Module Catalogue 2019/20

CME8124 : Multivariate Methods

  • Offered for Year: 2019/20
  • Module Leader(s): Dr Chris O'Malley
  • Owning School: Engineering
  • Teaching Location: Newcastle City Campus
Semesters
Semester 1 Credit Value: 10
ECTS Credits: 5.0
Pre Requisites
Pre Requisite Comment

Basic knowledge of statistics from A-level mathematics or equivalent

Co Requisites
Co Requisite Comment

N/A

Aims

To introduce students to multivariate statistical methodologies that allow them to analyse industrial data thereby realising an enhanced understanding of the process. The course covers three aspects in terms of focus – data pre-screening, feature extraction and process modelling.

This module addresses a real challenge that is of extreme relevance to the process industries. There are many cases where it is not possible to undertake experimental design and utilise the resulting data to attain enhanced process understanding, or the development of models for process monitoring or optimisation. The only data available may be that collected directly on the plant. Consequently it is not necessarily of an appropriate form, in the first instance, to execute the required task, be it finger printing or modelling. Therefore to extract the maximum amount of information, necessitates in the first instance the cleaning of the data (removing outliers, filtering the data, in-filling for missing data, time shifting variable, for example) prior to the application of appropriate statistical methodologies, such as principal component analysis if the objective is feature extraction. Multivariate statistical control of both continuous and batch industrial processes will be explored through the extension to principal component analyses. Additionally for process control and optimisation, it is essential to develop a robust model of the process of interest. The course introduces a number of linear modelling techniques to help realise the aforementioned objectives.

Outline Of Syllabus

Multivariate Data Analysis: Introduction: What problems can be addressed using these techniques; Preliminary Data Analysis – Handling of Inhomogeneous Data (Missing Data; Outliers; Noisy Data; Time Alignment); Graphical Procedures. Dimensionality Reduction (Principal Component Analysis); Modelling techniques: Multiple linear regression, Principal component regression; Projection to Latent Structures. Multivariate Statistical Performance Monitoring – Continuous and Batch Processes.

Learning Outcomes

Intended Knowledge Outcomes

To develop the knowledge of the students, through their exposure to a raft of methodologies (data pre-screening, feature extraction and process modelling) that are applicable both in the laboratory and the production plant, thereby enabling them to help in the delivery of enhanced process performance, process understanding and process optimisation.

To develop an awareness of the advantages and disadvantages of the different methodologies (data pre-screening, feature extraction and process modelling) presented for the analysis of industrial data.

To develop the critical ability of the students enabling them to identify the most appropriate methodologies for the problem to be addressed (data pre-screening, feature extraction and process modelling).

Intended Skill Outcomes

The ability to understand the fundamental statistical techniques that form the basis of multivariate methods and how they relate to the baseline discipline.

The ability to interrogate the results from the execution of a multivariate data analysis in the context of the problem being addressed, e.g. to realise an enhanced understanding of process operation, and to determine their validity and applicability for solving the problem.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture161:0016:00N/A
Guided Independent StudyAssessment preparation and completion14:304:30Problem Solving Exercise 2 and subsequent writing up in report format
Guided Independent StudyAssessment preparation and completion14:304:30Problem Solving Exercise 1 and subsequent writing up in report format
Scheduled Learning And Teaching ActivitiesSmall group teaching162:0032:00Numerical practice sessions
Guided Independent StudyIndependent study143:0043:00Review lecture material and prepare for small group teaching
Total100:00
Teaching Rationale And Relationship

Lectures convey the statistical concepts and theory and their application in process engineering. Numerical practice sessions support the learning introduced in lectures through the students having the opportunity to apply the concepts to a number of problems varying in terms of complexity. The numerical practice sessions allow the completion of assignment work.

Reading Lists

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Prob solv exercises1M50Assessed report - Data Prescreening, Principal Component Analysis & process monitoring (maximum 25 pages)
Prob solv exercises1M50Assessed report - Process Data Modelling (set week 11), maximum 20 pages
Assessment Rationale And Relationship

Assignments allow engineering problems to be set and solved using computer software. They also provide the opportunity for the key skills listed above to be assessed and implemented.

Timetable

Past Exam Papers

General Notes

N/A

Disclaimer: The information contained within the Module Catalogue relates to the 2019/20 academic year. In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described. Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2020/21 entry will be published here in early-April 2019. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.