ORCID Profile
0000-0002-7336-3932
Current Organisation
University of Western Australia
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Civil Geotechnical Engineering | Maritime Engineering | Ocean Engineering | Stochastic Analysis and Modelling | Simulation and Modelling | Engineering Practice | Infrastructure Engineering and Asset Management | Interdisciplinary Engineering | Physical Oceanography
ANZSRC Socio-Economic Objective (SEO)
Productivity (excl. Public Sector) | Oil and Gas Exploration | Oil and Gas Extraction | Industrial Energy Conservation and Efficiency | Application Tools and System Utilities | Mining Machinery and Equipment | Expanding Knowledge in Engineering |
Related Links
Publications
The role of organizational factors in achieving reliability in the design and manufacture of subsea equipment
Publisher: Wiley
Date: 12-12-2011
DOI: 10.1002/HFM.20290
Rethinking maintenance terminology for an industry 4.0 future
Publisher: PHM Society
Date: 24-03-2021
DOI: 10.36001/IJPHM.2021.V12I1.2932
Abstract: Sensors and mathematical models have been used since the 1990’s to assess the health of systems and diagnose anomalous behavior. The advent of the Internet of Things (IoT) increases the range of assets on which data can be collected cost effectively. Cloud-computing and the wider availability of data and models are democratizing the implementation of prognostic health (PHM) technologies. Together, these advancements and other Industry 4.0 developments are creating a paradigm shift in how maintenance work is planned and executed. In this new future, maintenance will be initiated once a potential failure has been detected (using PHM) and thus completed before a functional failure has occurred. Thus corrective work is required since corrective work is defined as “work done to restore the function of an asset after failure or when failure is imminent.” Many metrics for measuring the effectiveness of maintenance work management are grounded in a negative perspective of corrective work and do not clearly capture work arising from condition monitoring and predictive modeling investments. In this paper, we use case studies to demonstrate the need to rethink maintenance terminology. The outcomes of this work include 1) definitions to be used for consistent evaluation of work management performance in an Industry 4.0 future and 2) recommendations to improve detection of work related to PHM activities.
Cleaning historical maintenance work order data for reliability analysis
Publisher: Emerald
Date: 09-05-2016
DOI: 10.1108/JQME-04-2015-0013
Abstract: – The purpose of this paper is to identify quality issues with using historical work order (WO) data from computerised maintenance management systems for reliability analysis and develop an efficient and transparent process to correct these data quality issues to ensure data is fit for purpose in a timely manner. – This paper develops a rule-based approach to data cleansing and demonstrates the process on data for heavy mobile equipment from a number of organisations. – Although historical WO records frequently contain missing or incorrect functional location, failure mode, maintenance action and WO status fields the authors demonstrate it is possible to make these records fit for purpose by using data in the freeform text fields an understanding of the maintenance tactics and practices at the operation and knowledge of where the asset is in its life cycle. The authors demonstrate that it is possible to have a repeatable and transparent process to deal with the data cleaning activities. – How engineers deal with raw maintenance data and the decisions they make in order to produce a data set for reliability analysis is seldom discussed in detail. Assumptions and actions are often left undocumented. This paper describes typical data cleaning decisions we all have to make as a routine part of the analysis and presents a process to support the data cleaning decisions in a repeatable and transparent fashion.
