Denoising autoencoder in damage detection of pipeline using guided ultrasonic wave

Pipeline condition monitoring is essential in critical sectors such as the petrochemical, nuclear and energy sectors. The guided ultrasonic wave (GUW) monitoring system is an available pipeline condition monitoring system that is gaining much attention owing to its portability, long coverage and high sensitivity to damage. However, environmental and operational conditions (EOCs) effects, especially temperature and random noise may generate unwanted peaks, which are falsely identified as damage. Attempts to deal with EOC effects have not solved the problem, especially for small damage cases (damage equal to or less than 5% cross sectional area loss (CSAL)). In this study, a new damage feature extraction method based on the residual reliability criterion (RRC) is proposed. The performance of the proposed method is measured using the established receiver operating characteristics (ROCs) performance evaluation method. The findings show that this method performs well, with an AUC value greater than 0.9, based on numerical model under 40 ? variations and 10% random noise level, and that the application of RRC is intuitively simple. To ensure the practicality of the method, a 6 metre long, 8 inches diameter experimental pipe model filled with liquid is used to form a GUW database of small damage under 30 ? variations by using Torsional T(0,1) excitation mode at 26 kHz centre frequency. However, the RRC underperformed when experimental data is used because the random noise generated by healthy and damaged signals interferes and generates high amplitude noise. Therefore, this study proposed a denoising autoencoder (DAE) neural network to deal with the effects of EOCs. A DAE decodes high-dimensional data into low-dimensional features and reconstructs the original data from these low-dimensional features. By providing GUW signals at a reference temperature, this structure forces the DAE to learn the essential features hidden within complex data. The proposed DAE showed perfect detection (AUC value of 1.0) using numerical model and performs well (AUC greater than 0.9) using experimental model in terms of small damage identification. Moreover, the proposed method showed superiority among other advanced EOC compensation techniques using both numerical and experimental models

Nuclear Power - System Simulations and Operation

At the onset of the 21st century, we are searching for reliable and sustainable energy sources that have a potential to support growing economies developing at accelerated growth rates, technology advances improving quality of life and becoming available to larger and larger populations. The quest for robust sustainable energy supplies meeting the above constraints leads us to the nuclear power technology. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. Catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, requirements and facilitated growing interests in designs, which can withstand natural disasters and avoid catastrophic consequences. This book is one in a series of books on nuclear power published by InTech. It consists of ten chapters on system simulations and operational aspects. Our book does not aim at a complete coverage or a broad range. Instead, the included chapters shine light at existing challenges, solutions and approaches. Authors hope to share ideas and findings so that new ideas and directions can potentially be developed focusing on operational characteristics of nuclear power plants. The consistent thread throughout all chapters is the system-thinking approach synthesizing provided information and ideas. The book targets everyone with interests in system simulations and nuclear power operational aspects as its potential readership groups - students, researchers and practitioners

Nuclear Power

At the onset of the 21st century, we are searching for reliable and sustainable energy sources that have a potential to support growing economies developing at accelerated growth rates, technology advances improving quality of life and becoming available to larger and larger populations. The quest for robust sustainable energy supplies meeting the above constraints leads us to the nuclear power technology. Today's nuclear reactors are safe and highly efficient energy systems that offer electricity and a multitude of co-generation energy products ranging from potable water to heat for industrial applications. Catastrophic earthquake and tsunami events in Japan resulted in the nuclear accident that forced us to rethink our approach to nuclear safety, requirements and facilitated growing interests in designs, which can withstand natural disasters and avoid catastrophic consequences. This book is one in a series of books on nuclear power published by InTech. It consists of ten chapters on system simulations and operational aspects. Our book does not aim at a complete coverage or a broad range. Instead, the included chapters shine light at existing challenges, solutions and approaches. Authors hope to share ideas and findings so that new ideas and directions can potentially be developed focusing on operational characteristics of nuclear power plants. The consistent thread throughout all chapters is the "system-thinking" approach synthesizing provided information and ideas. The book targets everyone with interests in system simulations and nuclear power operational aspects as its potential readership groups - students, researchers and practitioners

Structural Health Monitoring of Pipelines in Radioactive Environments Through Acoustic Sensing and Machine Learning

Structural health monitoring (SHM) comprises multiple methodologies for the detection and characterization of stress, damage, and aberrations in engineering structures and equipment. Although, standard commercial engineering operations may freely adopt new technology into everyday operations, the nuclear industry is slowed down by tight governmental regulations and extremely harsh environments. This work aims to investigate and evaluate different sensor systems for real-time structural health monitoring of piping systems and develop a novel machine learning model to detect anomalies from the sensor data. The novelty of the current work lies in the development of an LSTM-autoencoder neural network to automate anomaly detection on pipelines based on a fiber optic acoustic transducer sensor system. Results show that pipeline events and faults can be detected by the MLM developed, with a high degree of accuracy and low rate of false positives even in a noisy environment near pumps and machinery

Determination of the thermal characteristic of the ground in Cyprus and their effect on ground heat exchangers

