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At least 919 records · Page 51

To Fail or not to Fail: An Exploration of Machine Learning Techniques for Predictive Maintenance

Predictive maintenance refers to the ability to predict when machinery or systems need to be maintained. Making an accurate prediction is quite challenging given the costs for both over-estimating (unnecessary maintenance and reduction in availability of assets) and under-estimating (untimely breakdowns and possible loss of equipment or lives). To address these challenges researchers were able to develop new approaches for analyzing oil samples taken extracting samples from oil-wetted machinery that may provide information critical to developing predictive capabilities. We consider the problem from both supervised (though data limited) and unsupervised approaches and provide a first look into a data driven approach for identification of condition indicators. Through this work we identify a collection of candidate features that can form the basis of condition indicators for both a high level discrimination of failure vs. normal operation as well as a set for potential failure mode identification. Finally, we present an anomaly detection framework for detecting failures which can be a viable solution for an onboard analysis tool in deployed systems.

predictive maintenance, anomaly detection, Laserne↗

Chemical signature characterization with hyperspectral imagery: novel deep learning model architectures and physically-motivated data augmentation techniques

The high spectral resolution afforded by Hyperspectral Imaging (HSI) sensors is poised to bring unprecedented advancements to signature characterization applications. Thus far, much of the research in the machine learning field devoted to HSI applications has focused on a few specific tasks like land-use land-cover classification. In land classification tasks, spatial information is very important, and model architectures are often designed to leverage spatial contexts. However, it is unclear how well these spatially-tuned models will translate to tasks where spectral information is critical, like the detection and characterization of chemicals. In this work, we compare spectral models (inputs are 1D spectra) and spatial-spectral models (inputs are 3D cubes) in the context of predicting chemical concentration maps. We find that spatial-spectral models perform the best, though we find a wide range in performance across the different architectures tested. Additionally, we find that model performance is impacted by the availability of training data, particularly in scenarios where the training data doesn't fully capture the true variance of real-world conditions. We find that data augmentation can help mitigate sparse coverage of observed parameter space (e.g., seasonal or geographic variability in ground cover), and present augmentation strategies that are tailored to hyperspectral data.

• Artificial intelligence (AI) / machine learning ↗

Strain shift measured from stress-controlled oscillatory shear: Evidence for a continuous yielding transition and new techniques to determine recovery rheology measures

Understanding the yielding of complex fluids is an important rheological challenge that affects our ability to engineer and process materials for a wide variety of applications. Common theoretical understandings of yield stress fluids follow the Oldroyd–Prager formalism in which the material behavior below the yield stress is treated as solidlike, and above the yield stress as liquidlike, with an instantaneous transition between the two states. This formalism was built on a quasi-static approach to the yield stress, while most applications, ranging from material processing to end user applications, involve a transient approach to yielding over a finite timescale. Using stress-controlled oscillatory shear experiments, we show that yield stress fluids flow below their yield stresses. This is quantified through measuring the strain shift, which is the value about which the strain oscillates during a stress-controlled test and is a function of only the unrecoverable strain. Measurements of the strain shift are, therefore, measurements of flow having taken place. These experimental results are compared to the Herschel–Bulkley form of the Saramito model, which utilizes the Oldroyd–Prager formalism, and the recently published Kamani–Donley–Rogers (KDR) model, in which one constitutive equation represents the entire range of material responses. Scaling relationships are derived, which allow us to show why yield stress fluids will flow across all stresses, above and below their yield stress. Finally, derivations are presented that show strain shift can be used to determine average metrics previously attainable only through recovery rheology, and these are experimentally verified.

