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Course syllabus - Power plants and processes monitoring and diagnostics

Scope

2.5 credits

Course code

ERA314

Valid from

Autumn semester 2019

Education level

Second cycle

Progressive Specialisation

A1N (Second cycle, has only first-cycle course/s as entry requirements)

Main area(s)

Energy Engineering

Organisation

School of Business, Society and Engineering

Ratified

2019-01-31

Literature lists

Course literature is preliminary up to 8 weeks before course start. Course literature can be valid over several semesters.

    • Ratified date: 2019-10-22
    • Latest update: 2025-05-27

    Ingen specifik kurslitteratur. No specific literature.

    Akademin för ekonomi, samhälle och teknik,

Objectives

The purpose of the course is to familiarize with methods for process monitoring and diagnostics and with the use of physics-based and data driven models. The course will provide knowledge on fault detection and identification for condition based maintenance, the current state-of-the-art, and future technology developments.

Learning outcomes

  1. Describe the fundamental methods used for process monitoring and diagnostics
  2. Discuss the basic principles and limitations of physics-based and data driven techniques
  3. Compare different methods for fault detection and identification
    Per
  4. form fault detection and identification on data from a numerical model
  5. Describe the structure and functions of a diagnostic-prognostic framework and design a simple diagnostic system

Course content

The course will give an overview of process monitoring and diagnostics with a focus on the power generation sector. Different fault detection methods using physics-based and data-driven models coupled with machine learning techniques will be covered. The course will address challenges currently experienced in the process industry with regards to power plants health management. The participants will familiarize with process monitoring and apply their knowledge of process performance and simulation to the development of advanced algorithms for fault diagnosis.

Specific requirements

120 credits of which 90 credits engineering or natural science and 7,5 credits mathematics. In addition Swedish course B/Swedish course 3 and English course A/English course 6 are required. For courses given entirely in English exemption is made from the requirement in Swedish course B/Swedish course 3.

Examination

Seminar (SEM1) 0.5 hp, grade Passed (G), (Learning outcomes: 1, 2, 3, 4, 5)
Assignment (INL1) 1 hp, Grade: 3, 4, 5, (Learning outcomes: 3, 4)
Individual home examination (HEM1) 1 hp, Grade: 3, 4, 5, (Learning outcomes: 1, 2, 3, 4, 5)

A student who has a certificate from MDU regarding disability study support, can request adaptions for the examination. It is the examiner who takes decisions on any adaptions, based on the certificate and other conditions.

Grade

Four-grade scale, digits