An ontology for reasoning over engineering textual data stored in FMEA spreadsheet tables
Publisher: Elsevier BV
Date: 10-2021
Goal hierarchy: Improving asset data quality by improving motivation
Publisher: Elsevier BV
Date: 11-2011
Integrated Model for Optimizing Strategic Overhaul Planning of Distributed Pump Stations
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2011
Requirements for Adaptive User Interfaces for Industrial Maintenance Procedures
Publisher: ACM
Date: 02-12-2019
Lithium and Tamoxifen Modulate Behavior and Protein Kinase C Activity in the Animal Model of Mania Induced by Ouabain
Publisher: Oxford University Press (OUP)
Date: 07-08-2017
DOI: 10.1093/IJNP/PYX049
Rail Wagon Bearings Health Management based on Imperfect Acoustic Information
Publisher: Springer London
Date: 31-07-2014
A vibration cavitation sensitivity parameter based on spectral and statistical methods
Publisher: Elsevier BV
Date: 2015
A Framework to Assess Data Quality for Reliability Variables
Publisher: Springer London
Date: 2008
Framework for Validation of Permanently Installed MEMS-Based Acquisition Devices Using Soft Sensor Models
Publisher: MDPI AG
Date: 28-07-2020
Abstract: Asset integrity and predictive maintenance models require field data for an accurate assessment of an asset’s condition. Historically these data collected periodically in the field by technicians using portable units. The significant investment in inexpensive microelectromechanical (MEMS) sensors mounted on untethered (energy-harvesting or battery-powered) microprocessors communicating wirelessly to the cloud is expected to change the way we collect asset health data. Permanently installed MEMS-based sensing units will enable near-real time data collection and reduce the safety exposure of technicians by eliminating the need to manually collect field data. With hundreds of MEMS-based sensing units expected to be installed at a single site it is vital to assure the data they produce and maintain them cost effectively. An asset management framework for validation of MEMS-based sensing units for condition monitoring and structural integrity (CM& SI) applications is proposed. An integral part of this framework is the proposed use of soft sensor models to replace technician inspections in the field. Soft sensor models are used in the process industry to stabilize product quality and process operations but there are few ex les in asset management applications. The contributions of this paper are twofold. Firstly, we use an interdisciplinary approach drawing on electronics, process control, statistics, machine learning, and asset management fields to describe the emerging field of permanently installed MEMS-based sensing units for CM& SI. Secondly, we development a framework for assuring validation of the data these sensing units generate.
Probabilistic Assessment of the Effect of Bolt Pre-Load Loss Over Time in Offshore Wind Turbine Bolted Ring-Flanges Using a Gaussian Process Surrogate Model
Publisher: American Society of Mechanical Engineers
Date: 05-06-2022
Abstract: Achieving and maintaining a suitable level of bolt pre-load is critical to ensure structural reliability under the Fatigue Limit State for bolted ring-flanges in offshore wind turbine structures. Bolt pre-load is likely to vary over lifetime, with re-tensioning applied if relaxation exceeds design guideline allowance. An approach to assess the influence of varying bolt pre-load may be useful in the operational context. Recent work has demonstrated the suitability of a Gaussian Process surrogate model to emulate Finite Element Method structural simulations models of bolted ring-flanges, with computational efficiency gains. In this paper we predict cumulative fatigue damage in bolts over time, given uncertainty in bolt pre-load estimation, using a Gaussian Process surrogate model. We perform Structural Reliability Analysis to deliver approximations of annual Probability of Failure and the Reliability Index, under the Fatigue Limit State. Our approximations are compared to targets defined in relevant design standards. Furthermore, we incorporate observations, and maintenance actions, in updating the Structural Reliability Analysis during operation, and suggest practical applications of this method to inform inspection and maintenance practices.
