For pipeline leak detection air-random signals detection estimation and data analysis pdf acoustic signals are remotely captured. A method to find the key predictors that affect leak distinction is presented.
Data conversion of the acoustic signals facilitates distinguishing pipeline leaks. Performance metrics are derived by several learning algorithms for classification. Experiments show leaks can be better distinguished using less amount of predictors. Acoustic monitoring techniques are widely adopted for identifying various leaks from plant facilities to prevent loss of resources and any further structural damages. As the conventional sensing devices have measured acoustic signals at predesignated positions inside or very close to the object being observed, the need for more sophisticated and automated monitoring of more complex infrastructure has increased both the number of sensors to be installed and the amount of data to be analyzed. Thus, in order to diagnose the high-pressure steam leakage efficiently, this research proposes a novel method to find and condense the distinguishable features from the acoustic signals, which are captured by remotely dispersed microphone sensor nodes around a laboratory scale nuclear power plant coolant system. The performance of the proposed method is evaluated by several quantitative metrics resulting from the five state-of-the-art machine learning algorithms, together with the condensed data ratio.
Experimental results show that the proposed method can transform the original acoustic signals into a smaller number of featured predictors, even less than ten-thousandths of the original data amount, while improving classification accuracy despite loud machine-driven noises nearby. Check if you have access through your login credentials or your institution. The purpose of this page is to provide resources in the rapidly growing area of computer-based statistical data analysis. Topics include questionnaire design and survey sampling, forecasting techniques, computational tools and demonstrations. This site offers information on statistical data analysis. It describes time series analysis, popular distributions, and other topics. It examines the use of computers in statistical data analysis.
It also lists related books and links to related Web sites. Enter a word or phrase in the dialogue box, e. Why Is Every Thing Priced One Penny Off the Dollar? What is Statistical Data Analysis? What Is a Geometric Mean?
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What Is a Regression Tree? What is Intelligent Numerical Computation? Developments in the field of statistical data analysis often parallel or follow advancements in other fields to which statistical methods are fruitfully applied. Because practitioners of the statistical analysis often address particular applied decision problems, methods developments is consequently motivated by the search to a better decision making under uncertainties.
Decision making process under uncertainty is largely based on application of statistical data analysis for probabilistic risk assessment of your decision. Managers need to understand variation for two key reasons. Therefore, it is a course in statistical thinking via a data-oriented approach. Statistical models are currently used in various fields of business and science. Employees waste time scouring multiple sources for a database. The decision-makers are frustrated because they cannot get business-critical data exactly when they need it.
Many opportunities are also missed, if they are even noticed at all. Knowledge is what we know well. Information is the communication of knowledge. In every knowledge exchange, there is a sender and a receiver. The sender make common what is private, does the informing, the communicating. The explicit information can be explained in structured form, while tacit information is inconsistent and fuzzy to explain.