Pipeline Damage and Leak Detection Based on Sound Spectrum LPCC and HMM

Ai, Changsheng; Zhao, Honghua; Ma, Ren; Dong, Xueren

doi:10.1109/isda.2006.215

Cited by 25 publications

(22 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The learning rate is set to Z ¼ 0:001. The detection performance of the proposed method is compared with that of LPCC method (Ai et al 2006) and SVM method (Toshitaka and Akira 2007). Table 3 gives the comparison result of detection performances.…”

Section: Detection Of Leak In the Presence Of Non-leak Noise Outside mentioning

confidence: 99%

“…Michael presented a method, which extracted a plurality of linear predictive coding cepstrum coefficients as the acoustic features to distinguish the leak signal from ambient non-leak noises (Seaford 1994). Ai et al adopted linear prediction cepstrum coefficient (LPCC) and hidden Markov models (HMM) to recognise leak acoustic signals (Ai et al 2006). Toshitaka and Akira proposed a leak detection method using the Support Vector Machine (SVM) (Toshitaka and Akira 2007).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Study on an improved acoustic leak detection method for water distribution systems

Yang

Wen

et al. 2013

Urban Water Journal

View full text Add to dashboard Cite

In this paper, a new method is proposed to detect leaks in the presence of the non-leak noise inside or outside a pipeline. Due to the ability to analyze the coherent structure of time series, the autocorrelation function is used to describe the self-similarity feature of the leak signal. The values of the autocorrelation function for the lag t larger than the correlation length of the signal, not the signal itself or the entire autocorrelation function, are used to extract or evaluate the self-similarity degree of the signal by the approximate entropy. Based on feature extraction, a new detection function related to autocorrelation functions of the acquired signals is built to detect leak. Then a neural-network approach is utilised as a classifier to discriminate the leak signals from the non-leak signals inside and outside pipes. The proposed method has been employed to detect leak in the presence of the non-leak noises inside and outside pipes, and achieved a 93.8% and 86.3% correct detection rate, respectively.

show abstract

Section: Detection Of Leak In the Presence Of Non-leak Noise Outside mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study on an improved acoustic leak detection method for water distribution systems

Yang

Wen

et al. 2013

Urban Water Journal

View full text Add to dashboard Cite

show abstract

“…Acoustic leak detection techniques and equipments have been shown to be effective and are widely used in the water pipelines [1,[9][10][11][12][13][14][15][16]. The conventional acoustic equipments include listening rods, geophones and ground microphones, and may be either mechanical or electronic [9][10].…”

Section: Introductionmentioning

confidence: 99%

“…These methods were somewhat successful in detection leaks, but were incapable of differentiating alarms due to non-leak acoustic sources outside pipelines, such as machine noises and construction noises. Michael Savic (1995) presented an apparatus for determining the existence of a leak in an underground pipe, which extracted a plurality of linear predictive coding (LPC) cepstrum coefficients as the acoustic features to distinguish the leak signal from ambient acoustic signals [14]. Ai et al (2006) described a leak detection system based on linear prediction cepstrum coefficient (LPCC) and hidden Markov models (HMM) to recognize acoustic signals [15].…”

Section: Introductionmentioning

confidence: 99%

“…Michael Savic (1995) presented an apparatus for determining the existence of a leak in an underground pipe, which extracted a plurality of linear predictive coding (LPC) cepstrum coefficients as the acoustic features to distinguish the leak signal from ambient acoustic signals [14]. Ai et al (2006) described a leak detection system based on linear prediction cepstrum coefficient (LPCC) and hidden Markov models (HMM) to recognize acoustic signals [15]. Toshitaka and Akira (2007) proposed a leak detection method using the Support Vector Machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Information processing for leak detection on underground water supply pipelines

Yang

Wen

2010

Third International Workshop on Advanced Computational Intelligence

View full text Add to dashboard Cite

In the correlation-based leak location, it is supposed that the correlative components in the spatially separately collected acoustic signals merely result from a leak or leaks. This is why a false leak location will be produced when there is a non-leak acoustic source occurring outside a pipeline. To void a false leak location, it is necessary to detect whether or not a real leak exists in the pipeline beforehand. The traditional methods can detect leak only when the leak signal and non-leak signal are not acquired by the vibration sensors in the pipeline simultaneously. However, in practice, the leak signal is always blurred with the non-leak signal, and they will be picked up by the vibration sensors simultaneously. In this case, the traditional detection methods are infeasible. In this paper, a new feature extraction and leak identification method using autocorrelation analysis and approximate entropy algorithm is proposed to detect leak in the presence of non-leak acoustic sources. Due to the ability to analyze the coherent structure of time series, the autocorrelation function is used to describe the self-similarity of the signal. And the autocorrelation function values for the delay τ larger than the signal correlation length, not the signal itself or its entire autocorrelation function, is used to extract or evaluate the self-similarity degree of the signal by the approximate entropy algorithm. A neural-network approach has been developed as a classifier, which uses the identified self-similarity degrees as the network inputs. The method has been employed to identify the leak signals in the presence of some non-leak sounds, and achieved a 93.8% correct detection rate.

show abstract

Pipeline Fault Diagnosis Using Wavelet Entropy and Ensemble Deep Neural Technique

Duong

Kim

2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Pipeline Damage and Leak Detection Based on Sound Spectrum LPCC and HMM

Cited by 25 publications

References 1 publication

Study on an improved acoustic leak detection method for water distribution systems

Study on an improved acoustic leak detection method for water distribution systems

Information processing for leak detection on underground water supply pipelines

Pipeline Fault Diagnosis Using Wavelet Entropy and Ensemble Deep Neural Technique

Contact Info

Product

Resources

About