Assessment nstructions: 1. Please familiarise yourself with the EIT Academic Honesty and Misconduct Policy , in order to understand your requirements and responsibilties as a student of EIT 2. Please refer to our Assessment, Moderation and Student Progress Procedure for information relating to extensions. Exte nsion requests should be submitted to your LSO a least 3 days prior to the due date. 3. Assessments submitted va email wil not be accepted. 4. Assessments must be submitted through Turn-it-in (unless otherwise stated). Your submission must:

1. Be a single document Word or PDF only)
2. Include at least 20 words of machine -readabe text, and
3. Not exceed 10MB

5. You must use the provided assessment cover page availabe on your Moodle sudent homepage. Submissions wthout a cover page wil not be acce pted. 6. You must correctly title your document/s For example: UNIT#_ASSESSMENT#_YOURNAME_DATE E.g. ME501_Assessment2_SteveMackay_01Aug2019 7. You must reference all content used from other sources incuding course materials, sldes, diagrams, etc. Do not dir ectly copy and paste from course materials or any other resources. Refer to the referencing section of the EIT eLibrary on Moodle for referencing guides. 8. It is your responsibty to check that you have submitted the correct fle, as revised submissions are not permitted after the due date and time. Important note : Failure to adhere to the above may result in academic penalties. Pease refer to the unit outline or EIT Policies and Procedures for further information.

Unit code and name:		ME605: Machine Learning for ndustrial Automation
Assessment #: Assessment type: Weighting: Total marks:		2A Mid-term Project Report) 25% 45 marks

Please complete your answers on the assessment cover page document available on Moodle. Clearly label your question numbers (there is no need to copy the full question over). nclude all working out. Task: You are required to write a report treating the subject of Machine Learning Based Condition Monitoring and Fault Detection in Engines. In this assignment you are required to do a lterature review and propose an architecture. No implementation is expected. Write a report that covers the following:

• Introduction/Background where you discuss condition monitoring and fault detection for motors and assets n general as applied to industrial applications.
• Literature review wher e you summarise 4 to 6 research papers that apply machine earning to the probem of condition monitoring and faut detection.
• Requirements to build a model where you discuss the steps to follow f you were to buld a a machine earning model for condition monitoring and fault detection. In particular, discuss how you would obtain and pre-process the data and the machine earning models that could be used.
• Conclusion
• References

Mark allocations:

• Structure and clearness of the report (5 marks) ? Formatting, writing, and lack of grammatical and spelling errors
• Introduction/Background (10 marks)

? It is expected to provide a clear background on the problem clarifying: what t s , why to solve it, and where it is required to be solved. Master of Engineering (Industrial Automation) 4

• Literature review (10 marks)

- It is expected to provide a comprehensive review of the lterature summarising 4 to 6 research papers.

• Proposed methodology for budng a model (10 marks)

- Suitablty of the proposed methodology in terms of data acquisition, pre-processing, and machine earning model. A diagram/flowchart clarifying the steps to follow and the architecture is expected.

• Conclusion (5 marks) - Clarity of the conclusion
• Referencing style and number of references cited (5 marks) - A minimum o 4 -6 high quality scholarly papers are expected to be cited . Word length:

2,000 words 20% The following papers could be useful:

• [1] Garcia, Carla E., Mario R. Camana, and nsoo Koo. "Machine learning-based scheme for multi-class fault detection in turbne engine dsks." ICT Express 7.1 (2021): 15-22.

https://ww w.sciencedirect.com/science/artice/pii/S2405959521000096

• [2] Tessaro, Iron, Viviana Cocco Mariani and Leandro dos Santos Coelho "Machine Learning Models Applied to Predictive Maintenance in Automotive Engine

Components. Multidisciplinary Digital Pubshng nstitute Proceedngs. Vol 64 No. 1. 2020 https://wwwmdpi.com/938200

Attachments:

ME605-Assessm....pdf