evaluation level is b k =max{b1,b2,…,b m}, then the corresponding evaluation level vk The final evaluation result;

[0049] Weighted average method:

[0050] Give each evaluation level v j Assign the corresponding score c j , calculate the comprehensive score The pipe burst risk level is determined based on the comprehensive score.

[0051] The present invention provides a PCCP pipeline wire breakage monitoring and pipe burst warning system, which is applied to the above-mentioned PCCP pipeline wire breakage monitoring and pipe burst warning method, including a sensor module, which includes a distributed optical fiber sensor, a pressure sensor, and a temperature sensor for real-time monitoring of the vibration, strain, internal pressure, and temperature data of the PCCP pipeline;

[0052] A signal transmission module, used for transmitting the signal to the signal processing module;

[0053] The data processing module includes a data preprocessing unit, a feature extraction unit, a broken wire identification and location unit, and a pipe burst risk assessment unit. It is used to preprocess the data, extract frequency domain features, time domain features, strain rate, and strain gradient, identify whether broken wires exist, determine the number and location of broken wires, and assess the risk of pipe burst.

[0054] The early warning module includes a sound warning unit and a light warning unit, which are used to issue an early warning when the pipe burst risk level exceeds a preset threshold.

[0055] Therefore, the present invention adopts the above-mentioned PCCP pipeline broken wire monitoring and pipe burst warning method and system, which has the following beneficial effects:

[0056] 1) Deploying sensor modules on PCCP pipelines can fully and accurately reflect the operating status of the pipelines, improving the real-time and accuracy of monitoring;

[0057] 2) Preprocessing and feature extraction of the collected data can effectively remove noise interference and extract feature information related to broken wires and pipe bursts, providing reliable data support for broken wire identification and pipe burst risk assessment;

[0058] 3) The broken wire identification model is used to determine whether there is a broken wire phenomenon and determine the number and location of the broken wires. The burst risk assessment unit is used to assess the risk level of the burst, which improves the intelligent level of monitoring, reduces manual intervention, and reduces monitoring costs.

[0059] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The present invention is a flowchart of a PCCP pipeline broken wire monitoring and pipe burst warning method and system embodiment. DETAILED DESCRIPTION

[0061] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0062] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0063] Example 1

[0064] like Figure 1 As shown, the present invention provides a PCCP pipeline broken wire monitoring and pipe burst warning method, comprising the following steps:

[0065] S1. Distributed optical fiber sensors are deployed on the PCCP pipeline, and pressure sensors and temperature sensors are deployed at the inlet and outlet of the PCCP pipeline respectively;

[0066] S2, distributed optical fiber sensors, pressure sensors and temperature sensors monitor the PCCP pipe in real time and transmit the acquired data to the signal transmission module;

[0067] S3, the signal transmission module transmits the signal to the signal processing module for data preprocessing;

[0068] The data preprocessing operation is band-pass filtering, and the strain data is amplified.

[0069] S4, the feature extraction unit performs fast Fourier transform on the preprocessed data, extracts frequency domain features, calculates the peak value, mean value and variance of the vibration signal as time domain features, and extracts the strain change rate and strain gradient of the strain data;

[0070] The Fast Fourier Transform FFT specifically includes the following steps:

[0071] S4.1.1. The continuous vibration signal of the PCCP pipeline must first be sampled and quantized to obtain a discrete time-domain signal sequence {x(n)}, where n = 0, 1, ..., N-1, and N is the number of sampling points;

[0072] S4.1.2. Select the radix-2 FFT algorithm to decompose the discrete Fourier transform (DFT) of N points into multiple DFTs of shorter points for calculation; extract the DFT by time. The specific operation of the radix-2 FFT algorithm is as follows:

[0073] The original N-point discrete time domain signal {x(n)} is divided into two groups of subsequences according to the odd or even value of n, namely the even group x even(r) = x(2r) and odd number x odd (r) = x(2r+1), where

[0074] For the two groups of subsequences, DFT calculation of point X even (k) and X odd (k),

[0075] According to the properties of DFT, the DFT results of the two subsequences are combined into the N-point DFT result X(k) of the original sequence using the following formula:

[0076]

[0077] in, is called the twiddle factor, j is the imaginary unit;

[0078] Repeat the above operation in S4.1.2, halving the length of the subsequence each time, until the subsequence length is 1, and finally obtain the complete N-point DFT result, that is, the frequency domain signal {X(k)}, k = 0, 1, ..., N-1;

[0079] S4.1.3. After FFT transformation, the frequency resolution of the frequency domain signal is Among them, f s is the sampling frequency.

