Data Analytics Call

Data Analytics Competition 2016 – Call for Applications (November 2016)

This competition has now closed. We will be publishing results of the competition in due course. 

 

Core expertise of the Optimise Hub is listed below and therefore applicants should consider these when applying. The expertise can be applied to a variety of different sectors.

Big Data Analytics & Process Optimisation

Operations managers can use advanced analytics experts to take a deep dive into historical process data in order to identify patterns and relationships among discrete process steps and inputs, and then optimise the factors that prove to have the greatest effect on yield. With the abundance of real-time data, and the capability to conduct such sophisticated statistical assessments our experts can take previously isolated data sets, aggregate them, and analyse them to reveal important insights.

Sensor Acquisition & Data Management

Sensor data acquisition is the process of measuring an electrical or physical phenomenon such as voltage, current, temperature, pressure, or sound with a machine of any kind. A DAQ (data acquisition) system consists of sensors, DAQ measurement hardware, and a computer with programmable software.  Through this method one can assess and manage processing power, productivity, display, and connectivity capabilities of industry-standard machines providing a more powerful, flexible, and cost-effective measurement solution.

CPS modelling and simulation

A cyber-physical system (CPS) is a system featuring a tight combination of, and coordination between, the system’s computational and physical elements.” Embedded computing devices controlling physical processes are today found in many diverse application areas, including automotive, aerospace, energy, and telecommunications. Simulation of a system is the operation of a model, that is a representation of that system.  The model is developed as it is amenable to manipulation which would otherwise be impossible, too expensive, or too impractical to perform on the system which it portrays. In this way the simulation of the model can be studied, and, from this, properties concerning the behaviour of the actual system can be inferred.

Fault Prediction and Detection

Quality assurance activities such as testing, verification and validation, fault tolerance and fault prediction are key elements of any business where machine-based systems are used. When any company does not have sufficient budget and time for testing an entire system or application, analysts can, through the use of advanced analytics, develop and use fault prediction algorithms to identify the parts of the system that are more defect prone.

Predictive Maintenance

Predictive Maintenance supports the convenient scheduling of corrective maintenance, and looks to prevent unexpected equipment failures through analysis of real time data.  Building on Fault prediction and detection techniques, companies can understand which equipment needs maintenance, and the maintenance work can then be better planned (spare parts, people, etc.) thus eliminating “unplanned stops” or failures, switching to shorter and fewer “planned stops”. The advantages of predictive maintenance are continuous use of assets,  increased equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimized spare parts handling.

Energy Efficiency in Manufacturing or other Business Systems

Traditionally the performance of any production system is assessed by monitoring the following: cost, time, quality and flexibility whilst research and development has typically focused on technological improvements often at the expense of a higher energy consumption. Utilising data analytics, experts can now look to develop tools that can integrate performance metrics, models and simulations with real-time plant energy data in order to optimize energy productivity in real-time and, in turn, reduce waste and improve energy throughout the waste hierarchy.

Eligibility Criteria

Applications will be assessed against the following criteria:

  • Novelty and technical risk
  • Alignment with Hub objectives and expertise – e.g. the increased take up of ICT technologies to optimise production and business processes

 

Duration of Winning Projects

Up to 3 weeks of FREE data analysis performed by researchers from the Institute of Industrial Research

Application process, requirements and assessment

Applicants should use the simple data-analysis-competition-application-form provided and completed forms should be e-mailed to the Hub email address: info@optimisehub.org.uk

 

Awards will be confirmed upon acceptance of the non-negotiable Terms and Conditions set out in the Award Letter for this call.

For any questions relating to the application process, please email: ann.swift@port.ac.uk

Schedule and deadline

  • Deadline for submission of applications: January 6th 2017                 
  • Notification of successful applications: January 27th 2017