Mechanics↗

Study of the p-p-K + and p-p-K - dynamics using the femtoscopy technique

The interactions of kaons (K) and antikaons ($\bar{K}$ ) with few nucleons (N) were studied so far using kaonic atom data and measurements of kaon production and interaction yields in nuclei. Some details of the three-body $\bar{K}$NN and NN dynamics are still not well understood, mainly due to the overlap with multi-nucleon interactions in nuclei. An alternative method to probe the dynamics of three-body systems with kaons is to study the final state interaction within triplet of particles emitted in pp collisions at the Large Hadron Collider, which are free from effects due to the presence of bound nucleons. This Letter reports the first femtoscopic study of p–p–K + and p–p–K - correlations measured in high-multiplicity pp collisions at $\sqrt{s}$ = 13 TeV by the ALICE Collaboration. The analysis shows that the measured p–p–K + and p–p–K - correlation functions can be interpreted in terms of pairwise interactions in the triplets, indicating that the dynamics of such systems is dominated by the two-body interactions without significant contributions from three-body effects or bound states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Shape evolution in even-mass 98-104 Zr isotopes via lifetime measurements using the $\gamma \gamma $-coincidence technique

The Zirconium (Z = 40) isotopic chain has attracted interest for more than four decades. The abrupt lowering of the energy of the first 2 + state and the increase in the transition strength B(E2; 2$^+_1$ →0$^+_1$) going from 98 Zr to 100 Zr has been the first example of “quantum phase transition” in nuclear shapes, which has few equivalents in the nuclear chart. Although a multitude of experiments have been performed to measure nuclear properties related to nuclear shapes and collectivity in the region, none of the measured lifetimes were obtained using the Recoil Distance Doppler Shift method in the γγ-coincidence mode where a gate on the direct feeding transition of the state of interest allows a strict control of systematical errors. Here this work reports the results of lifetime measurements for the first yrast excited states in 98-104 Zr carried out to extract reduced transition probabilities. The new lifetime values in γγ-coincidence and γ-single mode are compared with the results of former experiments. Recent predictions of the Interacting Boson Model with Configuration Mixing, the Symmetry Conserving Configuration Mixing model based on the Hartree–Fock–Bogoliubov approach and the Monte Carlo Shell Model are presented and compared with the experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

A search for new physics in central exclusive production using the missing mass technique with the CMS detector and the CMS-TOTEM precision proton spectrometer

A generic search is presented for the associated production of a Z boson or a photon with an additional unspecified massive particle X, pp → pp + Z/γ + X, in proton-tagged events from proton–proton collisions at $\sqrt{s}$ = 13 TeV, recorded in 2017 with the CMS detector and the CMS-TOTEM precision proton spectrometer. The missing mass spectrum is analysed in the 600–1600 GeV range and a fit is performed to search for possible deviations from the background expectation. No significant excess in data with respect to the background predictions has been observed. Model-independent upper limits on the visible pro duction cross section of pp → pp + Z/γ + X are set

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

A precise measurement of the jet energy scale derived from single-particle measurements and in situ techniques in proton–proton collisions at $\sqrt{s}=$ 13 TeV with the ATLAS detector

The jet energy calibration and its uncertainties are derived from measurements of the calorimeter response to single particles in both data and Monte Carlo simulation using proton–proton collisions at $\sqrt{s} = 13$ TeV collected with the ATLAS detector during Run 2 at the Large Hadron Collider. The jet calibration uncertainty for anti-$k_T$ jets with a jet radius parameter of R$_\textrm{jet} = 0.4$ and in the central jet rapidity region is about 2.5% for transverse momenta ($p_{\text {T}}$) of 20 $\text {GeV}$ , about 0.5% for $p_{\text {T}} = 300$ GeV and 0.7% for $p_{\text {T}} = 4$ TeV . Excellent agreement is found with earlier determinations obtained from -balance based in situ methods ($Z/\gamma$ +jets). The combination of these two independent methods results in the most precise jet energy measurement achieved so far with the ATLAS detector with a relative uncertainty of 0.3% at $p_\textrm{T} = 300$ GeV and 0.6% at 4 TeV. The jet energy calibration is also derived with the single-particle calorimeter response measurements separately for quark- and gluon-induced jets and furthermore for jets with R jet varying from 0.2 to 1.0 retaining the correlations between these measurements. Differences between inclusive jets and jets from boosted top-quark decays, with and without grooming the soft jet constituents, are also studied.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Empirical Study of Focus-Plus-Context and Aggregation Techniques for the Visualization of Streaming Data

