scholarly journals Sensor-integrating gears: wear detection by in-situ MEMS acceleration sensors

2021 ◽  
Author(s):  
Julian Peters ◽  
Lorenz Ott ◽  
Matthias Dörr ◽  
Thomas Gwosch ◽  
Sven Matthiesen
Keyword(s):  
Regression Model ◽  
Data Quality ◽  
Gear Tooth ◽  
Measurement Data ◽  
Tooth Wear ◽  
Sensor Data ◽  
Acceleration Sensors ◽  
In Situ Sensors ◽  
In Situ Sensor

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.

scholarly journals ESTIMATING AND MAPPING CHLOROPHYLL-A CONCENTRATION OF PHEWA LAKE OF KASKI DISTRICT USING LANDSAT IMAGERY

2019 ◽  
Vol IV-5/W2 ◽  
pp. 127-132
Author(s):  
N. Wagle ◽  
R. Pote ◽  
R. Shahi ◽  
S. Lamsal ◽  
S. Thapa ◽  
...  
Keyword(s):  
Regression Model ◽  
Chlorophyll A ◽  
Algal Blooms ◽  
Landsat Imagery ◽  
Measurement Data ◽  
Landsat 8 ◽  
Chl A ◽  
Gis Techniques ◽  
The One

Abstract. Water is a major component in the living ecosystem. As water quality is degrading due to human intervention, continuous monitoring is necessary. One of the indicators is Chlorophyll-a (Chl-a) which indicates algal blooms which are often driven by eutrophication phenomena in freshwater. Lakes should be monitored for Chl-a because Chla-a is related to eutrophication phenomena which are an enrichment of water by nutrients salt. When the environment becomes enriched with nutrients the excessive growth can lead to the death of fish. In this study, the Remote Sensing (RS) and Geographic Information System (GIS) techniques were utilized to determine Chl-a concentration of Phewa Lake of Kaski district. We used Landsat 8 satellite imagery for estimation and mapping of the Chl-a concentration. In-situ measurements from different sample points were taken and used to form a regression model for Chl-a and its concentration over the water body was calculated. The preceding year’s (2016) in situ measurement data of Chl-a concentration at a specific location were assessed with the one evaluated from the regression model thus produced for the succeeding year (2017) using Root Mean Square Error (RMSE) technique. As a result, we concluded that the estimation and mapping of Chl-a of a lake in Nepal can be done with the help of RS and GIS techniques.

scholarly journals Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review

2021 ◽  
Vol 11 (24) ◽  
pp. 11910
Author(s):  
Dalia Mahmoud ◽  
Marcin Magolon ◽  
Jan Boer ◽  
M.A Elbestawi ◽  
Mohammad Ghayoomi Mohammadi
Keyword(s):  
Machine Learning ◽  
Defect Detection ◽  
In Situ Monitoring ◽  
Sensor Data ◽  
Learning Approaches ◽  
Monitoring And Control ◽  
In Situ Sensors ◽  
Applications Of Machine Learning ◽  
And Control

One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.

Information fusion infrastructure for remote-sensing and in-situ sensor data to model people dynamics

2014 ◽  
Vol 5 (1) ◽  
pp. 54-69 ◽  
Author(s):  
Florian Hillen ◽  
Bernhard Höfle ◽  
Manfred Ehlers ◽  
Peter Reinartz
Keyword(s):  
Remote Sensing ◽  
Information Fusion ◽  
Sensor Data ◽  
In Situ Sensor

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

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.

Forecasting Seasonal Streamflow Using a Stacked Recurrent Neural Network

2020 ◽  
Author(s):  
David Lambl ◽  
Dan Katz ◽  
Eliza Hale ◽  
Alden Sampson
Keyword(s):  
Remote Sensing ◽  
Hydrological Modeling ◽  
Short Term Memory ◽  
Remote Sensing Data ◽  
Sensor Data ◽  
Historical Information ◽  
Sensing Data ◽  
The Future ◽  
In Situ Sensors

<p>Providing accurate seasonal (1-6 months) forecasts of streamflow is critical for applications ranging from optimizing water management to hydropower generation. In this study we evaluate the performance of stacked Long Short Term Memory (LSTM) neural networks, which maintain an internal set of states and are therefore well-suited to modeling dynamical processes.</p><p>Existing LSTM models applied to hydrological modeling use all available historical information to forecast contemporaneous output. This modeling approach breaks down for long-term forecasts because some of the observations used as input are not available in the future (e.g., from remote sensing and in situ sensors). To solve this deficiency we train a stacked LSTM model where the first network encodes the historical information in its hidden states and cells. These states and cells are then used to initialize the second LSTM which uses meteorological forecasts to create streamflow forecasts at various horizons. This method allows the model to learn general hydrological relationships in the temporal domain across different catchment types and project them into the future up to 6 months ahead.</p><p>Using meteorological time series from NOAA’s Climate Forecast System (CFS), remote sensing data including snow cover, vegetation and surface temperature from NASA’s MODIS sensors, SNOTEL sensor data, static catchment attributes, and streamflow data from USGS we train a stacked LSTM model on 100 basins, and evaluate predictions on out-of-sample periods from these same basins. We perform sensitivity analysis on the effects of remote sensing data, in-situ sensors, and static catchment attributes to understand the informational content of these various inputs under various model architectures. Finally, we benchmark our model to forecasts derived from simple climatological averages and to forecasts created by a single LSTM that excludes all inputs without forecasts.</p><p> </p>

scholarly journals The Soil Moisture Active Passive Marena, Oklahoma, In Situ Sensor Testbed (SMAP-MOISST): Testbed Design and Evaluation of In Situ Sensors

2016 ◽  
Vol 15 (4) ◽  
pp. vzj2015.09.0122 ◽  
Author(s):  
Michael H. Cosh ◽  
Tyson E. Ochsner ◽  
Lynn McKee ◽  
Jingnuo Dong ◽  
Jeffrey B. Basara ◽  
...  
Keyword(s):  
Soil Moisture ◽  
In Situ Sensors ◽  
In Situ Sensor

A Framework of In-Situ Sensor Data Processing System for Context Awareness

2006 ◽  
pp. 124-129 ◽  
Author(s):  
Young Jin Jung ◽  
Yang Koo Lee ◽  
Dong Gyu Lee ◽  
Mi Park ◽  
Keun Ho Ryu ◽  
...  
Keyword(s):  
Data Processing ◽  
Context Awareness ◽  
Processing System ◽  
Sensor Data ◽  
Data Processing System ◽  
In Situ Sensor
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