The early-warning model of equipment chain in gas pipeline based on DNN-HMM

Qiu, Jingwei; Liang, Wei; Zhang, Laibin; Yu, Xuchao; Zhang, Meng

doi:10.1016/j.jngse.2015.10.036

Cited by 30 publications

(13 citation statements)

References 26 publications

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“…Kuremoto et al, [155] applied DBN composed of two Restricted Botzmann Machines (RBM) to capture the input feature distribution and then optimized the size of the network and learning rate through Particle Swarm Optimization for forecasting purposes with time series data. Qiu et al, [156] proposed an early warning model where feature extraction through DNN with hidden state analysis of Hidden Markov Model (HMM) is carried out for health maintenance of equipment chain in gas pipeline. Gugulothu et al [157] proposed a forecasting scheme using a Recurrent Neural Network (RNN) model to generate embeddings which capture the trend of multivariate time series data which are supposed to be disparate for healthy and unhealthy devices.…”

Section: Prognostics and Health Managementmentioning

confidence: 99%

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

Sengupta¹,

Basak²,

Saikia³

et al. 2019

Preprint

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Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perception about the efficacy of deep neural architectures in complex pattern recognition tasks grows, sequentially up-to-date primers on the current state of affairs must follow. In this review, we seek to present a refresher of the many different stacked, connectionist networks that make up the deep learning architectures followed by automatic architecture optimization protocols using multi-agent approaches. Further, since guaranteeing system uptime is fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale with information.

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Section: Prognostics and Health Managementmentioning

confidence: 99%

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

Sengupta¹,

Basak²,

Saikia³

et al. 2019

Preprint

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show abstract

“…Recent works of machine learning have shown the great power of a deep architecture to learn highly non-linear and complicated patterns in data (Qiu et al, 2015). Inspired by these works, a SAE is developed in this work, made of several auto-encoders stacked one on top of another, in which the input of the upper layer is taken from the output of the lower hidden layer.…”

Section: Stacked Auto-encodersmentioning

confidence: 99%

“…Indeed, the complicated dynamic behaviors of a gas pipeline network can be "learned" by deep learning without prior knowledge, and the system dynamics can be accurately predicted. Although deep learning has been applied in the area of natural gas pipeline modeling and analysis, most of the works focus on the analysis of single units (such as compressor stations) or pipelines (Qiu et al, 2015). The application for the analysis of the dynamics of a complex pipeline network needs to be further explored.…”

Section: Introductionmentioning

confidence: 99%

A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

Zio

Zhang

et al. 2018

Journal of Natural Gas Science and Engineering

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et al.. A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks. A B S T R A C TThe current framework of management of natural gas pipeline systems, based on off-line simulation, is facing challenges because of the increasing complexity, uncertainty and a number of time-dependent factors. To be effective, it requires comprehensive knowledge of system characteristics, accurate initial and boundary conditions. In an attempt to circumvent these problems, in this work we propose to use the deep learning method in the natural gas transmission system operation and management context. A data-driven prediction method is developed from real-time data of operation pressure and gas consumption. Specifically, the deep learning method is combined with the data window method and structural controllability theory to predict the conditions of gas pipeline network components. The data window method is applied to reconstruct the data structure and build a "memory" for the deep learning method. Structural controllability theory is applied to extract critical parameters, for reducing the problem size. The developed method allows accurate and efficient predictions, especially in abnormal conditions. For demonstration, the method is applied to a complex gas pipeline network. The results show that the developed method can provide accurate real-time predictions useful for reducing potential losses in operation, and perform efficient and effective management of the gas pipeline system. In the case study, the average prediction accuracy is higher than 0.99.

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“…Meanwhile, problems such as resource waste and environmental pollution caused by pipeline leakage also exist. Therefore, real-time monitoring for pipelines’ status is essential [1,2,3]. In the process of pipeline monitoring, the research of OFPS mainly focused on monitoring and recognition of vibration signals [4,5].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition

Feng

Zhang

et al. 2019

Sensors

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Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals.

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The early-warning model of equipment chain in gas pipeline based on DNN-HMM

Cited by 30 publications

References 26 publications

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition

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