An Enhanced Data-Driven Algorithm for Shifting Bottleneck Detection

Bottleneck detection is vital for improving production capacity or reducing production time. Many different methods exist, although only a few of them can detect shifting bottlenecks. The active period method is based on the longest uninterrupted active time of a process, but the analytical algorithm is difficult to program requiring different self-iterating loops. Hence a simpler matrix-based algorithm was developed. This paper presents an improvement over the original algorithm with respect to accuracy

A data-driven approach to diagnosing throughput bottlenecks from a maintenance perspective

Prioritising maintenance activities in throughput bottlenecks increases the throughput from the production system. To facilitate the planning and execution of maintenance activities, throughput bottlenecks in the production system must be identified and diagnosed. Various research efforts have developed data-driven approaches using real-time machine data to identify throughput bottlenecks in the system. However, these efforts have mainly focused on identifying bottlenecks and only offer limited maintenance-related diagnostics for them. Moreover, these research efforts have been proposed from an academic perspective using rigorous scientific methods. A number of challenges must be addressed, if existing data-driven approaches are to be adapted to real-world practice. These include identifying relevant data types, data pre-processing and data modelling. Such challenges can be better addressed by including maintenance-practitioner input when developing data-driven approaches. The aim of this paper is therefore to demonstrate a data-driven approach to diagnosing throughput bottlenecks, using the combined knowledge of the maintenance and data-science domains. Diagnostic insights into throughput bottlenecks are obtained using unsupervised machine-learning techniques. The demonstration uses real-world machine datasets extracted from the production line. The novelty of the research presented in this paper is that it shows how inputs from maintenance practitioners can be used to develop data-driven approaches for diagnosing throughput bottlenecks having more practical relevance. By gaining these diagnostic insights, maintenance practitioners can better understand shop-floor throughput bottleneck behaviours from a maintenance perspective and thus prioritise various maintenance actions

A generic hierarchical clustering approach for detecting bottlenecks in manufacturing

The advancements in machine learning (ML) techniques open new opportunities for analysing production system dynamics and augmenting the domain expert\u27s decision-making. A common problem for domain experts on the shop floor is detecting throughput bottlenecks, as they constrain the system throughput. Detecting throughput bottlenecks is necessary to prioritise maintenance and improvement actions and obtain greater system throughput. The existing literature provides many ways to detect bottlenecks from machine data, using statistical-based approaches. These statistical-based approaches can be best applied in environments where the statistical descriptors of machine data (such as distribution of machine data, correlations and stationarity) are known beforehand. Computing statistical descriptors involves statistical assumptions. When the machine data doesn\u27t comply with these assumptions, there is a risk of the results being disconnected from actual production system dynamics. An alternative approach to detecting throughput bottlenecks is to use ML- based techniques. These techniques, particularly unsupervised ML techniques, require no prior statistical information on machine data. This paper proposes a generic, unsupervised ML-based hierarchical clustering approach to detect throughput bottlenecks. The proposed approach is the outcome of systematic and careful selection of ML techniques. It begins by generating a time series of the chosen bottleneck detection metric and then clustering the time series using a dynamic time-wrapping measure and a complete-linkage agglomerative hierarchical clustering technique. The results are clusters of machines with similar production dynamic profiles, revealed from the historical data and enabling the detection of bottlenecks. The proposed approach is demonstrated in two real-world production systems. The approach integrates the concept of humans in-loop by using the domain expert\u27s knowledge

Search for a Technicolor omega_T Particle in Events with a Photon and a b-quark Jet at CDF

If the Technicolor omega_T particle exists, a likely decay mode is omega_T -> gamma pi_T, followed by pi_T -> bb-bar, yielding the signature gamma bb-bar. We have searched 85 pb^-1 of data collected by the CDF experiment at the Fermilab Tevatron for events with a photon and two jets, where one of the jets must contain a secondary vertex implying the presence of a b quark. We find no excess of events above standard model expectations. We express the result of an exclusion region in the M_omega_T - M_pi_T mass plane.Comment: 14 pages, 2 figures. Available from the CDF server (PS with figs): http://www-cdf.fnal.gov/physics/pub98/cdf4674_omega_t_prl_4.ps FERMILAB-PUB-98/321-

Measurement of the B0 anti-B0 oscillation frequency using l- D*+ pairs and lepton flavor tags

The oscillation frequency Delta-md of B0 anti-B0 mixing is measured using the partially reconstructed semileptonic decay anti-B0 -> l- nubar D*+ X. The data sample was collected with the CDF detector at the Fermilab Tevatron collider during 1992 - 1995 by triggering on the existence of two lepton candidates in an event, and corresponds to about 110 pb-1 of pbar p collisions at sqrt(s) = 1.8 TeV. We estimate the proper decay time of the anti-B0 meson from the measured decay length and reconstructed momentum of the l- D*+ system. The charge of the lepton in the final state identifies the flavor of the anti-B0 meson at its decay. The second lepton in the event is used to infer the flavor of the anti-B0 meson at production. We measure the oscillation frequency to be Delta-md = 0.516 +/- 0.099 +0.029 -0.035 ps-1, where the first uncertainty is statistical and the second is systematic.Comment: 30 pages, 7 figures. Submitted to Physical Review

Search for New Particles Decaying to top-antitop in proton-antiproton collisions at squareroot(s)=1.8 TeV

We use 106 \ipb of data collected with the Collider Detector at Fermilab to search for narrow-width, vector particles decaying to a top and an anti-top quark. Model independent upper limits on the cross section for narrow, vector resonances decaying to \ttbar are presented. At the 95% confidence level, we exclude the existence of a leptophobic \zpr boson in a model of topcolor-assisted technicolor with mass M_{\zpr} $<$ 480 \gev for natural width $\Gamma$ = 0.012 M_{\zpr}, and M_{\zpr} $<$ 780 \gev for $\Gamma$ = 0.04 M_{\zpr}.Comment: The CDF Collaboration, submitted to PRL 25-Feb-200

Double Diffraction Dissociation at the Fermilab Tevatron Collider

We present results from a measurement of double diffraction dissociation in $\bar pp$ collisions at the Fermilab Tevatron collider. The production cross section for events with a central pseudorapidity gap of width $\Delta\eta^0>3$ (overlapping $\eta=0$) is found to be $4.43\pm 0.02{(stat)}{\pm 1.18}{(syst) mb}$ [$3.42\pm 0.01{(stat)}{\pm 1.09}{(syst) mb}$] at $\sqrt{s}=1800$ [630] GeV. Our results are compared with previous measurements and with predictions based on Regge theory and factorization.Comment: 10 pages, 4 figures, using RevTeX. Submitted to Physical Review Letter

A Measurement of the Differential Dijet Mass Cross Section in p-pbar Collisions at sqrt{s}=1.8 TeV

We present a measurement of the cross section for production of two or more jets as a function of dijet mass, based on an integrated luminosity of 86 pb^-1 collected with the Collider Detector at Fermilab. Our dijet mass spectrum is described within errors by next-to-leading order QCD predictions using CTEQ4HJ parton distributions, and is in good agreement with a similar measurement from the D0 experiment.Comment: 18 pages including 2 figures and 3 tables. Submitted to Phys. Rev. D Rapid Communication

Search for Gluinos and Scalar Quarks in ppˉp\bar{p} Collisions at s=1.8\sqrt{s}=1.8 TeV using the Missing Energy plus Multijets Signature

We have performed a search for gluinos (\gls) and squarks (\sq) in a data sample of 84 pb$^{-1}$ of \ppb collisions at $\sqrt{s}$ = 1.8 TeV, recorded by the Collider Detector at Fermilab, by investigating the final state of large missing transverse energy and 3 or more jets, a characteristic signature in R-parity-conserving supersymmetric models. The analysis has been performed `blind', in that the inspection of the signal region is made only after the predictions from Standard Model backgrounds have been calculated. Comparing the data with predictions of constrained supersymmetric models, we exclude gluino masses below 195 \gev (95% C.L.), independent of the squark mass. For the case \msq \approx \mgls, gluino masses below 300 \gev are excluded.Comment: 7 pages, 3 figure

Diffractive Dijet Production at sqrt(s)=630 and 1800 GeV at the Fermilab Tevatron

We report a measurement of the diffractive structure function $F_{jj}^D$ of the antiproton obtained from a study of dijet events produced in association with a leading antiproton in $\bar pp$ collisions at $\sqrt s=630$ GeV at the Fermilab Tevatron. The ratio of $F_{jj}^D$ at $\sqrt s=630$ GeV to $F_{jj}^D$ obtained from a similar measurement at $\sqrt s=1800$ GeV is compared with expectations from QCD factorization and with theoretical predictions. We also report a measurement of the $\xi$ ($x$-Pomeron) and $\beta$ ($x$ of parton in Pomeron) dependence of $F_{jj}^D$ at $\sqrt s=1800$ GeV. In the region $0.035<\xi<0.095$, $|t|<1$ GeV$^2$ and $\beta<0.5$, $F_{jj}^D(\beta,\xi)$ is found to be of the form $\beta^{-1.0\pm 0.1} \xi^{-0.9\pm 0.1}$, which obeys $\beta$-$\xi$ factorization.Comment: LaTeX, 9 pages, Submitted to Phys. Rev. Letter