供水管道泄漏自适应检测及定位信号处理方法研究
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摘要
供水管道泄漏检测及定位是一项具有重大经济、社会和环境效益的工作。目前基于泄漏声信号处理的方法——以相关检漏仪为代表,被广泛应用于供水管道泄漏检测与定位中。相关检漏仪大多采用广义互相关时延估计算法,需要已知或估计信号与噪声的功率谱设计相应滤波器,谱估计的准确性直接关系到时延估计器的性能。在实际供水管道泄漏检测应用中,泄漏信号自身的谱特征因管道参数及埋设环境的不同而各异,同时泄漏信号的传播路径及环境中各种噪声干扰等多种复杂因素的影响,每次检测时其信号与噪声的功率谱实际难以获得,单一因素的差异即会导致谱估计的不准确,从而使整个泄漏检测失效。
近几年来,作者所在的研究组将自适应时延估计方法引入地下管道的泄漏检测及漏点定位,实现对不同环境条件不同泄漏对象的自适应检测和定位。采用自适应滤波代替广义互相关算法,能够根据现场采集信号的实际特征实时更新和调整,通过自适应迭代由滤波器自身的参数直接得到泄漏声信号的最优相关结果,避免了相关检漏仪需要知道信号和噪声谱等先验知识的不足,使得泄漏检测仪器具有很好的跟踪和自适应能力,而且计算简便,能够实现在线操作,取得了显著的效果。但由于实际检漏环境复杂,自适应算法在复杂动态环境中的收敛状态难以用传统方法进行判断;泄漏水声的干扰因素很多,管道内在与外在多种环境噪声的叠加使得获取的泄漏信号信噪比较低,噪声引入的自适应有偏估计对可检测的信噪比极限及最优检测结果有影响;另外检测环境中各种突发干扰的发生,会影响自适应算法收敛解的位置。因此,论文提出了自适应收敛动态判断方法,自适应无偏时延估计以及突发干扰去除方法,相应解决自适应算法在低信噪比、非平稳干扰环境及其他恶劣工况检测条件下应用时遇到的实际问题,并改善泄漏检测定位的性能。
论文的具体研究内容有:
①研究了基于自适应时延估计的供水管道泄漏检测定位方法,以及泄漏检测的定位误差和时延估计误差。通过研究指出自适应算法在实际复杂环境及恶劣工况条件下检测遇到的几个关键问题,如自适应算法在复杂动态噪声背景下的收敛判断,噪声引入的自适应有偏估计问题,以及突发干扰对自适应收敛解位置的影响等,为整个论文的研究工作打下基础。
②基于自适应滤波器工作在理想最优条件时的正交原理,推导出考虑梯度噪声影响时误差与输出实际近似正交的约束条件,根据每次迭代不断更新的期望、输出及误差信号的均方估计,在线实时判断自适应算法是否收敛,克服了传统判断方法的缺点,解决了自适应算法在复杂动态噪声背景下收敛状态的判断问题。同时,通过在线实时评估自适应解的质量,在收敛点处选取最优估计并停止迭代,保证了自适应检测结果的可靠性,还提高了迭代处理的效率。实验结果表明,动态判断收敛方法对平稳和非平稳检测环境均适用,相应检测条件下的自适应系统参数辨识及时延估计的方差因此得到较大改善。
③针对泄漏检测中噪声引入的自适应有偏估计,基于Treichler的γ-LMS算法提出一种无偏自适应时延估计方法。提出利用最佳逼近原理解释滤波器中各信号间的几何关系,借助传统自适应滤波器得到的期望输出信号来估计输入噪声功率,应用γ-LMS算法在迭代过程中逐步去除输入噪声的影响。该无偏估计算法利用自适应滤波器可以获得的信息去除噪声影响,使最优维纳解的真实峰值得到增强,改善了低信噪比环境下的自适应时延估计性能。实验表明,此自适应无偏估计算法使可检测的信噪比极限从原来的-16dB下降至-20dB,通过增强峰值,还能够抑制检漏环境中非平稳干扰的影响。
④研究了泄漏信号与非平稳干扰噪声的可分辨特征,根据二者产生机理的差别,以及突发干扰信号特有的局部时频特性,利用时频字典匹配搜索的方法来去除来自不同源的不可预测突发干扰噪声。构建恰当的时频字典表,自适应搜索混合信号与字典表中元素最佳匹配或相关的确定性干扰噪声成分,分离出该信号,恢复出平稳的随机性泄漏信号。通过同时去除多种突发干扰源,提高了检测定位系统在非平稳干扰环境下的抗干扰能力、鲁棒性,及检测结果的可靠性。
⑤在研究组基于泄漏声信号自适应时延估计方法构建的智能检漏定位仪器平台上,将论文中提出的信号处理方法分别应用于系统的实际泄漏检测,通过大量数据处理实验验证了方法在实际检测应用中的有效性。自适应解的实时动态自评估、无偏自适应估计方法和采集信号中自适应匹配搜索去除突发干扰的预处理方法,不仅解决了自适应系统在泄漏检测定位应用中遇到的主要问题,而且可以使检测定位系统的检测灵敏度,复杂多变噪声环境中定位的准确性和稳定性,以及对环境中突发噪声的抗干扰能力都有较大的提高。通过实时判优,实际迭代时间平均减少至原来将数据迭代完毕时所需时间的1/5,实际非平稳干扰环境下的泄漏检测定位误差在2米以内(大部分在1米以内),接近于平稳环境下的检测结果;自适应无偏估计算法不仅提高了实际的检测灵敏度,而且可以使自适应系统在多变检漏环境中的稳定性和检测性能都有明显提高;在去除突发干扰改善信号质量后,避免自适应系统收敛至多个稳态解,提高了系统检测结果的可靠性,使得稳定后定位误差范围减小至去噪前的1/30~1/2,系统的抗干扰能力和稳定性得到增强。上述多种方法综合运用于智能检漏定位系统中,使得仪器不受环境限制,整体定位误差在1米以内,有效提高了检漏系统在复杂噪声环境及恶劣工况条件下的检测性能。
Water pipe leak detection and location is such an important activity that it can have profound effects on our society, economy and environment. At present acoustic leak processing methods have been widely employed for detecting and locating the water pipe leakage. In particular, correlators are the most popular ones, which utilize the generalized cross correlation method to analyze the leak sound signal. A cross correlator needs a priori knowledge of the signal and noise spectra to design its proper filter before correlating the two measurements. Accurate spectra acquisition is crucial for the following correlating process and finally getting correct delay estimation. However, for practical water pipe leak detection, the leak sound varies with different pipe parameters and its buried soil environments, the