[0080] The calculation process of time domain features is as follows:

[0081] In the discrete vibration signal sequence {x v (n)}, n=0,1,…,A-1, the element x with the largest value max That is the peak value of the discrete vibration signal. The calculation formula of the mean is as follows:

[0082]

[0083] Where A is the length of the discrete vibration signal sequence, x v (n) is the signal value at the nth moment in the sequence;

[0084] The formula for calculating variance is as follows:

[0085]

[0086] The discrete strain data sequence {ε(n)}, n = 0, 1, ..., N-1, where N is the number of sampling points and ε(n) represents the strain value at the nth sampling moment. The strain change rate is calculated as follows:

[0087]

[0088] Among them, Δt is the sampling time interval and the sampling frequency is f s ,but

[0089] The strain gradient is used to describe the change of strain in spatial position and reflects the unevenness of strain distribution. Multiple strain sensors are arranged along the axial or circumferential direction on the PCCP pipeline. The distance between adjacent sensors is Δx. The strain values ​​collected by each sensor are ε1, ε2, …, ε n , the strain gradient calculation formula at the mth position is as follows:

[0090]

[0091] S5. Input the extracted features into a broken wire recognition model trained based on a support vector machine algorithm to determine whether there is a broken wire phenomenon and determine the number and location of the broken wires;

[0092] S6. The burst risk assessment unit uses the analytic hierarchy process to determine the weight of each factor based on the number and location of broken wires and the internal pressure and temperature of the PCCP pipeline. It then uses the fuzzy comprehensive evaluation method to assess the burst risk and determine the risk level.

[0093] The specific operation steps of the fuzzy comprehensive evaluation method are as follows:

[0094] S6.1. Determine the set of evaluation factors;

[0095] Let U={u1,u2,…,u n} is a set of factors that affect the risk of PCCP pipeline burst, corresponding to the number of broken wires, broken wire location, pipeline internal pressure, and temperature;

[0096] S6.2, determine the evaluation level set;

[0097] Let V = {v1,v2,…,v m} is the level set of evaluation results;

[0098] S6.3, construct fuzzy relationship matrix;

[0099] Through expert scoring or actual data statistics, for each factor u i , i=0,1,…,n, determine its evaluation level v j , the membership degree r of i=0,1,…,m ij , thus constructing the fuzzy relationship matrix R=(r ij ) n×m , membership degree r ij Representation factor u i Belongs to evaluation level vj The degree of , the value range is [0,1];

[0100] S6.4, use the analytic hierarchy process (AHP) to calculate the factors u i The weight vector W relative to the target layer is (w1, w2, ..., w n ), and satisfy

[0101] S6.5, perform fuzzy synthesis operation;

[0102] The fuzzy synthesis operator is used to synthesize the weight vector W and the fuzzy relationship matrix R to obtain the comprehensive evaluation vector B. The calculation formula is: in is the fuzzy composition operator;

[0103] S6.6, Processing of evaluation results;

[0104] The maximum membership principle or weighted average method is used to evaluate the comprehensive evaluation vector B=(b1,b2,…,b m ) for processing,

[0105] Maximum membership principle:

[0106] The membership degree of the kth evaluation level is b k =max{b1,b2,…,b m}, then the corresponding evaluation level v k The final evaluation result;

[0107] Weighted average method:

[0108] Give each evaluation level v j Assign the corresponding score c j , calculate the comprehensive score The pipe burst risk level is determined based on the comprehensive score.

[0109] S7. When the pipe burst risk level exceeds a preset threshold, the warning unit issues a sound warning and a light warning.

