|
|
||||||||||||||||||
Course Catalog 2013-2014
EDE-33156 Machinery Diagnostics, 5 cr |
Additional information
Suitable for postgraduate studies
Person responsible
Juha Miettinen
Lessons
| Study type | P1 | P2 | P3 | P4 | Summer | Implementations | Lecture times and places |
|
|
|
|
|
|
|
|
Requirements
Accepted examination and exercises. If the course will be used in post graduate studies the minimum score is 3 and an extra exercise must be carried out.
Hyväksytyt tentti ja harjoitukset. Jos kurssin sisällyttää jatko-opintoihin on kurssista saatava vähintään arvosana 3 ja suoritettava henkilökohtainen lisäharjoitustyö.
Completion parts must belong to the same implementation
Principles and baselines related to teaching and learning
Course contains lextures, exercises and examination.
Learning outcomes
When passing the course the student knows operation monitoring, condition monitoring and machinery diagnostics processes. He knows the fundamentals of machinery vibration and he can identify normal running state and fault states from measurement result. Student can carry out analysis, diagnosis and conclusion of the running state of common industrial machinery by using measurement results and fault finding carts and diagnostics standards. He can implement special signal feature extraction procedures and simulate fault phenomena by using graphical programming soft ware. Student gets familiarize with multi parameter diagnostics and automated machinery diagnostics carried out by neural network and expert systems as well as safe running time prognostics methods.
Content
| Content | Core content | Complementary knowledge | Specialist knowledge |
| 1. | Operation monitoring, condition monitoring, diagnostics and prognostics. Diagnostics procedure. | Fault mode simulation with LabView software. | Principal component analysis. |
| 2. | Fundamentals of machine vibrations. Low and high frequency vibration measurement methods. | Neural networks in data mining and diagnostics of machinery operation state. | |
| 3. | Vibration sensors. Vibration signal analysis methods. Beating and modulation. | Industrial case examples. | |
| 4. | Rolling and sliding bearing monitoring. Gear transmission monitoring. Centrifugal pumps. | ||
| 5. | Acoustic emission. Oil monitoring methods. General analysis techniques in vibration diagnostics. Fault finding charts and standards. |
Evaluation criteria for the course
Accepted examination and exercises. Exercises are compulsory and they form a part of the final score of the cource. If the course will be used in post graduate studies the minimum score is 3 and an extra exercise must be carried out.
Assessment scale:
Numerical evaluation scale (1-5) will be used on the course
Partial passing:
Study material
| Type | Name | Author | ISBN | URL | Edition, availability, ... | Examination material | Language |
| Book | Introduction to Machinery Analysis and Monitoring, second edition | Mitchell, J.S. | 0-87814-401-3 | PennWell Publishing Co., Tusla, 1993, USA | English | ||
| Book | Rotating machinery vibration. From analysis to troubleshooting | Maurice L. Adams, JR | 978-1-4398-0717-0 | CRC Press Taylor & Francis Group Boca Raton, London, New York | English | ||
| Lecture slides | Machinery diagnostics | Juha Miettinen | Lecture slides. | English | |||
| Summary of lectures | Koneistojen diagnostiikka | Juha Miettinen | Sisältää myös luentokalvot. | Suomi |
Prerequisite relations (Requires logging in to POP)
Correspondence of content
| Course | Corresponds course | Description |
|
|
|
|
|
|
|
More precise information per implementation
| Implementation | Description | Methods of instruction | Implementation |
| When passing the source the student knows principles of most common condition monitoring methods and principles of determining the running situation and principles of fault diagnostics. The student can identify normal and abnormal phenomena of different machines based on measurement results. He can choose suitable vibration monitoring method for typical industrial machinery and he can analyse measurement results. The student can combine different measurement data and machine data for defining the running state of a machine or carry out fault diagnosis. |