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.

Technology Innovations in Dementia Care: Improvements in Functional Assessments Through Background Sensing

Technological innovations are becoming commonplace in research among persons living with dementia (PLWD) and their caregivers. However, few studies attempt to validate technology’s ability to appropriately monitor functional assessment in dementia care research. Bringing together industry, academia, and health care, we demonstrate the feasibility of using a novel in-situ sensor system to continuously and accurately assess daily functions for PWLDs in home or assisted care settings. Phase1 revealed a high accuracy (~85%) of detecting and classifying ADLs between sensors and human loggers across 26 defined activities. Phase 2, which will target 140 PLWDs, has already demonstrated the value of such sensors in detecting safety concerns (e.g., no heat). Technology-driven research for PLWD and their caregivers have practical applications for assessing diverse forms of functional assessment and environmental conditions which can improve measurement precision over time and space and the ability to better tailor care plans for PLWDs and their caregivers.

Advancing Dementia Care Interventions With Technology Solutions

Over the past two decades, a number of interventions have been developed and tested to help meet the complex care needs of persons living with dementia (PLWD) and the family care support system. Despite the large foundation of empirical evidence, they are often not readily available as part of dementia care support services. Interventions leveraging technology-based solutions have the potential to bolster their desirability, efficacy, and feasibility. While progress has been made, there is still a need to design and test new innovative solutions in real-world settings. This symposium will highlight three such innovative technology solutions for dementia care and explore lessons learned in their development and testing. Smith et al. demonstrate the feasibility of using a novel in-situ sensor system to assess daily functions for PLWD in home or assisted care settings. Results of detecting and classifying diverse forms of functional assessment and environmental conditions will be discussed in the presentation. Czaja et al. describe a randomized controlled trial evaluating the feasibility and efficacy of an innovative dyadic intervention (DT) delivered through an interactive technology. Recruitment challenges and lessons learned from the feasibility of implementing a dyadic intervention will be presented. Stevens et al. introduce an online approach to delivering REACH II, GamePlan4Care (GP4C). Qualitative thematic analyses from GP4C user test sessions related to both the content and technical features will be discussed. Discussant Dr. Elena Fazio will address the role of technology solutions as a strategy within dementia care interventions and unique challenges and contributions of each project.

Development of a Thin-Film Sensor for In Situ Measurement of the Temperature Rise in Rolling Contacts with Fluid Film and Mixed Lubrication

The following study presents an in situ sensor system which can measure the temperature change of rolling contacts for heavy duty during fluid as well as mixed friction. This thin-film sensor was optimized with regard to its size, spatial resolution, and wear resistance. Extensive tests were carried out with a two-disk test rig and the data of the temperature change were presented. The results show the complex processes within a rolling contact and the strongly interaction of pressure, friction, and temperature development within the contact zone. Due to the detailed sensor and disk characterization, the data are suitable for comparing calculation methods.

Radioactivity Monitoring at North Aegean Sea Integrating In-Situ Sensor in an Ocean Observing Platform

The integration of the radioactivity spectrometer KATERINA II in a fixed station (buoy) of the POSEIDON network at the North Aegean Sea within the framework of MARRE Project is presented. The acquisition period lasted from 20 November 2019 till 22 February 2020. An intense increment of the activity concentration of radon progenies (up to an order of magnitude) was recorded during rainfall. More specifically, the 214Bi activity concentration varied from 0.09 to 0.53 Bq L−1 without rainfall and the 214Pb activity concentration varied from 0.14 to 0.81 Bq L−1. The 214Bi activity concentration during rainfall ranged from 0.4 to 5.4 Bq L−1 and of 214Pb from 0.3 to 5.3 Bq L−1. The minimum detectable activity of the KATERINA II detection system for measuring low level activities of 137Cs is optimized applying background subtraction and the full spectrum analysis technique.

Glider-based observations of CO₂ in the Labrador Sea

Ocean gliders can provide high-spatial- and temporal-resolution data and target specific ocean regions at a low cost compared to ship-based measurements. An important gap, however, given the need for carbon measurements, is the lack of capable sensors for glider-based CO2 measurements. We need to develop robust methods to evaluate novel CO2 sensors for gliders. Here we present results from testing the performance of a novel CO2 optode sensor (Atamanchuk et al., 2014), deployed on a Slocum glider, in the Labrador Sea and on the Newfoundland Shelf. This paper (1) investigates the performance of the CO2 optode on two glider deployments, (2) demonstrates the utility of using the autonomous SeaCycler profiler mooring (Send et al., 2013; Atamanchuk et al., 2020) to improve in situ sensor data, and (3) presents data from moored and mobile platforms to resolve fine scales of temporal and spatial variability of O2 and pCO2 in the Labrador Sea. The Aanderaa CO2 optode is an early prototype sensor that has not undergone rigorous testing on a glider but is compact and uses little power. Our analysis shows that the sensor suffers from instability and slow response times (τ95>100 s), affected by different behavior when profiling through small (<3 ∘C) vs. large (>10 ∘C) changes in temperature over similar time intervals. We compare the glider and SeaCycler O2 and CO2 observations and estimate the glider data uncertainty as ± 6.14 and ± 44.01 µatm, respectively. From the Labrador Sea mission, we point to short timescales (<7 d) and distance (<15 km) scales as important drivers of change in this region.