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.Since the ancient years, human beings were using holes and caves to protect themselves from weather conditions making it the first known form of exploiting ground’s heat, known as Geothermal Energy. Nowadays, geothermal energy is mainly used for electricity production, space heating and cooling, Ground Coupled Heat Pump (GCHP) applications, and many other purposes depending on the morphology of the ground and its temperature. This study presents results of investigations into the evaluation of the thermal properties of the ground in Cyprus. The main objectives were i) to determine the thermal characteristics of the ground in Cyprus, ii) investigate how they affect the sizing and positioning of Ground Heat Exchangers (GHE) and iii) present the results for various ground depths, including a temperature map of the island, as a guide for engineers and specifiers of GCHPs. It was concluded that there is a potential for the efficient exploitation of the thermal properties of the ground in Cyprus for geothermal applications leading to significant savings in power and money as well. Six new boreholes were drilled and two existing ones were used for the investigation and determination of i) the temperature of the ground at various depths, ii) its thermal conductivity, iii) its specific heat and iv) its density. The thermal conductivity was determined by carrying out experiments using the line source method and was found to vary in the range between 1.35 and 2.1 W/mK. It was also observed that the thermal conductivity is strongly affected by the degree of saturation of the ground. The temperature of the undisturbed ground in the 8 borehole locations was recorded monthly for a period of 1 year. The investigations showed that the surface zone reaches a depth of 0.25 m and the shallow zone 7 to 8 m. The undisturbed ground temperature in the deep zone was measured to be in the range of 18.3 °C to 23.6 °C and is strongly dependent on the soil type. Since the ground temperature is a vital parameter in ground thermal applications, the temperature of the ground in locations that no information is available was predicted using Artificial Neural Networks and the temperature map of the island at depths of 20 m, 50 m and 100 m was generated. Data obtained at the location of each borehole were used for the training of the network. Data for the sizing of GHEs based on the ground properties of Cyprus were presented in an easily accessible form so that they can be used as a guide for preliminary system sizing calculations. With the aid of Computational Fluid Dynamics (CFD) software the capacity of the GHEs in each location and the optimum distance between them was estimated. Additionally, the long term temperature variation of the ground was investigated. For the first time since a limited study in the 1970’s, a research focusing on the determination and presentation of the thermal properties of the ground in Cyprus has been carried out. Additionally, the use of Artificial Neural Networks (ANNs) is an innovative approach for the prediction of data at locations where no information is available. The publication of this information not only contributes to knowledge locally but also internationally as it enables comparison with other countries with similar climatic conditions to be carried out.Research Promotion Foundation of Cypru

Dynamic reliability model for subsea pipeline risk assessment due to third-party interference

This research was sponsored by the Ministry of Finance of the Republic of Indonesia through the Indonesia Endowment Fund for Education (LPDP RI) (grant number: PRJ-4202 /LPDP.3/2016).Peer reviewedPublisher PD

Condition monitoring through advanced sensor and computational technology : final report (January 2002 to May 2005).

The overall goal of this joint research project was to develop and demonstrate advanced sensors and computational technology for continuous monitoring of the condition of components, structures, and systems in advanced and next-generation nuclear power plants (NPPs). This project included investigating and adapting several advanced sensor technologies from Korean and US national laboratory research communities, some of which were developed and applied in non-nuclear industries. The project team investigated and developed sophisticated signal processing, noise reduction, and pattern recognition techniques and algorithms. The researchers installed sensors and conducted condition monitoring tests on two test loops, a check valve (an active component) and a piping elbow (a passive component), to demonstrate the feasibility of using advanced sensors and computational technology to achieve the project goal. Acoustic emission (AE) devices, optical fiber sensors, accelerometers, and ultrasonic transducers (UTs) were used to detect mechanical vibratory response of check valve and piping elbow in normal and degraded configurations. Chemical sensors were also installed to monitor the water chemistry in the piping elbow test loop. Analysis results of processed sensor data indicate that it is feasible to differentiate between the normal and degraded (with selected degradation mechanisms) configurations of these two components from the acquired sensor signals, but it is questionable that these methods can reliably identify the level and type of degradation. Additional research and development efforts are needed to refine the differentiation techniques and to reduce the level of uncertainties

Liquid Transport Pipeline Monitoring Architecture Based on State Estimators for Leak Detection and Location

This research presents the implementation of optimization algorithms to build auxiliary signals that can be injected as inputs into a pipeline in order to estimate —by using state observers—physical parameters such as the friction or the velocity of sound in the fluid. For the state estimator design, the parameters to be estimated are incorporated into the state vector of a Liénard-type model of a pipeline such that the observer is constructed from the augmented model. A prescribed observability degree of the augmented model is guaranteed by optimization algorithms by building an optimal input for the identification. The minimization of the input energy is used to define the optimality of the input, whereas the observability Gramian is used to verify the observability. Besides optimization algorithms, a novel method, based on a Liénard-type model, to diagnose single and sequential leaks in pipelines is proposed. In this case, the Liénard-type model that describes the fluid behavior in a pipeline is given only in terms of the flow rate. This method was conceived to be applied in pipelines solely instrumented with flowmeters or in conjunction with pressure sensors that are temporarily out of service. The design approach starts with the discretization of the Liénard-type model spatial domain into a prescribed number of sections. Such discretization is performed to obtain a lumped model capable of providing a solution (an internal flow rate) for every section. From this lumped model, a set of algebraic equations (known as residuals) are deduced as the difference between the internal discrete flows and the nominal flow (the mean of the flow rate calculated prior to the leak). The residual closest to zero will indicate the section where a leak is occurring. The main contribution of our method is that it only requires flow measurements at the pipeline ends, which leads to cost reductions. Some simulation-based tes