different sound propagation paths and so on, and all kinds of interferences from the unpredictable noisy background differ all the time. It is very difficult to get accurate spectra of the signal and the noise seperately beforehand, and to design filters is not easy because there is no proper information provided for reference. Even one changing factor will make wrong estimates of the signal or noise spectra, and will lead to the failure of the whole detection.
To overcome the traditional cross correlator’s weakness, our research group recently applies adaptive time delay estimation, which employs adaptive filtering instead of the generalized cross correlation method, into buried water pipeline leak detection. The adaptive filter can be updated iteratively by adapting to the actual acquired signal’s characteristics and get the correlation results directly from its own system parameters, which doesn’t need any prior knowledge of the signal or the noise thus it can track the changing detection targets and time-varying environment. Besides, the LMS time delay estimation is simple and easy to compute, and can be implemented on line as well. However, the practical detection environment is always complicated. It is difficult to use traditional ways to determine when the adaptive algorithm converges in a dynamic complex noisy background. Besides, the leaking sound is contaminated by many different interferences coming from the inside and the outside of the pipe, thus the acquired signals are always with very low Signal-Noise-Ratio(SNR). The noises not only introduce the biased estimates for the adaptive filter, but also have an effect on the system’s detectible SNR limit and its optimal detection result. Moreover, when it is mixed with nonstationary bursting noises, the location of the convergent adaptive solution varies with different signal characteristics at different time. To solve these problems confronted in the practical application of adaptive time delay estimation, several signal processing methods are proposed in this paper to dynamically determine when the adaptation process acctually converges, to elimate the noise influence with the bias-free adaptive algorithm, and to eradicate the unpredictable bursting interferences, in order to improve the adaptive leak detection and location performance for the water pipe in nonstationary, or low-SNR, or other abominable noisy environments. The detail of the work is described as follows:
①It is studied the leak location principle based on adaptive time delay estimation method, and its pinpointing error and the time delay estimation error. From the practical problems confronted with the leak detection in complicated and abominable noisy environments, it is pointed out three key issues to be solved when adaptive method is adopted: dynamic discrimination for the converging state of the adaptive process, biased estimation induced by the noises in the adaptive algorithm, and the influence of the interferences for the Wiener solution. All of these are the work foundation for the whole paper.
