Wireless Nde Sensor System for Continuous Monitoring
21 Jun 2011-Vol. 1335, Iss: 1, pp 1758-1765
TL;DR: This paper presents a wireless system for continuous monitoring, identification of anomalous events, NDE data acquisition and data transfer that is integrated with a wireless radio unit such as a MICA mote.
Abstract: For continuous monitoring of power‐plant components, the use of in‐situ sensors (i.e., sensors that are permanently mounted on the structure) is necessary. In‐situ wired sensors require an unrealistic amount of cabling for power and data transfer, which can drive up costs of installation and maintenance. In addition, the use of cabling in hostile environments (high temperature/pressure environments) is not a viable option. This paper presents a wireless system for continuous monitoring, identification of anomalous events, NDE data acquisition and data transfer. NDE sensors are integrated with a wireless radio unit such as a MICA mote. Measurements from the sensors are typically acquired at prescribed intervals, encoded and compressed, and transmitted to a central processing server, where appropriate signal processing techniques may be used to filter out noise in the measurements, enhance the desired signal and quantify the damage in terms of severity.
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TL;DR: In this paper , a new framework for data management of IoT applications is designed and implemented, called MLADCF (Machine Learning Analytics-based Data Classification Framework), which is a two-stage framework that combines a regression model and a Hybrid Resource Constrained KNN (HRCKNN).
Abstract: In applications of the Internet of Things (IoT), where many devices are connected for a specific purpose, data is continuously collected, communicated, processed, and stored between the nodes. However, all connected nodes have strict constraints, such as battery usage, communication throughput, processing power, processing business, and storage limitations. The high number of constraints and nodes makes the standard methods to regulate them useless. Hence, using machine learning approaches to manage them better is attractive. In this study, a new framework for data management of IoT applications is designed and implemented. The framework is called MLADCF (Machine Learning Analytics-based Data Classification Framework). It is a two-stage framework that combines a regression model and a Hybrid Resource Constrained KNN (HRCKNN). It learns from the analytics of real scenarios of the IoT application. The description of the Framework parameters, the training procedure, and the application in real scenarios are detailed. MLADCF has shown proven efficiency by testing on four different datasets compared to existing approaches. Moreover, it reduced the global energy consumption of the network, leading to an extended battery life of the connected nodes.
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TL;DR: This paper presents a wireless sensor system that could be interfaced with piezoelectric transducers for the identification of anomalous events using ultrasonic techniques and power aware algorithms are used to coordinate the actuator-sensor network interaction with a central processing server.
Abstract: Continuous structural health monitoring (SHM) uses permanently mounted sensor networks on critical locations of a structural component. In-situ wired sensors require a large amount of cabling for power and data transfer, which can drive up costs of installation and maintenance. Hence the need for developing wireless sensors for SHM. The major obstacles preventing the widespread use of wireless sensor networks (WSN) for SHM is the availability of portable, low cost, low powered, low footprint, and high SNR based instrumentation. This paper presents a wireless sensor system that could be interfaced with piezoelectric transducers for the identification of anomalous events using ultrasonic techniques. Power aware algorithms are used to coordinate the actuator-sensor network interaction with a central processing server, where appropriate signal processing techniques are used to quantify the damage in terms of severity.
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TL;DR: In this article, the authors discuss technologies needed to ensure safe and effective operation of the existing US nuclear power plant fleet and discuss the increased need for condition-based maintenance applied to active components.
Abstract: There is growing interest in longer-term operation of the current US nuclear power plant fleet. This paper will discuss technologies needed to ensure safe and effective operation of the existing fleet. There will be an increased need for condition-based maintenance applied to active components. It is increasingly recognized that new and advanced sensors and systems will be needed to enable the move from current NDE, with periodic inspections and a find-and-fix approach, to prognostics: using online monitoring to enable operators to proactively manage systems with prediction of remaining useful life.
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