[0110] The present invention provides a PCCP pipeline wire breakage monitoring and pipe burst warning system, which is applied to the above-mentioned PCCP pipeline wire breakage monitoring and pipe burst warning method, including a sensor module, which includes a distributed optical fiber sensor, a pressure sensor, and a temperature sensor for real-time monitoring of the vibration, strain, internal pressure, and temperature data of the PCCP pipeline;

[0111] A signal transmission module, used for transmitting the signal to the signal processing module;

[0112] The data processing module includes a data preprocessing unit, a feature extraction unit, a broken wire identification and location unit, and a pipe burst risk assessment unit. It is used to preprocess the data, extract frequency domain features, time domain features, strain rate, and strain gradient, identify whether broken wires exist, determine the number and location of broken wires, and assess the risk of pipe burst.

[0113] The early warning module includes a sound warning unit and a light warning unit, which are used to issue an early warning when the pipe burst risk level exceeds a preset threshold.

[0114] Therefore, the present invention adopts the above-mentioned PCCP pipeline broken wire monitoring and pipe burst early warning method and system. The sensor module deployed on the PCCP pipeline can comprehensively and accurately reflect the operating status of the pipeline, thereby improving the real-time and accuracy of monitoring; preprocessing and feature extraction of the collected data can effectively remove noise interference and extract feature information related to broken wires and pipe burst, providing reliable data support for broken wire identification and pipe burst risk assessment; using the broken wire identification model to determine whether the wire is broken and the number and location of the broken wires, and using the pipe burst risk assessment unit to assess the risk level of the pipe burst, the intelligent level of monitoring is improved, manual intervention is reduced, and monitoring costs are reduced.

[0115] 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 the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A PCCP pipeline wire breakage monitoring and pipe burst warning method, characterized by: The following steps are involved: S1. Distributed optical fiber sensors are deployed on the PCCP pipeline, and pressure sensors and temperature sensors are deployed at the inlet and outlet of the PCCP pipeline respectively; S2, distributed optical fiber sensors, pressure sensors and temperature sensors monitor the PCCP pipe in real time and transmit the acquired data to the signal transmission module; S3, the signal transmission module transmits the signal to the signal processing module for data preprocessing; S4, the feature extraction unit performs fast Fourier transform on the preprocessed data, extracts frequency domain features, calculates the peak value, mean value and variance of the vibration signal as time domain features, and extracts the strain change rate and strain gradient of the strain data; S5. Input the extracted features into a broken wire recognition model trained based on a support vector machine algorithm to determine whether there is a broken wire phenomenon and determine the number and location of the broken wires; S6. The burst risk assessment unit uses the analytic hierarchy process to determine the weight of each factor based on the number and location of broken wires and the internal pressure and temperature of the PCCP pipeline. It then uses the fuzzy comprehensive evaluation method to assess the burst risk and determine the risk level. S7. When the pipe burst risk level exceeds a preset threshold, the warning unit issues a sound warning and a light warning.

2. A PCCP pipeline wire breakage monitoring and pipe burst early warning method according to claim 1, characterized in that: The data preprocessing operation in S3 is band-pass filtering, and the strain data is amplified.

3. A PCCP pipeline wire breakage monitoring and pipe burst early warning method according to claim 1, characterized in that: The Fast Fourier Transform (FFT) in S4 specifically includes the following steps: S4.1.

1. The continuous vibration signal of the PCCP pipeline must first be sampled and quantized to obtain a discrete time-domain signal sequence {x(n)}, where n = 0, 1, ..., N-1, and N is the number of sampling points; S4.1.

2. Select the radix-2 FFT algorithm to decompose the discrete Fourier transform (DFT) of N points into multiple DFTs of shorter points for calculation; extract the DFT by time. The specific operation of the radix-2 FFT algorithm is as follows: The original N-point discrete time domain signal {x(n)} is divided into two groups of subsequences according to the odd or even value of n, namely the even group x even (r) = x(2r) and odd number x odd (r) = x(2r+1), where For the two groups of subsequences, DFT calculation of point X even (k) and X odd (k), According to the properties of DFT, the DFT results of the two subsequences are combined into the N-point DFT result X(k) of the original sequence using the following formula: in, is called the twiddle factor, j is the imaginary unit; Repeat the above operation in S4.1.2, halving the length of the subsequence each time, until the subsequence length is 1, and finally obtain the complete N-point DFT result, that is, the frequency domain signal {X(k)}, k = 0, 1, ..., N-1; S4.1.

