# (Ass): ASSIGNMENT

ASSIGNMENT/COURSEWORK PROFORMA
Module code:MN5618
Assessment title:Advanced measurement systems and dataanalysis
Module tutors:Haitham Elhussieny
Main objectives of the assessment:To enable students to demonstrate their in-depth knowledge of the principles of advancedmeasurement system and data analysis and abilities to apply them to solve practical problems relatedto advanced measurement system and data analysis.
Brief Description of the assessment:Given some measurement tasks in an industrial context, you will need to complete three tasks: thefirst two tasks are to identify and evaluate the suitable measurement solutions and the third task is todesign/develop the Matlab or Excelprogram for the measurement data analysis. These tasks arerelated to the key topics of the module.
Learning outcomes for the assessment (refer to theappropriate module learning outcomes)Students will be able to demonstrate the following:• To gain systematic understanding of measurement errorsand uncertainties;• To gain comprehensive understanding of the operatingprinciples of advanced sensors;• To develop critical awareness of the range and capabilitiesof advanced measuring instruments;• To gain systematic understanding of linear/nonlinear modelsfor advanced data analysis.• To perform error analysis and uncertainty evaluation ofpractical measurement systems;• To critically apply and evaluate different linear/nonlinearmodels for advanced data analysis.• To gain practical experience in the use of measuringinstruments;• To develop critical awareness of the wide applications ofadvanced measuring instruments.
Assessment criteria:1. Understanding of measurementsystems (including sensors /transducers, CMMs and surfacemeasuring machines) (40%)2. Consideration of errorsources, calibration andtraceability (10%)3. Selection considerations (10%)4. Understanding of nonlinearleast square method (15%)5. Data analysis algorithm / coding(15%)6. Discussions and presentation(10%)Task 1: 45%Task 2: 20%Task 3: 35%
Assessment method by which a student can demonstrate the learningoutcomes:Identification and evaluation of the practical measuring systems for thegiven measurement tasks. Design and implementation of algorithms andMatlab programs for practical data analysis.
Weighting:100% of module marks
Format of the assessment/coursework: (Guidelines on the expected format and length ofsubmission): *Note: full reports may not exceed 30 pages (including appendices)Format is a formal written report including diagrams/code; calculations (with data; formula; workingsand assumptions) and discussion/ comments. Report to be written using Word in a 12 point font.Typical length of report is 2500 words, comprising Title Page; Introduction; Task 1, Task 2,Discussions, Conclusions and References. Word count does not include code done in Matlab or excel,which should be included in an appendix.
Assessment date/submission deadline:Please submit by 11:59 am UK Time on Thursday, 3 March 2022 via WISEflow** Please ensure to give yourself enough time to submit before the deadline.
Indicative reading list:• Fraden J. (2015). Handbook of modern sensors: physics, designs, and applications. Springer.• Morris A S. (2012). Measurement and instrumentation: theory and application. Academic Press.• Leach R. (2014). Fundamental principles of engineering nanometrology (Micro and NanoTechnologies). Elsevier.• Flack D R and Hannaford J. (2005). Fundamental good practice in dimensional metrology”. NPLGood practice guide No. 80 (free download from www.npl.co.uk).• Bell S A. (2001). A beginner’s guide to uncertainty in measurement. NPL good practice guide No.11 (free download from www.npl.co.uk).• Barker R M, Cox M G, Forbes A B and Harris P M. (2007). Software Support for Metrology BestPractice Guide No. 10: Discrete Modelling and Experimental Data Analysis. National PhysicalLaboratory, Teddington.• Higham D J, Higham N J. (2016). MATLAB Guide, Third edition. Philadelphia: Society forIndustrial and Applied Mathematics.• Bell S A. (2001) A beginner’s guide to uncertainty in measurement. NPL good practice guide No.11 – www.npl.co.uk
Other informationUse your student ids for anonymity (i.e. no student names on the assignment itself).It is the student’s responsibility to ensure they are aware of polices and practice ongood academic practice and plagiarism. You can find more information about this in theSenate Regulations: Senate-Regulation-6-2020-07-01.pdf (brunel.ac.uk).
X
Y
100.027
9.972
87.871
54.955
55.016
87.950
9.969
99.947
-35.038
87.922
-67.983
55.071
-79.997
10.023
-67.853
-34.982
-34.939
-67.983
10.022
-80.039
55.000
-67.960
87.951
-35.001
100.008
10.057