A convolutional neural network aided physical model improvement for AC solenoid valves diagnosis

This paper focuses on the development of a physics-based diagnostic tool for alternating current (AC) solenoid valves which are categorized as critical components of many machines used in the process industry. Signal processing and machine learning based approaches have been proposed in the literature to diagnose the health state of solenoid valves. However, the approaches do not give a physical explanation of the failure modes. In this work, being capable of diagnosing failure modes while using a physically interpretable model is proposed. Feature attribution methods are applied to CNN on a large data set of the current signals acquired from accelerated life tests of several AC solenoid valves. The results reveal important regions of interest on current signals that guide the modeling of the main missing component of an existing physical model. Two model parameters, which are the shading ring and kinetic coulomb forces, are then identified using current measurements along the lifetime of valves. Consistent trends are found for both parameters allowing to diagnose the failure modes of the solenoid valves. Future work will consist of not only diagnosing the failure modes, but also of predicting the remaining useful life

Statistical Physics of Rupture in Heterogeneous Media

The damage and fracture of materials are technologically of enormous interest due to their economic and human cost. They cover a wide range of phenomena like e.g. cracking of glass, aging of concrete, the failure of fiber networks in the formation of paper and the breaking of a metal bar subject to an external load. Failure of composite systems is of utmost importance in naval, aeronautics and space industry. By the term composite, we refer to materials with heterogeneous microscopic structures and also to assemblages of macroscopic elements forming a super-structure. Chemical and nuclear plants suffer from cracking due to corrosion either of chemical or radioactive origin, aided by thermal and/or mechanical stress. Despite the large amount of experimental data and the considerable effort that has been undertaken by material scientists, many questions about fracture have not been answered yet. There is no comprehensive understanding of rupture phenomena but only a partial classification in restricted and relatively simple situations. This lack of fundamental understanding is indeed reflected in the absence of reliable prediction methods for rupture, based on a suitable monitoring of the stressed system. Not only is there a lack of non-empirical understanding of the reliability of a system, but also the empirical laws themselves have often limited value. The difficulties stem from the complex interplay between heterogeneities and modes of damage and the possible existence of a hierarchy of characteristic scales (static and dynamic). The paper presents a review of recent efforts from the statistical physics community to address these points.Comment: Enlarged review and updated references, 21 pages with 2 figure

Application of tire dynamics to aircraft landing gear design analysis

The tire plays a key part in many analyses used for design of aircraft landing gear. Examples include structural design of wheels, landing gear shimmy, brake whirl, chatter and squeal, complex combination of chatter and shimmy on main landing gear (MLG) systems, anti-skid performance, gear walk, and rough terrain loads and performance. Tire parameters needed in the various analyses are discussed. Two tire models are discussed for shimmy analysis, the modified Moreland approach and the von Schlippe-Dietrich approach. It is shown that the Moreland model can be derived from the Von Schlippe-Dietrich model by certain approximations. The remaining analysis areas are discussed in general terms and the tire parameters needed for each are identified. Accurate tire data allows more accurate design analysis and the correct prediction of dynamic performance of aircraft landing gear

An analysis of fast photochemistry over high northern latitudes during spring and summer using in-situ observations from ARCTAS and TOPSE

Observations of chemical constituents and meteorological quantities obtained during the two Arctic phases of the airborne campaign ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) are analyzed using an observationally constrained steady state box model. Measurements of OH and HO2 from the Penn State ATHOS instrument are compared to model predictions. Forty percent of OH measurements below 2 km are at the limit of detection during the spring phase (ARCTAS-A). While the median observed-to-calculated ratio is near one, both the scatter of observations and the model uncertainty for OH are at the magnitude of ambient values. During the summer phase (ARCTAS-B), model predictions of OH are biased low relative to observations and demonstrate a high sensitivity to the level of uncertainty in NO observations. Predictions of HO2 using observed CH2O and H2O2 as model constraints are up to a factor of two larger than observed. A temperature-dependent terminal loss rate of HO2 to aerosol recently proposed in the literature is shown to be insufficient to reconcile these differences. A comparison of ARCTAS-A to the high latitude springtime portion of the 2000 TOPSE campaign (Tropospheric Ozone Production about the Spring Equinox) shows similar meteorological and chemical environments with the exception of peroxides; observations of H2O2 during ARCTAS-A were 2.5 to 3 times larger than those during TOPSE. The cause of this difference in peroxides remains unresolved and has important implications for the Arctic HOx budget. Unconstrained model predictions for both phases indicate photochemistry alone is unable to simultaneously sustain observed levels of CH2O and H2O2; however when the model is constrained with observed CH2O, H2O2 predictions from a range of rainout parameterizations bracket its observations. A mechanism suitable to explain observed concentrations of CH2O is uncertain. Free tropospheric observations of acetaldehyde (CH3CHO) are 2–3 times larger than its predictions, though constraint of the model to those observations is sufficient to account for less than half of the deficit in predicted CH2O. The box model calculates gross O3 formation during spring to maximize from 1–4 km at 0.8 ppbv d−1, in agreement with estimates from TOPSE, and a gross production of 2–4 ppbv d−1 in the boundary layer and upper troposphere during summer. Use of the lower observed levels of HO2 in place of model predictions decreases the gross production by 25–50%. Net O3 production is near zero throughout the ARCTAS-A troposphere, and is 1–2 ppbv in the boundary layer and upper altitudes during ARCTAS-B