Prognostic modelling options for remaining useful life estimation by industry
Publisher: Elsevier BV
Date: 07-2011
Asset Planning Performance Measurement
Publisher: Springer International Publishing
Date: 2016
IoT-Based Prognostics and Systems Health Management for Industrial Applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2016
Editorial: Data-Driven Cognitive Manufacturing—Applications in Predictive Maintenance and Zero Defect Manufacturing
Publisher: Frontiers Media SA
Date: 08-02-2021
Data-driven reliability analysis of Boeing 787 Dreamliner
Publisher: Elsevier BV
Date: 07-2020
Factors that influence failure behaviour and remaining useful life of mining equipment components
Publisher: SAGE Publications
Date: 2013
DOI: 10.1155/2013/913048
Abstract: Mobile mining equipment often operates in harsh environments characterised by remote locations and highly variable rock and operating conditions. This research explores the hypothesis that the failure behaviour of mining equipment is influenced by the physical properties of the ore and waste. We describe a method of examining this relationship via data mining on maintenance records and apply it to the hydraulic cylinders of two classes of earthmoving mobile equipment. Failure data for the analysis are drawn from maintenance work orders from 14 sites mining for haematite iron, nickel sulphide, and coking and thermal coal. The results show that the distributions of the estimated life parameters for hydraulic cylinders on earthmoving equipment are distinctly different for haematite iron, coal, and nickel sulphide sites. Analysis of the relationship between selected physical properties identified the influence of rock impact hardness number, abrasion index, and absolute hardness of the ore as significant factors for these hydraulic cylinders. Their effects are significant when parameters are considered in combination, for ex le, rock impact hardness number and abrasion index, and vary according to the cylinder type and asset class. The engineering implications of these results are considered with respect to known failure modes of the cylinders.
Detecting Asset Cascading Failures Using Complex Network Analysis
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Risk prioritisation for cultural and arts infrastructure
Publisher: Springer International Publishing
Date: 12-11-2019
Long-term reliability of lead-free electronic systems
Publisher: IEEE
Date: 07-2016
Requirements for the application of the Digital Twin Paradigm to offshore wind turbine structures for uncertain fatigue analysis
Publisher: Elsevier BV
Date: 02-2023
Sensing system for low cost condition monitoring of remote assets
Publisher: Elsevier BV
Date: 2020
DRAT: Data risk assessment tool for university–industry collaborations
Publisher: Cambridge University Press (CUP)
Date: 2020
DOI: 10.1017/DCE.2020.13
Abstract: For research in the fields of engineering asset management (EAM) and system health, relevant data resides in the information systems of the asset owners, typically industrial corporations or government bodies. For academics to access EAM data sets for research purposes can be a difficult and time-consuming task. To facilitate a more consistent approach toward releasing asset-related data, we have developed a data risk assessment tool (DRAT). This tool evaluates and suggests controls to manage, risks associated with the release of EAM datasets to academic entities for research purposes. Factors considered in developing the tool include issues such as where accountability for approval sits in organizations, what affects an in idual manager’s willingness to approve release, and how trust between universities and industry can be established and damaged. This paper describes the design of the DRAT tool and demonstrates its use on case studies provided by EAM owners for past research projects. The DRAT tool is currently being used to manage the data release process in a government-industry-university research partnership.
Case study: Level of service criteria for critical rotating assets
Publisher: Springer London
Date: 31-07-2011
RelOps – A Whole-of-Organisation Approach for Reliability Analytics
Publisher: Springer International Publishing
Date: 2023
A study of vibroacoustic coupling between a pump and attached water-filled pipes
Publisher: Acoustical Society of America (ASA)
Date: 02-2007
DOI: 10.1121/1.2405131
Abstract: This paper presents a model for the vibroacoustical behavior of a pump coupled with water-filled pipes. Coupling between (a) the pump and the inlet and outlet pipes, and (b) the pipe wall and the fluid contained in the pipe, is investigated through analytical modeling and numerical simulation. In the model, the pump is represented by a rigid body supported by multiple elastic mounts, and the inlet and outlet pipes by two semi-infinite water-filled pipes. The vibration characteristics of the coupled system under the excitation of mechanical forces and fluid-borne forces at the pump are calculated. The results enhance our understanding about how the input mechanical and fluid excitation energy at the pump is transmitted to the pipes and how to relate the piping vibroacoustical response to the excitations at the pump. This study assists in predicting dynamic stress in pipes for given excitations at the pump, and in developing methods to identify the nature (fluid or mechanical) of the excitation forces at the pump using the vibration and dynamic pressure measurements on the pump ipe system.
