Parallelism, Performance, and Energy-Efficiency Tradeoffs for In Situ Sensor Data Processing

AbstractGear tooth wear is a common phenomenon leading to malfunctions in machines. To detect wear and faults, gear condition monitoring by vibration is established. The problem is that the measurement data quality for detection of wear by vibration is not good enough with currently established measurement methods, caused by long signal paths of the commonly used housing mounted sensors. In-situ sensors directly at the gear achieve better data quality, but are not yet proved in wear detection. Further it is unknown what analysis methods are suited for in-situ sensor data. Existing gear condition metrics are mainly focused on localized gear tooth faults, and do not estimate wear related values. This contribution aims to improve wear detection by investigating in-situ sensors and advance gear condition metrics. Using a gear test rig to conduct an end of life test, the wear detection ability of an in-situ sensor system and reference sensors on the bearing block are compared through standard gear condition metrics. Furthermore, a machine-learned regression model is developed that maps multiple features related to gear dynamics to the gear mass loss. The standard gear metrics used on the in-situ sensor data are able to detect wear, but not significantly better compared to the other sensors. The regression model is able to estimate the actual wear with a high accuracy. Providing a wear related output improves the wear detection by better interpretability.

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X-ray Computed Tomography Data ofAdditive Manufacturing Metrology Testbed (AMMT) Parts: “Overhang Part X4”

Journal of Research of the National Institute of Standards and Technology ◽

10.6028/jres.125.031 ◽

2020 ◽

Vol 125 ◽

Author(s):

Max Praniewicz ◽

Brandon Lane ◽

Felix Kim ◽

Christopher Saldana

Keyword(s):

Additive Manufacturing ◽

Three Dimensional ◽

Sensor Data ◽

X Ray ◽

Surface Data ◽

Design Build ◽

X Ray Computed ◽

In Situ Sensor ◽

Stereolithography Files

This document provides details on the data and files generated from post-build X-ray computedtomography (XCT) measurements of the four parts built as part of the “Overhang Part X4” dataset. The “Overhang Part X4” dataset was a three-dimensional (3D) additive manufacturing (AM) build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung and Brandon Lane on June 28, 2019. The files discussed in this document include image sequences for each part, stereolithography files (.STL) of the surface data extracted from XCT. This data is one of a set of “AMMT Process Monitoring Datasets”, as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project at the National Institute of Standards and Technology (NIST). In-situ sensor data, part design, build command and scan strategy data, materials, and associated metadata for this build are described in Ref. [1]. Readers should refer to the AMMT datasets web page for updates.

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A geographical approach for on-the-fly prioritizing social-media messages for flood risk management based on sensor data

10.5753/brasnam.2015.6768 ◽

2015 ◽

Author(s):

Luiz Fernando Assis ◽

Flavio Horita ◽

Benjamin Herfort ◽

Enrico Steiger ◽

João Porto De Albuquerque

Keyword(s):

Social Media ◽

Risk Management ◽

Flood Risk ◽

Flood Risk Management ◽

Sensor Data ◽

Accurate Information ◽

Media Messages ◽

Hypothetical Scenario ◽

In Situ Sensor

Flood risk management requires updated and accurate information about the overall situation in vulnerable areas. Social media messages are considered to be as a valuable additional source of information to complement authoritative data (e.g. in situ sensor data). In some cases, these messages might also help to complement unsuitable or incomplete sensor data, and thus a more complete description of a phenomenon can be provided. Nevertheless, it remains a difficult matter to identify information that is significant and trustworthy. This is due to the huge volume of messages that are produced and which raises issues regarding their authenticity, confidentiality, trustworthiness, ownership and quality. In light of this, this paper adopts an approach for on-the-fly prioritization of social media messages that relies on sensor data (esp. water gauges). A proof-of-concept application of our approach is outlined by means of a hypothetical scenario, which uses social media messages from Twitter as well as sensor data collected through hydrological stations networks maintained by Pegelonline in Germany. The results have shown that our approach is able to prioritize social media messages and thus provide updated and accurate information for supporting tasks carried out by decision-makers in flood risk management.

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