②Based on the principle of orthogonality, an on-line constraint of the adaptive detection system which can be used to dynamically determine the LMS algorithm’s convergence, is derived when the error and output get approximately orthogonal due to the gradient noise practically. The dynamic discriminant obtained through estimated mean-square values of the desired, output and error signals at each iteration, can be updated along with the adaptation. While avoiding the weakness of the traditional discrimination methods, it can dynamically evaluate quality of the adaptive solution and solves the first problem in practical detection. Through ending the iteration timely and accurately it can get the optimal estimation even with complicated nonstationary interferences. As a result, the performance and efficiency of time delay estimation are improved simultaneously while using adaptive algorithms. The experiment results show that it is applicable to adaptive detection systems in nonstationary as well as stationary environments, and the estimation variances of the adaptive parameter identification and time delay estimation applications are significantly decreased because of the dynamic convergence determination.
③For the measurement data of lower SNR with stationary noises, the biased Wiener solution induced by the input noise will result in deteriorated performance in LMS time delay estimator (LMSTDE). Then a modified bias-free scheme based on Treichler’s r -LMS algorithm is developed for eliminating the input noise iteratively, in which the input noise variance can be simply obtained from the steady output of the traditional adaptive filter by the geometric interpretation of the best approximation projection of relative signals. It utilizes available information from the adaptive filter itself without any a priori knowledge of the interference, to enhance the peak of the optimal Wiener solution, thus it can improve the performance of the LMSTDE in lower SNR environments. Simulation and real data application are both provided to validate its improved effectiveness: it not only makes the detectible SNR level to be reduced to -20dB from -16dB, but also can resist the nonstationary interferences in the actual leak detection to some degree.
④It is analyzed the distinctive features of leak sound and other interference noises. Due to the absolutely different mechanics of generation of these two types of signals, and the local time and frequency distribution property of the interferences in time-frequency plane, an application of matching pursuit with a Time-Frequency Dictionary is proposed to remove the unpredictable but determined interferences, which could come from many different sources. A proper time-frequency dictionary is built, then adaptive matching pursuit method is used to iteratively decompose the original mixture signal into a linear expansion of waveforms that are selected from the time-frequency dictionary. These waveforms are chosen in order to best match the signal structure. After the adaptive time-frequency transformation, the main determined interferences in the mixture signals are first extracted, while the leak signal is left behind and recovered at last. In the end, the signal quality is enhanced greatly after the removal of strong time-varying noises and the adaptive leak detector no longer convergences to multiple stable Wiener solutions. The detector is then much more immune to the influence of bursting interferences in practical complicated noisy environments, and more reliable and robust under nonstationary dynamic detection condition.
⑤An intelligent leak detection and location instrument is developed by our group based on the adaptive time delay estimation with leak sound. By applying the above signal processing methods into this leak detection instrument, lots of experiments are employed with practical acquired leak data to validate their effectiveness respectively. It shows that through the real-time dynamic convergence discrimination, the bias-free LMSTDE, and the burst interference removal, this adaptive system’s detection sensitiviry, location precision and stability in nonstationary noisy environment, and its interference resistance capability after the adaptation process enters its steady state, are all improved dramatically. The actual leak detection error in varying complicated environments can be controlled in less than 2 meters(mostly less than 1 meter) when using the dynamic discriminant, which are close to that in stationary background. And the adaptation process time can be reduced to 1/5 of the original time. The bias-free LMSTDE can not only reduce the detectible SNR limit level but also improve the reliablity and other detection performances of the adaptive system in changeable environments. And after the burst interference removal for practical measurements, the adaptive location error range when the filter enters its steady state can be reduced to 1/30 to 1/2 of the original case, the detection system’s robustness is thus enhanced. When all the above signal processing methods are applied into this intelliget leak detection and location instrument, the detector’s location error can be controlled in no more than 1 meter, and the system’s performance is improved in the dynamic complicated noisy environments.