A new look at compliance with work procedures: An engagement perspective
Publisher: Elsevier BV
Date: 06-2018
Are you sure you want me to follow this? A study of procedure management, user perceptions and compliance behaviour
Publisher: Elsevier BV
Date: 2018
Relevant asset health information for executives
Publisher: Inderscience Publishers
Date: 2013
The development of ISO 55000 series standards
Publisher: Springer International Publishing
Date: 30-11-2015
Cavitation Sensitivity Parameter Analysis for Centrifugal Pumps Based on Spectral Methods
Publisher: Springer International Publishing
Date: 30-11-2014
Automatic semantic knowledge extraction from electronic forms
Publisher: SAGE Publications
Date: 23-05-2022
DOI: 10.1177/1748006X221098272
Abstract: Electronic tabular forms are an intuitive way for organisations to collect, present and store structured information for human readers. Forms use features such as fonts, colours and cell positioning to help readers navigate and find information. Millions of forms, typically in Portable Document Format (PDF), are generated by businesses as part of routine operations. Unlike human readers, machines are not able to directly ‘understand’ the implicit cues contained in the fonts, colours and use of boxes without explicit processing. In this paper, a supervised computer vision model is proposed to decompose the PDF form document into nested microtables. The cells within these microtables are then processed using a customisable rule bank for meaningful table content and semantic relationship extraction. The process is demonstrated on an industry dataset of 37 maintenance procedure documents containing 373 pages and 1016 unique microtables. A web application EMU (Extracting Machine Understandable Semantics from Forms) demonstrates how data captured in tables with different dimensions in procedural forms can be automatically extracted and stored in JavaScript Object Notation (JSON). Identifying and extracting nested tables is a critical fundamental step for future applications to support machine-automated search and extraction of data at scale for both maintenance and other procedural documentation.
Where do we start? guidance for technology implementation in maintenance management for manufacturing
Publisher: American Society of Mechanical Engineers
Date: 10-06-2019
Abstract: Recent efforts in Smart Manufacturing (SM) have proven quite effective at elucidating system behavior using sensing systems, communications and computational platforms, along with statistical methods to collect and analyze real-time performance data. However, how do you effectively select where and when to implement these technology solutions within manufacturing operations? Furthermore, how do you account for the human-driven activities in manufacturing when inserting new technologies? Due to a reliance on human problem solving skills, today’s maintenance operations are largely manual processes without wide-spread automation. The current state-of-the-art maintenance management systems and out-of-the-box solutions do not directly provide necessary synergy between human and technology, and many paradigms ultimately keep the human and digital knowledge systems separate. Decision makers are using one or the other on a case-by-case basis, causing both human and machine to cannibalize each other’s function, leaving both disadvantaged despite ultimately having common goals. A new paradigm can be achieved through a hybridized systems approach — where human intelligence is effectively augmented with sensing technology and decision support tools, including analytics, diagnostics, or prognostic tools. While these tools promise more efficient, cost-effective maintenance decisions, and improved system productivity, their use is hindered when it is unclear what core organizational or cultural problems they are being implemented to solve. To explicitly frame our discussion about implementation of new technologies in maintenance management around these problems, we adopt well established error mitigation frameworks from human factors experts — who have promoted human-systems integration for decades — to maintenance in manufacturing. Our resulting tiered mitigation strategy guides where and how to insert SM technologies into a human-dominated maintenance management process.
Technical language processing: Unlocking maintenance knowledge
Publisher: Elsevier BV
Date: 2021
Where Do We Start? Guidance for Technology Implementation in Maintenance Management for Manufacturing
Publisher: ASME International
Date: 22-07-2019
DOI: 10.1115/1.4044105
Abstract: Recent efforts in smart manufacturing (SM) have proven quite effective at elucidating system behavior using sensing systems, communications, and computational platforms, along with statistical methods to collect and analyze the real-time performance data. However, how do you effectively select where and when to implement these technology solutions within manufacturing operations? Furthermore, how do you account for the human-driven activities in manufacturing when inserting new technologies? Due to a reliance on human problem-solving skills, today’s maintenance operations are largely manual processes without wide-spread automation. The current state-of-the-art maintenance management systems and out-of-the-box solutions do not directly provide necessary synergy between human and technology, and many paradigms ultimately keep the human and digital knowledge systems separate. Decision makers are using one or the other on a case-by-case basis, causing both human and machine to cannibalize each other’s function, leaving both disadvantaged despite ultimately having common goals. A new paradigm can be achieved through a hybridized system approach—where human intelligence is effectively augmented with sensing technology and decision support tools, including analytics, diagnostics, or prognostic tools. While these tools promise more efficient, cost-effective maintenance decisions and improved system productivity, their use is hindered when it is unclear what core organizational or cultural problems they are being implemented to solve. To explicitly frame our discussion about implementation of new technologies in maintenance management around these problems, we adopt well-established error mitigation frameworks from human factors experts—who have promoted human–system integration for decades—to maintenance in manufacturing. Our resulting tiered mitigation strategy guides where and how to insert SM technologies into a human-dominated maintenance management process.
MWO2KG and Echidna
Publisher: SAGE Publications
Date: 05-11-2022
DOI: 10.1177/1748006X221131128
Abstract: Unstructured technical texts are a rich resource of engineering knowledge underutilised for data analysis. Maintenance work orders (MWO), for ex le, capture valuable information to inform what work was done on an asset and why. Data in MWO short text fields is unstructured, terse and jargon-rich, complicating the ability of both humans and machines to read it. Our challenge is to efficiently extract technical information from the MWO short text field and combine it with data in structured fields such as dates, functional location, make and model of the asset. In this paper we present a technical language processing-based solution for this problem. Echidna is an intuitive query-enabling interface that visualises historic asset data in the form of a knowledge graph. This knowledge graph is produced by MWO2KG, which uses deep learning supported by annotated training data to automatically construct knowledge graphs from unstructured technical text combined with data from structured fields. The tools are tested on maintenance work order and delay accounting data provided by industry partners. These tools provide reliability engineers with an efficient way to find information in historic asset data for failure modes and effects analysis, maintenance strategy validation and process improvement work. Source code for both tools is available on GitHub under the Apache 2.0 License.
A general articulation angle stability model for non-slewing articulated mobile cranes on slopes
Publisher: Informa UK Limited
Date: 2014
Modeling Tool to Support Budgeting and Planning Decisions for Pump Overhauls
Publisher: American Society of Civil Engineers (ASCE)
Date: 07-2011
Informative Bayesian Survival Analysis to Handle Heavy Censoring in Lifetime Data
Publisher: IEEE
Date: 12-12-2021
Applying cognitive computing to maintainer-collected data
Publisher: No publisher found
Date: 2017
Adapting natural language processing for technical text
Publisher: Wiley
Date: 30-06-2021
DOI: 10.1002/AIL2.33
Abstract: Despite recent dramatic successes, natural language processing (NLP) is not ready to address a variety of real‐world problems. Its reliance on large standard corpora, a training and evaluation paradigm that favors the learning of shallow heuristics, and large computational resource requirements, makes domain‐specific application of even the most successful NLP techniques difficult. This paper proposes technical language processing (TLP) which brings engineering principles and practices to NLP specifically for the purpose of extracting actionable information from language generated by experts in their technical tasks, systems, and processes. TLP envisages NLP as a socio‐technical system rather than as an algorithmic pipeline. We describe how the TLP approach to meaning and generalization differs from that of NLP, how data quantity and quality can be addressed in engineering technical domains, and the potential risks of not adapting NLP for technical use cases. Engineering problems can benefit immensely from the inclusion of knowledge from unstructured data, currently unavailable due to issues with out of the box NLP packages. We illustrate the TLP approach by focusing on maintenance in industrial organizations as a case‐study.
IEEE Access Special Section Editorial: Intelligent Systems for the Internet of Things
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Methods for displaying and calibration of Cox proportional hazards models
Publisher: SAGE Publications
Date: 26-11-2017
Abstract: Cox proportional hazards modelling is a widely used technique for determining relationships between observed data and the risk of asset failure when model performance is satisfactory. Cox proportional hazards models possess good explanatory power and are used by asset managers to gain insight into factors influencing asset life. However, validation of Cox proportional hazards models is not straightforward and is seldom considered in the maintenance literature. A comprehensive validation process is a necessary foundation to build trust in the failure models that underpin remaining useful life prediction. This article describes data splitting, model discrimination, misspecification and fit methods necessary to build trust in the ability of a Cox proportional hazards model to predict failures on out-of-s le assets. Specifically, we consider (1) Prognostic Index comparison for training and test sets, (2) Kaplan–Meier curves for different risk bands, (3) hazard ratios across different risk bands and (4) calibration of predictions using cross-validation. A Cox proportional hazards model on an industry data set of water pipe assets is used for illustrative purposes. Furthermore, because we are dealing with a non-statistical managerial audience, we demonstrate how graphical techniques, such as forest plots and nomograms, can be used to present prediction results in an easy to interpret way.
Asset data quality - A case study on mobile mining assets
Publisher: Springer International Publishing
Date: 30-11-2014
Fault prediction and modelling in transport networks
Publisher: IEEE
Date: 05-2018
Using Job Characteristics to Inform Interface Design for Industrial Maintenance Procedures
Publisher: ACM
Date: 06-05-2021
Uncertainty Quantification of Density and Stratification Estimates with Implications for Predicting Ocean Dynamics
Publisher: American Meteorological Society
Date: 07-2019
DOI: 10.1175/JTECH-D-18-0200.1
Abstract: We present a statistical method for reconstructing continuous background density profiles that embeds incomplete measurements and a physically intuitive density stratification model within a Bayesian hierarchal framework. A double hyperbolic tangent function is used as a parametric density stratification model that captures various pycnocline structures in the upper ocean and offers insight into several density profile characteristics (e.g., pycnocline depth). The posterior distribution is used to quantify uncertainty and is estimated using recent advances in Markov chain Monte Carlo s ling. Temporally evolving posterior distributions of density profile characteristics, isopycnal heights, and nonlinear ocean process models for internal gravity waves are presented as ex les of how uncertainty propagates through models dependent on the density stratification. The results show 0.95 posterior interval widths that ranged from 2.5% to 4% of the expected values for the linear internal wave phase speed and 15%–40% for the nonlinear internal wave steepening parameter. The data, collected over a year from a through-the-column mooring, and code, implemented in the software package Stan, accompany the article.
Optimised asset replacement strategy in the presence of lead time uncertainty
Publisher: Elsevier BV
Date: 02-2013
Variable selection for conveyor-belt mean wear rate prediction
Publisher: Juniper Publishers
Date: 26-02-2021
Designing SNAKES AND LADDERS: An Analogy for Asset Management Strategy Development
Publisher: SAGE Publications
Date: 02-07-2015
Abstract: Background. Asset Management improvement initiatives often fail to deliver the intended value. Poorly developed strategy contributes to these failures. Aim. To assist managers in developing skills in Asset Management (AM) strategy in order to meet the requirements set out in the new ISO 55001 Asset Management Standard. Method. Managers develop the layout for a SNAKES AND LADDERS (S& L) game board. This involves managers deciding what strategic activities to pursue (ladders) and where to place them on the board. Other considerations include the sequence of the ladders and their length, where the length is a proxy for impact of the activity. Managers also need to identify what risks (snakes) are associated with each activity and what impact they have (the length of the snake). Results. The board development process helps managers to identify factors crucial to effective strategy development such as limiting the number of activities, sequencing these activities over an appropriate time scale, assessing risks of each activities and the consequence of risks being realized on subsequent activities. Conclusion. The S& L training is an accessible and time-efficient way to get managers to engage with each other and with crucial concepts in strategy development in the asset management context.
Managing streamed sensor data for mobile equipment prognostics
Publisher: Cambridge University Press (CUP)
Date: 2022
DOI: 10.1017/DCE.2022.4
Abstract: The ability to wirelessly stream data from sensors on heavy mobile equipment provides opportunities to proactively assess asset condition. However, data analysis methods are challenging to apply due to the size and structure of the data, which contain inconsistent and asynchronous entries, and large periods of missing data. Current methods usually require expertise from site engineers to inform variable selection. In this work, we develop a data preparation method to clean and arrange this streaming data for analysis, including a data-driven variable selection. Data are drawn from a mining industry case study, with sensor data from a primary production excavator over a period of 9 months. Variables include 58 numerical sensors and 40 binary indicators captured in 45-million rows of data describing the conditions and status of different subsystems of the machine. A total of 57% of time st s contain missing values for at least one sensor. The response variable is drawn from fault codes selected by the operator and stored in the fleet management system. Application to the hydraulic system, for 21 failure events identified by the operator, shows that the data-driven selection contains variables consistent with subject matter expert expectations, as well as some sensors on other systems on the excavator that are less easy to explain from an engineering perspective. Our contribution is to demonstrate a compressed data representation using open-high-low-close and variable selection to visualize data and support identification of potential indicators of failure events from multivariate streamed data.
Cox Proportional Hazards modelling of blockage risk in vitrified clay pipes
Publisher: Informa UK Limited
Date: 30-09-2017
Developing and evaluating predictive conveyor belt wear models
Publisher: Cambridge University Press (CUP)
Date: 2020
DOI: 10.1017/DCE.2020.1
Abstract: Conveyor belt wear is an important consideration in the bulk materials handling industry. We define four belt wear rate metrics and develop a model to predict wear rates of new conveyor configurations using an industry dataset that includes ultrasonic thickness measurements, conveyor attributes, and conveyor throughput. All variables are expected to contribute in some way to explaining wear rate and are included in modeling. One specific metric, the maximum throughput-based wear rate, is selected as the prediction target, and cross-validation is used to evaluate the out-of-s le performance of random forest and linear regression algorithms. The random forest approach achieves a lower error of 0.152 mm/megatons (standard deviation [SD] = 0.0648). Permutation importance and partial dependence plots are computed to provide insights into the relationship between conveyor parameters and wear rate. This work demonstrates how belt wear rate can be quantified from imprecise thickness testing methods and provides a transparent modeling framework applicable to other supervised learning problems in risk and reliability.
Characterizing the Performance of LED Reflective Distance Sensors
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Parameter extraction with reservoir computing
Publisher: AIP Publishing
Date: 03-2021
DOI: 10.1063/5.0039193
Abstract: We study the task of determining parameters of dynamical systems from their time series using variations of reservoir computing. Averages of reservoir activations yield a static set of random features that allows us to separate different parameter values. We study such random feature models in the time and frequency domain. For the Lorenz and Rössler systems throughout stable and chaotic regimes, we achieve accurate and robust parameter extraction. For vibration data of centrifugal pumps, we find a significant ability to recover the operating regime. While the time domain models achieve higher performance for the numerical systems, the frequency domain models are superior in the application context.
A conceptual framework and practical guide for assessing fitness-to-operate in the offshore oil and gas industry
Publisher: Elsevier BV
Date: 07-2014
DOI: 10.1016/J.AAP.2013.12.005
Abstract: The paper outlines a systemic approach to understanding and assessing safety capability in the offshore oil and gas industry. We present a conceptual framework and assessment guide for understanding fitness-to-operate (FTO) that builds a more comprehensive picture of safety capability for regulators and operators of offshore facilities. The FTO framework defines three enabling capitals that create safety capability: organizational capital, social capital, and human capital. For each type of capital we identify more specific dimensions based on current theories of safety, management, and organizational processes. The assessment guide matches specific characteristics to each element of the framework to support assessment of safety capability. The content and scope of the FTO framework enable a more comprehensive coverage of factors that influence short-term and long-term safety outcomes.
Digitalization and reasoning over engineering textual data stored in spreadsheet tables
Publisher: Elsevier BV
Date: 2020
Estimating mean residual life for a case study of rail wagon bearings
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2012
Natural Language Query for Technical Knowledge Graph Navigation
Publisher: Springer Nature Singapore
Date: 2022
Are managerial pressure, technological control and intrinsic motivation effective in improving data quality?
Publisher: Elsevier BV
Date: 11-2013
Asset management - quo vadis (where are you going)?
Publisher: Inderscience Publishers
Date: 2015
A fusion prognostics-based qualification test methodology for microelectronic products
Publisher: Elsevier BV
Date: 08-2016
A Sequential k-Nearest Neighbor Classification Approach for Data-Driven Fault Diagnosis Using Distance- and Density-Based Affinity Measures
Publisher: Springer International Publishing
Date: 2016
Classifying machinery condition using oil samples and binary logistic regression
Publisher: Elsevier BV
Date: 08-2015
Machinery Condition Prognosis Using Multivariate Analysis
Publisher: Springer London
Date: 2006
Data-driven approach for labelling process plant event data
Publisher: PHM Society
Date: 24-01-2022
DOI: 10.36001/IJPHM.2022.V13I1.3045
Abstract: An essential requirement in any data analysis is to have a response variable representing the aim of the analysis. Much academic work is based on laboratory or simulated data, where the experiment is controlled, and the ground truth clearly defined. This is seldom the reality for equipment performance in an industrial environment and it is common to find issues with the response variable in industry situations. We discuss this matter using a case study where the problem is to detect an asset event (failure) using data available but for which no ground truth is available from historical records. Our data frame contains measurements of 14 sensors recorded every minute from a process control system and 4 current motors on the asset of interest over a three year period. In this situation the ``how to'' label the event of interest is of fundamental importance. Different labelling strategies will generate different models with direct impact on the in-service fault detection efficacy of the resulting model. We discuss a data-driven approach to label a binary response variable (fault/anomaly detection) and compare it to a rule-based approach. Labelling of the time series was performed using dynamic time warping followed by agglomerative hierarchical clustering to group events with similar event dynamics. Both data sets have significant imbalance with 1,200,000 non-event data but only 150 events in the rule-based data set and 64 events in the data-driven data set. We study the performance of the models based on these two different labelling strategies, treating each data set independently. We describe decisions made in window-size selection, managing imbalance, hyper-parameter tuning, training and test selection, and use two models, logistic regression and random forest for event detection. We estimate useful models for both data sets. By useful, we understand that we could detect events for the first four months in the test set. However as the months progressed the performance of both models deteriorated, with an increasing number of false positives, reflecting possible changes in dynamics of the system. This work raises questions such as ``what are we detecting?'' and ``is there a right way to label?'' and presents a data driven approach to support labelling of historical events in process plant data for event detection in the absence of ground truth data.
A cost-benefit analysis of electric loaders to reduce diesel emissions in underground hard rock mines
Publisher: IEEE
Date: 10-2014
Using Context-Free Grammar to Generate Synthetic Technical Short Texts
Publisher: Springer International Publishing
Date: 2022
Related Organisations
University Of Oxford
Location: United Kingdom of Great Britain and Northern Ireland
Related Funding Activities
Industrial Transformation Research Hubs - Grant ID: IH140100012
Start Date: 04-2016
End Date: 12-2021
Amount: $4,997,672.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Research Hubs - Grant ID: IH200100009
Start Date: 07-2021
End Date: 06-2026
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded ActivityIndustrial Transformation Training Centres - Grant ID: IC180100030
Start Date: 02-2019
End Date: 02-2025
Amount: $3,925,357.00
Funder: Australian Research Council
View Funded Activity