Analysis of streaming data often involves both real-time monitoring of incoming data as well as contextual awareness of data history. A focus-plus-context approach can support both goals, with variable levels of visual aggregation making it possible to provide a high level of detail for incoming and recent data while providing contextual information about recent history. Visual aggregation reduces data resolution in order to show the context of data over large periods of time within a limited display space. With a controlled experiment, we evaluated the effectiveness of different types of aggregation for four types of stream-analysis tasks. Overall, the results show that a focus-plus-context design has little negative impact on the ability to successfully monitor and analyze streaming data, making it possible to show longer periods of time than other approaches. However, visual aggregation can be problematic for trend recognition tasks. This research demonstrates how the effectiveness of the visualization depends on the specifics of the analysis task.

Ragan, Eric↗

Determining Proton Transport in Pseudo Catalyst Layers Using Hydrogen Pump DC and AC Techniques

Optimizing electrode morphology with a more uniform ionomer distribution is key to reducing ohmic losses and increasing electrocatalyst utilization in polymer electrolyte fuel cells (PEFCs). Inherent ionomer conductivity, volume fraction and tortuosity determine effective ionic conductivity. We use hydrogen pump (HP) method to measure effective ionic conductivity of a pseudo catalyst layer (PCL) comprised of Vulcan XC-72 carbon black and 3M 825 EW ionomer with ionomer to carbon (I/C) ratios of 0.6, 1 and 1.4 and relative humidity (RH) range of 50 to 120%. These direct current (DC) experiments are then compared with electrochemical impedance spectroscopy (EIS). Both DC and EIS methods show good agreement, indicating that EIS can be used as an alternative to DC method in HP experiment. Ionic conductivity for PCL with I/C of 1 and 1.4 was found to be about one order of magnitude higher than I/C of 0.6 for most of the RH range. At 90% RH tortuosities for I/C = 1 and 1.4 were close to 1, whereas tortuosity for I/C = 0.6 was 3. With decrease in relative humidity tortuosities increased linearly and at 50% relative humidity a PCL with I/C = 0.6 had the highest tortuosity of 6.1.

25 ENERGY STORAGE↗

Understanding the distinct impacts of MCS and non-MCS rainfall on the surface water balance in the central US using a numerical water-tagging technique

Warm-season rainfall associated with mesoscale convective systems (MCSs) in the central US is characterized by higher intensity and nocturnal timing compared to rainfall from non-MCS systems, suggesting their potentially different footprints on the land surface. To differentiate the impacts of MCS and non-MCS rainfall on the surface water balance, a water tracer tool embedded in the Noah land surface model with multi-parameterization options (WT-Noah-MP) is used to numerically “tag” water from MCS and non-MCS rainfall separately during April to August (1997-2018) and track their transit in the terrestrial system. From the water-tagging results, over 50% of warm-season rainfall leaves the surface-subsurface system through evapotranspiration by the end of August, but non-MCS rainfall contributes a larger fraction. However, MCS rainfall plays a more important role in generating surface runoff. These differences are mostly attributed to the rainfall intensity differences. The higher intensity MCS rainfall tends to produce more surface runoff through infiltration excess flow and drives a deeper penetration of the rainwater into the soil. Over 70% of the top 10 percentile runoff is contributed by MCS rainfall, demonstrating its important contribution to local flooding. In contrast, lighter intensity non-MCS rainfall resides mostly in the top layer and contributes more to evapotranspiration through soil evaporation. Diurnal timing of rainfall has negligible effects on the flux partitioning for both MCS and non-MCS rainfall. Differences in soil moisture profiles for MCS and non-MCS rainfall and the resultant evapotranspiration suggest differences in their roles in soil moisture-precipitation feedbacks and ecohydrology.

Hu, Huancui↗