3. After FFT transformation, the frequency resolution of the frequency domain signal is Among them, f s is the sampling frequency.

4. A PCCP pipeline wire breakage monitoring and pipe burst early warning method according to claim 1, characterized in that: The calculation process of time domain features in S4 is as follows: In the discrete vibration signal sequence {x v (n)}, n=0,1,…,A-1, the element x with the largest value max That is the peak value of the discrete vibration signal. The calculation formula of the mean is as follows: Where A is the length of the discrete vibration signal sequence, x v (n) is the signal value at the nth moment in the sequence; The formula for calculating variance is as follows:

5. The PCCP pipeline wire breakage monitoring and pipe burst early warning method according to claim 1 is characterized by: The discrete strain data sequence in S4 is {ε(n)}, n = 0, 1, ..., N-1, where N is the number of sampling points and ε(n) represents the strain value at the nth sampling moment. The strain change rate is calculated as follows: Among them, Δt is the sampling time interval and the sampling frequency is f s ,but The strain gradient is used to describe the change of strain in spatial position and reflects the unevenness of strain distribution. Multiple strain sensors are arranged along the axial or circumferential direction on the PCCP pipeline. The distance between adjacent sensors is Δx. The strain values ​​collected by each sensor are ε1, ε2, …, ε n , the strain gradient calculation formula at the mth position is as follows:

6. A PCCP pipeline wire breakage monitoring and pipe burst early warning method according to claim 1, characterized in that: The specific operation steps of the fuzzy comprehensive evaluation method in S6 are as follows: S6.

1. Determine the set of evaluation factors; Let U={u1,u2,…,u n } is a set of factors that affect the risk of PCCP pipeline burst, corresponding to the number of broken wires, broken wire location, pipeline internal pressure, and temperature; S6.2, determine the evaluation level set; Let V = {v1,v2,…,v m } is the level set of evaluation results; S6.3, construct fuzzy relationship matrix; Through expert scoring or actual data statistics, for each factor u i , i=0,1,…,n, determine its evaluation level v j , the membership degree r of i=0,1,…,m ij , thus constructing the fuzzy relationship matrix R=(r ij ) n×m , membership degree r ij Representation factor u i Belongs to evaluation level v j The degree of , the value range is [0,1]; S6.4, use the analytic hierarchy process (AHP) to calculate the factors u i The weight vector W relative to the target layer is (w1, w2, ..., w n ), and satisfy w i ≥0; S6.5, perform fuzzy synthesis operation; The fuzzy synthesis operator is used to synthesize the weight vector W and the fuzzy relationship matrix R to obtain the comprehensive evaluation vector B. The calculation formula is: in is the fuzzy composition operator; S6.6, Processing of evaluation results; The maximum membership principle or weighted average method is used to evaluate the comprehensive evaluation vector B=(b1,b2,…,b m ) for processing, Maximum membership principle: The membership degree of the kth evaluation level is b k =max{b1,b2,…,b m }, then the corresponding evaluation level v k The final evaluation result; Weighted average method: Give each evaluation level v j Assign the corresponding score c j , calculate the comprehensive score The pipe burst risk level is determined based on the comprehensive score.

7. A PCCP pipeline wire breakage monitoring and pipe burst warning system, applied to a PCCP pipeline wire breakage monitoring and pipe burst warning method according to any one of claims 1 to 6, characterized in that: The sensor module includes a distributed optical fiber sensor, a pressure sensor, and a temperature sensor for real-time monitoring of vibration, strain, internal pressure, and temperature data of the PCCP pipeline; A signal transmission module, used for transmitting the signal to the signal processing module; The data processing module includes a data preprocessing unit, a feature extraction unit, a broken wire identification and location unit, and a pipe burst risk assessment unit. It is used to preprocess the data, extract frequency domain features, time domain features, strain rate, and strain gradient, identify whether broken wires exist, determine the number and location of broken wires, and assess the risk of pipe burst. The early warning module includes a sound warning unit and a light warning unit, which are used to issue an early warning when the pipe burst risk level exceeds a preset threshold.

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