Methods of Technical Prognostics Applicable to Embedded Systems

Hlavní cílem dizertace je poskytnutí uceleného pohledu na problematiku technické prognostiky, která nachází uplatnění v tzv. prediktivní údržbě založené na trvalém monitorování zařízení a odhadu úrovně degradace systému či jeho zbývající životnosti a to zejména v oblasti komplexních zařízení a strojů. V současnosti je technická diagnostika poměrně dobře zmapovaná a reálně nasazená na rozdíl od technické prognostiky, která je stále rozvíjejícím se oborem, který ovšem postrádá větší množství reálných aplikaci a navíc ne všechny metody jsou dostatečně přesné a aplikovatelné pro embedded systémy. Dizertační práce přináší přehled základních metod použitelných pro účely predikce zbývající užitné životnosti, jsou zde popsány metriky pomocí, kterých je možné jednotlivé přístupy porovnávat ať už z pohledu přesnosti, ale také i z pohledu výpočetní náročnosti. Jedno z dizertačních jader tvoří doporučení a postup pro výběr vhodné prognostické metody s ohledem na prognostická kritéria. Dalším dizertačním jádrem je představení tzv. částicového filtrovaní (particle filtering) vhodné pro model-based prognostiku s ověřením jejich implementace a porovnáním. Hlavní dizertační jádro reprezentuje případovou studii pro velmi aktuální téma prognostiky Li-Ion baterii s ohledem na trvalé monitorování. Případová studie demonstruje proces prognostiky založené na modelu a srovnává možné přístupy jednak pro odhad doby před vybitím baterie, ale také sleduje možné vlivy na degradaci baterie. Součástí práce je základní ověření modelu Li-Ion baterie a návrh prognostického procesu.The main aim of the thesis is to provide a comprehensive overview of technical prognosis, which is applied in the condition based maintenance, based on continuous device monitoring and remaining useful life estimation, especially in the field of complex equipment and machinery. Nowadays technical prognosis is still evolving discipline with limited number of real applications and is not so well developed as technical diagnostics, which is fairly well mapped and deployed in real systems. Thesis provides an overview of basic methods applicable for prediction of remaining useful life, metrics, which can help to compare the different approaches both in terms of accuracy and in terms of computational/deployment cost. One of the research cores consists of recommendations and guide for selecting the appropriate forecasting method with regard to the prognostic criteria. Second thesis research core provides description and applicability of particle filtering framework suitable for model-based forecasting. Verification of their implementation and comparison is provided. The main research topic of the thesis provides a case study for a very actual Li-Ion battery health monitoring and prognostics with respect to continuous monitoring. The case study demonstrates the prognostic process based on the model and compares the possible approaches for estimating both the runtime and capacity fade. Proposed methodology is verified on real measured data.

PICES Press, Vol. 20, No. 2, Summer 2012

•The 2012 Inter-sessional Science Board Meeting: A Note from Science Board Chairman (pp. 1-4) ◾PICES Interns (p. 4) ◾2012 Inter-sessional Workshop on a Roadmap for FUTURE (pp. 5-8) ◾Second Symposium on “Effects of Climate Change on the World’s Oceans” (pp. 9-13) ◾2012 Yeosu Workshop on “Framework for Ocean Observing” (pp. 14-15) ◾2012 Yeosu Workshop on “Climate Change Projections” (pp. 16-17) ◾2012 Yeosu Workshop on “Coastal Blue Carbon” (pp. 18-20) ◾Polar Comparisons: Summary of 2012 Yeosu Workshop (pp. 21-23) ◾2012 Yeosu Workshop on “Climate Change and Range Shifts in the Oceans" (pp. 24-27) ◾2012 Yeosu Workshop on “Beyond Dispersion” (pp. 28-30) ◾2012 Yeosu Workshop on “Public Perception of Climate Change” (pp. 31, 50) ◾PICES Working Group 20: Accomplishments and Legacy (pp. 32-33) ◾The State of the Western North Pacific in the Second Half of 2011 (pp. 34-35) ◾Another Cold Winter in the Gulf of Alaska (pp. 36-37) ◾The Bering Sea: Current Status and Recent Events (pp. 38-40) ◾PICES/ICES 2012 Conference for Early Career Marine Scientists (pp. 41-43) ◾Completion of the PICES Seafood Safety Project – Indonesia (pp. 44-46) ◾Oceanography Improves Salmon Forecasts (p. 47) ◾2012 GEOHAB Open Science Meeting (p. 48-50) ◾Shin-ichi Ito awarded 2011 Uda Prize (p. 50

Identification of Evolving Rule-based Models.

An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy System