Detecting Asset Cascading Failures Using Complex Network Analysis

Experienced process plant personnel observe that corrective maintenance work on one asset may often be followed by corrective work on the same asset or connected assets within a short amount of time. This problem is referred to as a cascading failure. Confirming if these events are chronic is difficult given the number of assets and the volume of maintenance and operation data. If cascading events can be identified, preventative measures can be implemented to prevent those cascades, eliminating unnecessary corrective work. This project uses complex network analysis to identify cascading events and where co-occurrence of work is most frequent, in a process plant. Data is drawn from over 50,000 work orders for 5,655 pumps in a mining company over a five-year period. A complex network is produced by connecting assets based on the frequency of co-occurrence of work. Beside the advantages of the visualisation of complex networks, the method produces quantified measures, normalised degree, eigenvector centrality and betweenness centrality, which are used to identify assets with significant impact on other assets. Affected pumps are apparent as communities in the network. This analysis identifies pumps that are 'super-spreaders': pumps who experience corrective maintenance events which lead to corrective maintenance events on other pumps. The model can be tuned to different time windows, for example events within one or seven days. From these insights, changes can be made to operational, maintenance and recording practices to prevent re-occurrence. Of particular note in this data was the occurrence of self-loops in certain pumps and the prevalence of hidden failures in standby pumps

Optimal Shadowing Filter for a Positioning and Tracking Methodology with Limited Information

Positioning and tracking a moving target from limited positional information is a frequently-encountered problem. For given noisy observations of the target’s position, one wants to estimate the true trajectory and reconstruct the full phase space including velocity and acceleration. The shadowing filter offers a robust methodology to achieve such an estimation and reconstruction. Here, we highlight and validate important merits of this methodology for real-life applications. In particular, we explore the filter’s performance when dealing with correlated or uncorrelated noise, irregular sampling in time and how it can be optimised even when the true dynamics of the system are not known

Objective Domain Boundaries Detection in New Caledonian Nickel Laterite from Spectra Using Quadrant Scan

Modelling of 3D domain boundaries using information from drill holes is a standard procedure in mineral exploration and mining. Manual logging of drill holes can be difficult to exploit as the results may not be comparable between holes due to the subjective nature of geological logging. Exploration and mining companies commonly collect geochemical or mineralogical data from diamond drill core or drill chips; however, manual interpretation of multivariate data can be slow and challenging; therefore, automation of any of the steps in the interpretation process would be valuable. Hyperspectral analysis of drill chips provides a relatively inexpensive method of collecting very detailed information rapidly and consistently. However, the challenge of such data is the high dimensionality of the data’s variables in comparison to the number of samples. Hyperspectral data is usually processed to produce mineral abundances generally involving a range of assumptions. This paper presents the results of testing a new fast and objective methodology to identify the lithological boundaries from high dimensional hyperspectral data. This method applies a quadrant scan analysis to recurrence plots. The results, applied to nickel laterite deposits from New Caledonia, demonstrate that this method can identify transitions in the downhole data. These are interpreted as reflecting mineralogical changes that can be used as an aid in geological logging to improve boundary detection

Objective domain boundaries detection in new caledonian nickel laterite from spectra using quadrant scan

Modelling of 3D domain boundaries using information from drill holes is a standard pro-cedure in mineral exploration and mining. Manual logging of drill holes can be difficult to exploit as the results may not be comparable between holes due to the subjective nature of geological log-ging. Exploration and mining companies commonly collect geochemical or mineralogical data from diamond drill core or drill chips; however, manual interpretation of multivariate data can be slow and challenging; therefore, automation of any of the steps in the interpretation process would be valuable. Hyperspectral analysis of drill chips provides a relatively inexpensive method of collect-ing very detailed information rapidly and consistently. However, the challenge of such data is the high dimensionality of the data’s variables in comparison to the number of samples. Hyperspectral data is usually processed to produce mineral abundances generally involving a range of assump-tions. This paper presents the results of testing a new fast and objective methodology to identify the lithological boundaries from high dimensional hyperspectral data. This method applies a quadrant scan analysis to recurrence plots. The results, applied to nickel laterite deposits from New Caledo-nia, demonstrate that this method can identify transitions in the downhole data. These are inter-preted as reflecting mineralogical changes that can be used as an aid in geological logging to im-prove boundary detection

Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types

Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Visualisation and statistical modelling techniques for the management of inventory stock levels

This paper describes the investigations conducted in a Mathematics-in-Industry Study Group project from the Australian meeting at Queensland University of Technology in 2015. This concerned the management of stock levels of raw materials used to construct aortic stents. The approaches used included network visualisation, classification and regression trees, and time series modelling. This work will be of general interest to those who are managing stock levels in a highly volatile context. The methods applied show that there is potential value in taking a statistical approach to understand and make decisions within such volatility. The work provides a basis for developing more advanced statistical approaches for specific inventory problems. References Breiman, L. (1996) Bagging Predictors. Machine Learning, 24, 2, 123–140. doi:10.1023/A:1018054314350 Breiman, L. (2001) Statistical Modeling: The Two Cultures Statistical Science, 16, 3, 199–231. https://projecteuclid.org/download/pdf_1/euclid.ss/1009213726 Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J. (1984) Classification and Regression Trees Wadsworth, Belmont, Ca. ISBN-13: 978-0412048418 ISBN-10: 0412048418 Csardi, G., Nepusz, T. (2006) The igraph software package for complex network research. InterJournal: Complex Systems, 1695, 5, 1–9. http://www.interjournal.org/manuscript_abstract.php?361100992 Fruchterman, T. M. J., Reingold, E. M. (1991) Graph drawing by force-directed placement. Software: Practice and Experience, 21, 11, 1129–1164. doi:10.1002/spe.4380211102 Hastie, T., Tibshirani, R., Friedman, J. H. (2009) The elements of statistical learning : Data mining, inference, and prediction, 2nd edition New York: Springer Verlag. ISBN 978-0-387-84858-7 (eBook), ISBN 978-0-387-84857-0 (Hardcover) doi:10.1007/978-0-387-84858-7 Kim, J. H., Wong, K., Athanasopoulos, G., Liu, S. (2011) Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals. International Journal of Forecasting, 27, 887–901. doi:10.1016/j.ijforecast.2010.02.014 Liu, S., Maharaj, E. A. (2013). A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples. Computational Statistics and Data Analysis, 60, 32–49. doi:10.1016/j.csda.2012.11.014 Liu, S., Maharaj, E. A., Inder, B. (2014) Polarization of forecast densities: A new approach to time series classification. Computational Statistics and Data Analysis, 70, 345–361. doi:10.1016/j.csda.2013.10.008 Liu, S., McGree, J., Ge, Z., Xie, Y. (2015) Computational and Statistical Methods for Analysing Big Data with Applications. Elsevier, London. ISBN: 978-0-12-803732-4 Reingold, E. M., Tilford, J. S. (1981) Tidier drawings of trees. IEEE Transactions on Software Engineering, 7, 2, 223–228. doi:10.1109/TSE.1981.23451

Optimisation of the thermal and structural performance of an integrated patio door

Frames for glass doors and windows need to be designed to be both thermally insulating and structurally strong, and there are computer programs which accurately determine the required properties. However, these programs are complex and expensive to use and are not useful for initial design work. By identifying the structural and insulating roles of the various layers within the frames we obtain simple results for the thermal transmittance and structural properties of frames. These results can be used for design guidance. More accurate (but still simple) results were obtained and coded in Excel for easy processing. References R. J. Roark ``Formulas for stress and strain'' McGraw-Hill (1965), 4th ed. ``Thermal performance of windows, doors and sharing devices–-Detailed Calculations''. iso 15099:2003(E) ``Thermal performance of windows, doors and shutters–-Calculation of thermal transmittance–-Part 2 Numerical Method for Frames''. bs eniso 10077-2-2003 ``Thermal performance of windows, doors and sharing devices–-Part 1 General''. bs en 10077-1:200