近几年来,作者所在的研究组将自适应时延估计方法引入地下管道的泄漏检测及漏点定位,实现对不同环境条件不同泄漏对象的自适应检测和定位。采用自适应滤波代替广义互相关算法,能够根据现场采集信号的实际特征实时更新和调整,通过自适应迭代由滤波器自身的参数直接得到泄漏声信号的最优相关结果,避免了相关检漏仪需要知道信号和噪声谱等先验知识的不足,使得泄漏检测仪器具有很好的跟踪和自适应能力,而且计算简便,能够实现在线操作,取得了显著的效果。但由于实际检漏环境复杂,自适应算法在复杂动态环境中的收敛状态难以用传统方法进行判断;泄漏水声的干扰因素很多,管道内在与外在多种环境噪声的叠加使得获取的泄漏信号信噪比较低,噪声引入的自适应有偏估计对可检测的信噪比极限及最优检测结果有影响;另外检测环境中各种突发干扰的发生,会影响自适应算法收敛解的位置。因此,论文提出了自适应收敛动态判断方法,自适应无偏时延估计以及突发干扰去除方法,相应解决自适应算法在低信噪比、非平稳干扰环境及其他恶劣工况检测条件下应用时遇到的实际问题,并改善泄漏检测定位的性能。
论文的具体研究内容有:
①研究了基于自适应时延估计的供水管道泄漏检测定位方法,以及泄漏检测的定位误差和时延估计误差。通过研究指出自适应算法在实际复杂环境及恶劣工况条件下检测遇到的几个关键问题,如自适应算法在复杂动态噪声背景下的收敛判断,噪声引入的自适应有偏估计问题,以及突发干扰对自适应收敛解位置的影响等,为整个论文的研究工作打下基础。
②基于自适应滤波器工作在理想最优条件时的正交原理,推导出考虑梯度噪声影响时误差与输出实际近似正交的约束条件,根据每次迭代不断更新的期望、输出及误差信号的均方估计,在线实时判断自适应算法是否收敛,克服了传统判断方法的缺点,解决了自适应算法在复杂动态噪声背景下收敛状态的判断问题。同时,通过在线实时评估自适应解的质量,在收敛点处选取最优估计并停止迭代,保证了自适应检测结果的可靠性,还提高了迭代处理的效率。实验结果表明,动态判断收敛方法对平稳和非平稳检测环境均适用,相应检测条件下的自适应系统参数辨识及时延估计的方差因此得到较大改善。
③针对泄漏检测中噪声引入的自适应有偏估计,基于Treichler的γ-LMS算法提出一种无偏自适应时延估计方法。提出利用最佳逼近原理解释滤波器中各信号间的几何关系,借助传统自适应滤波器得到的期望输出信号来估计输入噪声功率,应用γ-LMS算法在迭代过程中逐步去除输入噪声的影响。该无偏估计算法利用自适应滤波器可以获得的信息去除噪声影响,使最优维纳解的真实峰值得到增强,改善了低信噪比环境下的自适应时延估计性能。实验表明,此自适应无偏估计算法使可检测的信噪比极限从原来的-16dB下降至-20dB,通过增强峰值,还能够抑制检漏环境中非平稳干扰的影响。
④研究了泄漏信号与非平稳干扰噪声的可分辨特征,根据二者产生机理的差别,以及突发干扰信号特有的局部时频特性,利用时频字典匹配搜索的方法来去除来自不同源的不可预测突发干扰噪声。构建恰当的时频字典表,自适应搜索混合信号与字典表中元素最佳匹配或相关的确定性干扰噪声成分,分离出该信号,恢复出平稳的随机性泄漏信号。通过同时去除多种突发干扰源,提高了检测定位系统在非平稳干扰环境下的抗干扰能力、鲁棒性,及检测结果的可靠性。
⑤在研究组基于泄漏声信号自适应时延估计方法构建的智能检漏定位仪器平台上,将论文中提出的信号处理方法分别应用于系统的实际泄漏检测,通过大量数据处理实验验证了方法在实际检测应用中的有效性。自适应解的实时动态自评估、无偏自适应估计方法和采集信号中自适应匹配搜索去除突发干扰的预处理方法,不仅解决了自适应系统在泄漏检测定位应用中遇到的主要问题,而且可以使检测定位系统的检测灵敏度,复杂多变噪声环境中定位的准确性和稳定性,以及对环境中突发噪声的抗干扰能力都有较大的提高。通过实时判优,实际迭代时间平均减少至原来将数据迭代完毕时所需时间的1/5,实际非平稳干扰环境下的泄漏检测定位误差在2米以内(大部分在1米以内),接近于平稳环境下的检测结果;自适应无偏估计算法不仅提高了实际的检测灵敏度,而且可以使自适应系统在多变检漏环境中的稳定性和检测性能都有明显提高;在去除突发干扰改善信号质量后,避免自适应系统收敛至多个稳态解,提高了系统检测结果的可靠性,使得稳定后定位误差范围减小至去噪前的1/30~1/2,系统的抗干扰能力和稳定性得到增强。上述多种方法综合运用于智能检漏定位系统中,使得仪器不受环境限制,整体定位误差在1米以内,有效提高了检漏系统在复杂噪声环境及恶劣工况条件下的检测性能。
Water pipe leak detection and location is such an important activity that it can have profound effects on our society, economy and environment. At present acoustic leak processing methods have been widely employed for detecting and locating the water pipe leakage. In particular, correlators are the most popular ones, which utilize the generalized cross correlation method to analyze the leak sound signal. A cross correlator needs a priori knowledge of the signal and noise spectra to design its proper filter before correlating the two measurements. Accurate spectra acquisition is crucial for the following correlating process and finally getting correct delay estimation. However, for practical water pipe leak detection, the leak sound varies with different pipe parameters and its buried soil environments, the different sound propagation paths and so on, and all kinds of interferences from the unpredictable noisy background differ all the time. It is very difficult to get accurate spectra of the signal and the noise seperately beforehand, and to design filters is not easy because there is no proper information provided for reference. Even one changing factor will make wrong estimates of the signal or noise spectra, and will lead to the failure of the whole detection.
