Agnostic Learning for Packing Machine Stoppage Prediction in Smart Factories

The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as ``agnostic''. In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases

Velocity of magnetic holes in the solar wind from Cluster multipoint measurements

We present the first statistical study on the velocity of magnetic holes (MHs) in the solar wind. Magnetic holes are localized depressions of the magnetic field, often divided into two classes: rotational and linear MHs. We have conducted a timing analysis of observations of MHs from the Cluster mission in the first quarter of 2005. In total, 69 events were used; out of these, there were 40 linear and 29 rotational MHs, where the limit of magnetic field rotation was set to 50∘. The resulting median velocity was 7.4 ± 45 and 25 ± 42 km s−1 for linear and rotational MHs, respectively. For both classes, around 70 % of the events had a velocity in the solar wind frame that was lower than the Alfvén velocity. Therefore, we conclude that within the observational uncertainties, both linear and rotational MHs are convected with the solar wind.</p

Ultrasonic wave propagation in multilayered piezoelectric substrates

Due to the increasing demand for higher operating frequency, lower attenuation, and stronger piezoelectricity, use of the layered structure has become necessary. Theoretical studies are carried out for ultrasonic waves propagating in the multilayered piezoelectric substrates. Each layer processes up to as low as monoclinic symmetry with various thickness and orientation. A plane acoustic wave is assumed to be incident, at varied frequency and incidence angle, from a fluid upon a multilayered substrate. Simple analytical expressions for the reflection and transmission coefficients are derived from which all propagation characteristics are identified. Such expressions contain, as a by-product, the secular equation for the propagation of free harmonic waves on the multilayered piezoelectric substrates. Solutions are obtained for the individual layers which relate the field variables at the upper layer surfaces. The response of the total system proceeds by satisfying appropriate interfacial conditions across the layers. Based on the boundary conditions, two cases, {open_quotes}shorted{close_quotes} and {open_quotes}free{close_quotes}, are derived from which a so-called piezoelectric coupling factor is calculated to show the piezoelectric efficiency. Our results are rather general and show that the phase velocity is a function of frequency, layer thickness, and orientation

Ultrasonic methods for measuring liquid viscosity and volume percent of solids

This report describes two ultrasonic techniques under development at Argonne National Laboratory (ANL) in support of the tank-waste transport effort undertaken by the U.S. Department of Energy in treating low-level nuclear waste. The techniques are intended to provide continuous on-line measurements of waste viscosity and volume percent of solids in a waste transport line. The ultrasonic technique being developed for waste-viscosity measurement is based on the patented ANL viscometer. Focus of the viscometer development in this project is on improving measurement accuracy, stability, and range, particularly in the low-viscosity range (<30 cP). A prototype instrument has been designed and tested in the laboratory. Better than 1% accuracy in liquid density measurement can be obtained by using either a polyetherimide or polystyrene wedge. To measure low viscosities, a thin-wedge design has been developed and shows good sensitivity down to 5 cP. The technique for measuring volume percent of solids is based on ultrasonic wave scattering and phase velocity variation. This report covers a survey of multiple scattering theories and other phenomenological approaches. A theoretical model leading to development of an ultrasonic instrument for measuring volume percent of solids is proposed, and preliminary measurement data are presented

An ultrasonic instrument for measuring density and viscosity of tank waste

An estimated 381,000 m{sup 3}/1.1 x 10{sup 9} Ci of radioactive waste are stored in high-level waste tanks at the Hanford Savannah River, Idaho Nuclear Engineering and Environmental Laboratory, and West Valley facilities. This nuclear waste has created one of the most complex waste management and cleanup problems that face the United States. Release of radioactive materials into the environment from underground waste tanks requires immediate cleanup and waste retrieval. Hydraulic mobilization with mixer pumps will be used to retrieve waste slurries and salt cakes from storage tanks. To ensure that transport lines in the hydraulic system will not become plugged, the physical properties of the slurries must be monitored. Characterization of a slurry flow requires reliable measurement of slurry density, mass flow, viscosity, and volume percent of solids. Such measurements are preferably made with on-line nonintrusive sensors that can provide continuous real-time monitoring. With the support of the U.S. Department of Energy (DOE) Office of Environmental Management (EM-50), Argonne National Laboratory (ANL) is developing an ultrasonic instrument for in-line monitoring of physical properties of radioactive tank waste

Exploiting Sparse Representations for Robust Analysis of Noisy Complex Video Scenes

Abstract. Recent works have shown that, even with simple low level visual cues, complex behaviors can be extracted automatically from crowded scenes, e.g. those depicting public spaces recorded from video surveillance cameras. However, low level features as optical flow or fore-ground pixels are inherently noisy. In this paper we propose a novel unsupervised learning approach for the analysis of complex scenes which is specifically tailored to cope directly with features ’ noise and uncer-tainty. We formalize the task of extracting activity patterns as a matrix factorization problem, considering as reconstruction function the robust Earth Mover’s Distance. A constraint of sparsity on the computed basis matrix is imposed, filtering out noise and leading to the identification of the most relevant elementary activities in a typical high level behavior. We further derive an alternate optimization approach to solve the pro-posed problem efficiently and we show that it is reduced to a sequence of linear programs. Finally, we propose to use short trajectory snippets to account for object motion information, in alternative to the noisy optical flow vectors used in previous works. Experimental results demonstrate that our method yields similar or superior performance to state-of-the arts approaches.

Backpropagation training in adaptive quantum networks

We introduce a robust, error-tolerant adaptive training algorithm for generalized learning paradigms in high-dimensional superposed quantum networks, or \emph{adaptive quantum networks}. The formalized procedure applies standard backpropagation training across a coherent ensemble of discrete topological configurations of individual neural networks, each of which is formally merged into appropriate linear superposition within a predefined, decoherence-free subspace. Quantum parallelism facilitates simultaneous training and revision of the system within this coherent state space, resulting in accelerated convergence to a stable network attractor under consequent iteration of the implemented backpropagation algorithm. Parallel evolution of linear superposed networks incorporating backpropagation training provides quantitative, numerical indications for optimization of both single-neuron activation functions and optimal reconfiguration of whole-network quantum structure.Comment: Talk presented at "Quantum Structures - 2008", Gdansk, Polan