2,836 research outputs found
Nonlinear dynamic process monitoring using kernel methods
The application of kernel methods in process monitoring is well established. How-
ever, there is need to extend existing techniques using novel implementation strate-
gies in order to improve process monitoring performance. For example, process
monitoring using kernel principal component analysis (KPCA) have been reported.
Nevertheless, the e ect of combining kernel density estimation (KDE)-based control
limits with KPCA for nonlinear process monitoring has not been adequately investi-
gated and documented. Therefore, process monitoring using KPCA and KDE-based
control limits is carried out in this work. A new KPCA-KDE fault identi cation
technique is also proposed.
Furthermore, most process systems are complex and data collected from them have
more than one characteristic. Therefore, three techniques are developed in this
work to capture more than one process behaviour. These include the linear latent
variable-CVA (LLV-CVA), kernel CVA using QR decomposition (KCVA-QRD) and
kernel latent variable-CVA (KLV-CVA).
LLV-CVA captures both linear and dynamic relations in the process variables. On
the other hand, KCVA-QRD and KLV-CVA account for both nonlinearity and pro-
cess dynamics. The CVA with kernel density estimation (CVA-KDE) technique
reported does not address the nonlinear problem directly while the regular kernel
CVA approach require regularisation of the constructed kernel data to avoid com-
putational instability. However, this compromises process monitoring performance.
The results of the work showed that KPCA-KDE is more robust and detected faults
higher and earlier than the KPCA technique based on Gaussian assumption of pro-
cess data. The nonlinear dynamic methods proposed also performed better than
the afore-mentioned existing techniques without employing the ridge-type regulari-
sation
Assesment of biomass and carbon dynamics in pine forests of the Spanish central range: A remote sensing approach
Forests play a dynamic role in the terrestrial carbon (C) budget, by means of the biomass stock and C fluxes involved in photosynthesis and respiration. Remote sensing in combination with data analysis constitute a practical means for evaluation of forest implications in the carbon cycle, providing spatially explicit estimations of the amount, quality, and spatio-temporal dynamics of biomass and C stocks. Medium and high spatial resolution optical data from satellite-borne sensors were employed, supported by field measures, to investigate the carbon role of Mediterranean pines in the Central Range of Spain during a 25 year period (1984-2009). The location, extent, and distribution of pine forests were characterized, and spatial changes occurred in three sub-periods were evaluated. Capitalizing on temporal series of spectral data from Landsat sensors, novel techniques for processing and data analysis were developed to identify successional processes at the landscape level, and to characterize carbon stocking condition locally, enabling simultaneous characterization of trends and patterns of change. High spatial resolution data captured by the commercial satellite QuickBird-2 were employed to model structural attributes at the stand level, and to explore forest structural diversity
Predictive condition monitoring of industrial systems for improved maintenance and operation
Maintenance strategies based on condition monitoring of the different machines
and devices in an industrial process can minimize downtime, increase the
safety of plant operations and help in the process of decision-taking for control
and maintenance actions in order to reduce maintenance and operating costs.
Multivariate statistical methods are widely used for process condition monitoring
in modern industrial sites due to the quantity of data available and the difficulties
of building analytical models in complex facilities.
Nevertheless, the performance of these methodologies is still far away from
being ideal, due to different issues such as process nonlinearities or varying
operational conditions. In addition application of the latest approaches
developed for process monitoring is not widely extended in real industry.
The aim of this investigation is to develop new and improve existing
methodologies for predictive condition monitoring through the use of
multivariate statistical methods. The research focuses on demonstrating the
applicability of multivariate algorithms in real complex cases, the improvement
of these methods in terms of fault detection and diagnosis by means of data
fusion and the estimation of process performance degradation caused by faults.Marie Curi
Seabed biodiversity on the continental shelf of the Great Barrier Reef World Heritage Area
Final report to the Cooperative Research Centre for the Great Barrier Reef World Heritage Are
Combining Canonical Variate Analysis, Probability Approach and Support Vector Regression for Failure Time Prediction
Reciprocating compressors are widely used in oil and gas industry for as transport, lift and injection. Critical reciprocating compressors that operate under high-speed conditions and compress hazardous gases are target equipment on maintenance improvement lists due to downtime risks and safety hazards. Estimating performance deterioration and failure time for reciprocating compressors could potentially reduce downtime and maintenance costs, and improve safety and availability. This study presents an application of Canonical Variate Analysis (CVA), Cox Proportional Hazard (CPHM) and Support Vector Regression (SVR) models to estimate failure degradation and remaining useful life based on sensory data acquired from an operational industrial reciprocating compressor. CVA was used to extract a one-dimensional health indicator from the multivariate data sets, thereby reducing the dimensionality of the original data matrix. The failure rate was obtained by using the CPHM based on historical failure times. Furthermore, a SVR model was used as a prognostic tool following training with failure rate vectors obtained from the CPHM and the one-dimensional performance measures obtained from the CVA model. The trained SVR model was then utilized to estimate the failure degradation rate and remaining useful life. The results indicate that the proposed method can be effectively used in real industrial processes to predict performance degradation and failure time
Continental-scale variation in otolith geochemistry of juvenile American shad (Alosa sapidissima)
Author Posting. © NRC Research Press, 2008. This article is posted here by permission of NRC Research Press for personal use, not for redistribution. The definitive version was published in Canadian Journal of Fisheries and Aquatic Sciences 65 (2008): 2623-2635, doi:10.1139/F08-164.We assembled a comprehensive atlas of geochemical signatures in juvenile American shad (Alosa sapidissima) to discriminate natal river origins on a large spatial scale and at a high spatial resolution. Otoliths and (or) water samples were collected from 20 major spawning rivers from Florida to Quebec and were analyzed for elemental (Mg:Ca, Mn:Ca, Sr:Ca, and Ba:Ca) and isotope (87Sr:86Sr and δ18O) ratios. We examined correlations between water chemistry and otolith composition for five rivers where both were sampled. While Sr:Ca, Ba:Ca, 87Sr:86Sr, and δ18O values in otoliths reflected those ratios in ambient waters, Mg:Ca and Mn:Ca ratios in otoliths varied independently of water chemistry. Geochemical signatures were highly distinct among rivers, with an average classification accuracy of 93% using only those variables where otolith values were accurately predicted from water chemistry data. The study represents the largest assembled database of otolith signatures from the entire native range of a species, encompassing approximately 2700 km of coastline and 19 degrees of latitude and including all major extant spawning populations. This database will allow reliable estimates of natal origins of migrating ocean-phase American shad from the 2004 annual cohort in the future.This work was funded by National Science
Foundation (NSF) grants OCE-0215905 and OCE-0134998
to SRT and by an American Museum of Natural History
Lerner–Gray Grant for Marine Research and a scholarship
from SEASPACE, Inc., to BDW
Statistics local fisher discriminant analysis for industrial process fault classification
In order to effectively identify industrial process faults, an improved Fisher discriminant analysis (FDA) method, referred to as the statistics local Fisher discriminant analysis (SLFDA), is proposed for fault classification. For mining statistics information hidden in process data, statistics pattern analysis is firstly applied to transform the original measured variables into the corresponding statistics, including second-order and higher-order ones. Furthermore, considering the local structure characteristics of fault data, local FDA (LFDA) is performed which computes the discriminant vectors by modifying the optimization objective with local weighting factor. Simulation results on the benchmark Tennessee Eastman process show that the proposed SLFDA has a better fault classification performance than the FDA and LFDA methods
Deep Learning-Based Machinery Fault Diagnostics
This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis
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