To overcome the traditional cross correlator’s weakness, our research group recently applies adaptive time delay estimation, which employs adaptive filtering instead of the generalized cross correlation method, into buried water pipeline leak detection. The adaptive filter can be updated iteratively by adapting to the actual acquired signal’s characteristics and get the correlation results directly from its own system parameters, which doesn’t need any prior knowledge of the signal or the noise thus it can track the changing detection targets and time-varying environment. Besides, the LMS time delay estimation is simple and easy to compute, and can be implemented on line as well. However, the practical detection environment is always complicated. It is difficult to use traditional ways to determine when the adaptive algorithm converges in a dynamic complex noisy background. Besides, the leaking sound is contaminated by many different interferences coming from the inside and the outside of the pipe, thus the acquired signals are always with very low Signal-Noise-Ratio(SNR). The noises not only introduce the biased estimates for the adaptive filter, but also have an effect on the system’s detectible SNR limit and its optimal detection result. Moreover, when it is mixed with nonstationary bursting noises, the location of the convergent adaptive solution varies with different signal characteristics at different time. To solve these problems confronted in the practical application of adaptive time delay estimation, several signal processing methods are proposed in this paper to dynamically determine when the adaptation process acctually converges, to elimate the noise influence with the bias-free adaptive algorithm, and to eradicate the unpredictable bursting interferences, in order to improve the adaptive leak detection and location performance for the water pipe in nonstationary, or low-SNR, or other abominable noisy environments. The detail of the work is described as follows:
①It is studied the leak location principle based on adaptive time delay estimation method, and its pinpointing error and the time delay estimation error. From the practical problems confronted with the leak detection in complicated and abominable noisy environments, it is pointed out three key issues to be solved when adaptive method is adopted: dynamic discrimination for the converging state of the adaptive process, biased estimation induced by the noises in the adaptive algorithm, and the influence of the interferences for the Wiener solution. All of these are the work foundation for the whole paper.
