Hidden Markov models for pipeline damage detection using piezoelectric transducers

Zhang, Mingchi; Chen, Xuemin; Li, Wei

doi:10.1007/s13349-021-00481-0

Cited by 16 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different leakage locations are chosen as different states in the HMM. Two damage indices, i.e., one time domain damage index and one frequency domain index, as we used in [14], are extracted from the original signals. The damage indices are adopted to indicate the signal variations and serve as observations of the HMM.…”

Section: Setup Of Experimentsmentioning

confidence: 99%

“…In our previews work [14], three different leakage locations were chosen as three states for the GMM-HMM method. To compare the performance of DNN-HMM with GMM-HMM, the same state setting and initial parameters were applied in the two models.…”

Section: Leakage Detection With Three Statesmentioning

confidence: 99%

“…In our previous study, GMM-HMM was applied for pipeline leakage and crack depth detection [14]. Each hidden state has a probability distribution over the possible leakage states where the probability distribution matrix is initialized by a Gaussian mixture model (GMM).…”

Section: Introductionmentioning

confidence: 99%

“…That work successfully answered our research questions, i.e., whether the pipeline has a leak, where the leakage location is and how deep the crack is. In addition, the GMM-HMM method [14] has the ability to detect the sequential changes of states. However, GMM-HMM becomes less efficient for pipeline leakage detection due to the demands of a massive amount of data to identify parameters of Gaussian mixtures.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Hybrid Hidden Markov Model for Pipeline Leakage Detection

Zhang

Chen

2021

Applied Sciences

Self Cite

View full text Add to dashboard Cite

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

show abstract

Section: Setup Of Experimentsmentioning

confidence: 99%

Section: Leakage Detection With Three Statesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Hidden Markov Model for Pipeline Leakage Detection

Zhang

Chen

2021

Applied Sciences

Self Cite

View full text Add to dashboard Cite

show abstract

“…Zhu et al utilized the PZT probe for impact detection and for positioning submarine pipelines [ 29 ]. Zhang et al put forward a Gaussian Mixture Model–Hidden Markov Model (GMM-HMM) method to detect pipeline leakage and crack depth by extracting the time-domain damage index and frequency-domain damage index from signals collected by PZT sensors [ 30 ]. However, most of the working environments of pipelines are exposed to harsh environments.…”

Section: Introductionmentioning

confidence: 99%

A Novel Pipeline Corrosion Monitoring Method Based on Piezoelectric Active Sensing and CNN

Yang

Zhang

Zhou³

et al. 2023

Sensors

View full text Add to dashboard Cite

In this study, a piezoelectric active sensing-based time reversal method was investigated for monitoring pipeline internal corrosion. An effective method that combines wavelet packet energy with a Convolutional Neural Network (CNN) was proposed to identify the internal corrosion status of pipelines. Two lead zirconate titanate (PZT) patches were pasted on the outer surface of the pipeline as actuators and sensors to generate and receive ultrasonic signals propagating through the inner wall of the pipeline. Then, the time reversal technique was employed to reverse the received response signal in the time domain, and then to retransmit it as an excitation signal to obtain the focused signal. Afterward, the wavelet packet transform was used to decompose the focused signal, and the wavelet packet energy (WPE) with large components was extracted as the input of the CNN model to rapidly identify the corrosion degree inside the pipeline. The corrosion experiments were conducted to verify the correctness of the proposed method. The occurrence and development of corrosion in pipelines were generated by electrochemical corrosion, and nine different depths of corrosion were imposed on the sample pipeline. The experimental results indicated that the classification accuracy exceeded 99.01%. Therefore, this method can quantitatively monitor the corrosion status of pipelines and can pinpoint the internal corrosion degree of pipelines promptly and accurately. The WPE-CNN model in combination with the proposed time reversal method has high application potential for monitoring pipeline internal corrosion.

show abstract

Bayesian change point prediction for downhole drilling pressures with hidden Markov models

Erivwo,

Makis,

Kwon

2023

Appl Stoch Models Bus & Ind

View full text Add to dashboard Cite

In the drilling of oil wells, the need to accurately detect downhole formation pressure transitions has long been established as critical for safety and economics. In this article, we examine the application of Hidden Markov Models (HMMs) to oilwell drilling processes with a focus on the real time evolution of downhole formation pressures in its partially observed state. The downhole drilling pressure system can be viewed as a nonlinear, non‐degrading stochastic process whose optimum performance is in a region in its warning state prior to random failure in time. The differential pressure system is modeled as a hidden 3 state continuous time Markov process. States 0 and 1 are not observable and represent the normally pressured (initiating ) and abnormally pressured or warning (reducing ) states respectively. State 2 is the observable failure state (from negative and loss of well control). The signal process of the evolution of differential pressure is identified in the changes in the observable rate of penetration (ROP) encoded in drilling performance data. The state and observation parameters of the HMM are estimated using the Expectation Maximization (EM) algorithm and we show, for a univariate system with a depth dependent time relationship, that the model parameter updates of the EM algorithm equation have explicit solutions. A Bayesian inference model, to determine the safety threshold of the system and early failure prediction at each sampling epoch, is thereafter proposed. The application of our stochastic model of the dynamic evolution of downhole pressures in operational time is illustrated with a hindcast case example. The analysis showed strong early indication of probable failure in real time and was validated in the field post drilling system failure that resulted in significant recovery costs. The potential to predict the differential pressure state transitions ahead of the bit represents a capability not currently available in the industry. This opens up significant opportunity for value creation from optimizing drilling operations to deliver substantial savings in well construction costs.

show abstract

Hidden Markov models for pipeline damage detection using piezoelectric transducers

Cited by 16 publications

References 37 publications

A Hybrid Hidden Markov Model for Pipeline Leakage Detection

A Hybrid Hidden Markov Model for Pipeline Leakage Detection

A Novel Pipeline Corrosion Monitoring Method Based on Piezoelectric Active Sensing and CNN

Bayesian change point prediction for downhole drilling pressures with hidden Markov models

Contact Info

Product

Resources

About