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

Gear 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.

Comparison of EO4Agri Recommendations With In-Depth Literature Review

Copernicus is Europe's space-based Earth monitoring asset, which consists of a complex set of systems that collect data from different sources: remote sensing satellites (RS) and in-situ sensors such as ground stations, airborne and marine sensors. This study was originally prepared for the needs of the Czech agricultural community, where we provided an in-depth analysis of articles related to Earth observation in precision agriculture. At a later stage, we extended this study by comparing the recommendations of the European EO4Agri project and scientific articles published in MDPI. We had two important objectives, one was to validate the results of the EO4Agri project and the other was to look for gaps in current research and community needs. To recognize the importance of using Sentinel 1 data, we also added a specific analysis of methods for data fusion of Sentinel 1 and Sentinel 2 data.

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

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.

Analyzing Effects of Crops on SMAP Satellite-based Soil Moisture using a Rainfall-Runoff Model in the U.S. Corn Belt

L-band microwave satellite missions provide soil moisture information potentially useful for streamflow and hence flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture Ocean Salinity), and two distributed hydrologic models with measurements from in~situ sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using in~situ data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflow predictions.

Reconstructing Missing and Anomalous Data Collected from High-Frequency In-Situ Sensors in Fresh Waters

In situ sensors that collect high-frequency data are used increasingly to monitor aquatic environments. These sensors are prone to technical errors, resulting in unrecorded observations and/or anomalous values that are subsequently removed and create gaps in time series data. We present a framework based on generalized additive and auto-regressive models to recover these missing data. To mimic sporadically missing (i) single observations and (ii) periods of contiguous observations, we randomly removed (i) point data and (ii) day- and week-long sequences of data from a two-year time series of nitrate concentration data collected from Arikaree River, USA, where synoptically collected water temperature, turbidity, conductance, elevation, and dissolved oxygen data were available. In 72% of cases with missing point data, predicted values were within the sensor precision interval of the original value, although predictive ability declined when sequences of missing data occurred. Precision also depended on the availability of other water quality covariates. When covariates were available, even a sudden, event-based peak in nitrate concentration was reconstructed well. By providing a promising method for accurate prediction of missing data, the utility and confidence in summary statistics and statistical trends will increase, thereby assisting the effective monitoring and management of fresh waters and other at-risk ecosystems.

Novel Cosmic Ray Neutron Sensor Accurately Captures Field-Scale Soil Moisture Trends under Heterogeneous Soil Textures

Soil moisture is a critical variable influencing plant water uptake, rainfall-runoff partitioning, and near-surface atmospheric conditions. Soil moisture measurements are typically made using either in-situ sensors or by collecting samples, both methods which have a small spatial footprint or, in recent years, by remote sensing satellites with large spatial footprints. The cosmic ray neutron sensor (CRNS) is a proximal technology which provides estimates of field-averaged soil moisture within a radius of up to 240 m from the sensor, offering a much larger sensing footprint than point measurements and providing field-scale information that satellite soil moisture observations cannot capture. Here we compare volumetric soil moisture estimates derived from a novel, less expensive lithium (Li) foil-based CRNS to those from a more expensive commercially available 3He-based CRNS, to measurements from in-situ sensors, and to four intensive surveys of soil moisture in a field with highly variable soil texture. Our results indicate that the accuracy of the Li foil CRNS is comparable to that of the commercially available sensors (MAD = 0.020 m3 m−3), as are the detection radius and depth. Additionally, both sensors capture the influence of soil textural variability on field-average soil moisture. Because novel Li foil-based CRNSs are comparable in accuracy to and much less expensive than current commercially available CRNSs, there is strong potential for future adoption by land and water managers and increased adoption by researchers interested in obtaining field-scale estimates of soil moisture to improve water conservation and sustainability.

Design of an Interactive Cellular System for the Remote Operation of Ocean Sensors: A Pilot Study Integrating Radioactivity Sensors

Ocean in-situ sensors are crucial for measuring oceanic parameters directly from the sea in a spatial and temporal basis. Real-time operation is used in many applications related to decision support tools and early warning services in case of accidents, incidents and/or disasters. The design of the proposed system is described as a rapid-response detection system, which aims to measure natural and artificial radioactive contaminants or other crucial ocean parameters, to replace the traditional method of sampling. The development of an interactive cellular system is undertaken using a commercial router that is programmed according to sensor specifications. A radioactivity sensor is integrated in a communication box enabling self-powered operation with a solar panel. The proposed system operates in (near) real-time mode and provides gamma-ray spectra by integrating the sensor and the appropriate electronic modules in it. Additionally, an on-site experiment was conducted to test the operability of the system in a real environment close to the sea, for monitoring fallout due to rainfall and snowfall events. The main intense radionuclides that were observed by different energy lines, were radon progenies (214Bi, 214Pb). The continuous operation of the whole system was controlled by operating the system during the winter period.

Internet-of-Things-Based Geotechnical Monitoring Boosted by Satellite InSAR Data

Landslides, often a side effect of mining activities, pose a significant risk to humans and infrastructures such as urban areas, power lines, and dams. Operational ground motion monitoring can help detect the spatial pattern of surface changes and their evolution over time. In this technical note, a commercial, cost-effective method combining a network of geotechnical surface sensors with the InSAR data was reported for the first time to accurately monitor surface displacement. The correlation of both data sets is demonstrated in the Gediminas Castle testbed, where slope failure events were detected. Two specific events were analyzed, and possible causes proposed. The combination of techniques allows one to detect the precursors of the events and characterize the consequences of the failures in different areas in proximity to the castle walls, since the solution allows for the confirmation of long-term drifts and sudden movements in real time. The data from the in situ sensors were also used to refine the satellite data analysis. The results demonstrate that not all events pose a direct threat to the safety of the structure monitored.

LARGE-SCALE SUBSIDENCE GEOHAZARD MONITORING WITH SENTINEL-1 SAR INTERFEROMETRY IN CENTRAL LISHUI (CHINA)

Ground subsidence has become a serious problem along with the rapid urban expansions. Compared with traditional point-based ground survey techniques (GPS, levelling measurement and in-situ sensors), SAR Interferometry are quite appreciated for large-scale subsidence monitoring with long term and high accuracy. In this study, we focused on large-scale subsidence geohazard monitoring of central Lishui (China) and extracted subsidence velocity map of Liandu District. 57 Sentinle-1 SAR images from April 2019 to September 2020 are analysed with SBAS-InSAR technique. The overall subsidence of Liandu is significantly correlated with the distributions of construction engineering sites with displacement velocity of approximately 30–60 mm/yr. Various types of urban ground subsidence could be identified, including the overall settlement of large construction site, the slope deformation of construction excavation, significant settlement of refuse landfill and mountain crossing tunnel, and small deformation of highvoltage towers in mountainous areas. Our results indicated that the rapid urban developments are the dominant impact factors of subsidence in Lishui, China.