②Based on the principle of orthogonality, an on-line constraint of the adaptive detection system which can be used to dynamically determine the LMS algorithm’s convergence, is derived when the error and output get approximately orthogonal due to the gradient noise practically. The dynamic discriminant obtained through estimated mean-square values of the desired, output and error signals at each iteration, can be updated along with the adaptation. While avoiding the weakness of the traditional discrimination methods, it can dynamically evaluate quality of the adaptive solution and solves the first problem in practical detection. Through ending the iteration timely and accurately it can get the optimal estimation even with complicated nonstationary interferences. As a result, the performance and efficiency of time delay estimation are improved simultaneously while using adaptive algorithms. The experiment results show that it is applicable to adaptive detection systems in nonstationary as well as stationary environments, and the estimation variances of the adaptive parameter identification and time delay estimation applications are significantly decreased because of the dynamic convergence determination.
③For the measurement data of lower SNR with stationary noises, the biased Wiener solution induced by the input noise will result in deteriorated performance in LMS time delay estimator (LMSTDE). Then a modified bias-free scheme based on Treichler’s r -LMS algorithm is developed for eliminating the input noise iteratively, in which the input noise variance can be simply obtained from the steady output of the traditional adaptive filter by the geometric interpretation of the best approximation projection of relative signals. It utilizes available information from the adaptive filter itself without any a priori knowledge of the interference, to enhance the peak of the optimal Wiener solution, thus it can improve the performance of the LMSTDE in lower SNR environments. Simulation and real data application are both provided to validate its improved effectiveness: it not only makes the detectible SNR level to be reduced to -20dB from -16dB, but also can resist the nonstationary interferences in the actual leak detection to some degree.
④It is analyzed the distinctive features of leak sound and other interference noises. Due to the absolutely different mechanics of generation of these two types of signals, and the local time and frequency distribution property of the interferences in time-frequency plane, an application of matching pursuit with a Time-Frequency Dictionary is proposed to remove the unpredictable but determined interferences, which could come from many different sources. A proper time-frequency dictionary is built, then adaptive matching pursuit method is used to iteratively decompose the original mixture signal into a linear expansion of waveforms that are selected from the time-frequency dictionary. These waveforms are chosen in order to best match the signal structure. After the adaptive time-frequency transformation, the main determined interferences in the mixture signals are first extracted, while the leak signal is left behind and recovered at last. In the end, the signal quality is enhanced greatly after the removal of strong time-varying noises and the adaptive leak detector no longer convergences to multiple stable Wiener solutions. The detector is then much more immune to the influence of bursting interferences in practical complicated noisy environments, and more reliable and robust under nonstationary dynamic detection condition.
⑤An intelligent leak detection and location instrument is developed by our group based on the adaptive time delay estimation with leak sound. By applying the above signal processing methods into this leak detection instrument, lots of experiments are employed with practical acquired leak data to validate their effectiveness respectively. It shows that through the real-time dynamic convergence discrimination, the bias-free LMSTDE, and the burst interference removal, this adaptive system’s detection sensitiviry, location precision and stability in nonstationary noisy environment, and its interference resistance capability after the adaptation process enters its steady state, are all improved dramatically. The actual leak detection error in varying complicated environments can be controlled in less than 2 meters(mostly less than 1 meter) when using the dynamic discriminant, which are close to that in stationary background. And the adaptation process time can be reduced to 1/5 of the original time. The bias-free LMSTDE can not only reduce the detectible SNR limit level but also improve the reliablity and other detection performances of the adaptive system in changeable environments. And after the burst interference removal for practical measurements, the adaptive location error range when the filter enters its steady state can be reduced to 1/30 to 1/2 of the original case, the detection system’s robustness is thus enhanced. When all the above signal processing methods are applied into this intelliget leak detection and location instrument, the detector’s location error can be controlled in no more than 1 meter, and the system’s performance is improved in the dynamic complicated noisy environments.
引文
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