# Bachelor seminar Predictive Maintenance

##### every 


##### (in German: Bachelorseminar Predictive Maintenance )

<table bgcolor="#7FB3D9" border="1" id="bkmrk-%5Btitle%5D" style="width: 100%; border-collapse: collapse; border-width: 1px; border-style: none;"><colgroup><col style="width: 100%;"></col></colgroup><tbody><tr style="background-color: rgb(235, 243, 249);"><td>##### <span style="color: rgb(0, 104, 180);">**Module-ID:** </span><span style="color: rgb(0, 104, 180);">**FIN-INF-110524** </span>

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<table bgcolor="#7FB3D9" border="1" id="bkmrk-" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 13.061%;"></col><col style="width: 15.442%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col><col style="width: 14.2515%;"></col></colgroup><tbody><tr><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Abbreviation**</span>

<span style="color: rgb(61, 61, 61);">PREDMA\_BSEM </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Credit Points**</span>

<span style="color: rgb(61, 61, 61);">5 </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Semester**</span>

<span style="color: rgb(61, 61, 61);">every </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Term**</span>

<span style="color: #3d3d3d;">ab 3. </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);">**<span style="color: rgb(61, 61, 61);">Duration</span>**

<span style="color: rgb(61, 61, 61);">1 Semester </span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Language**</span>

<span style="color: rgb(61, 61, 61);">english</span>

</td><td class="align-center" style="border: 5px solid rgb(255, 255, 255);"><span style="color: rgb(61, 61, 61);">**Level**</span>

<span style="color: rgb(61, 61, 61);">Bachelor </span>

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<table border="1" id="bkmrk-verantwortung%3A-verwe" style="border-collapse: collapse; width: 100%; height: 124.8px; border-width: 1px; border-style: none;"><colgroup><col style="width: 24.7879%;"></col><col style="width: 75.2121%;"></col></colgroup><tbody><tr style="height: 35.4px;"><td style="height: 35.4px;">**Link:**</td><td style="height: 35.4px;">[ LSF](https://lsf.ovgu.de/qislsf/rds?state=wsearchv&search=1&subdir=veranstaltung&veranstaltung.semester=20261&veranstaltung.dtxt=Bachelorseminar+Predictive+Maintenance&P_start=0&P_anzahl=30&P.sort=veranstaltung.dtxt&_form=display "Bachelorseminar Predictive Maintenance")</td></tr><tr><td>**Limited accessability:**</td><td>true </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Responsibility:**</td><td style="height: 29.8px;">Prof. Dr. Myra Spiliopoulou, Prof. Dr.-Ing. Benjamin Noack </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Lecturer:**</td><td style="height: 29.8px;">Prof. Dr. Myra Spiliopoulou, Prof. Dr.-Ing. Benjamin Noack </td></tr><tr><td>**Classes:**</td><td>- Seminar *PredMa\_BSeminar*

 </td></tr><tr style="height: 29.8px;"><td style="height: 29.8px;">**Applicability in curriculum:**</td><td style="height: 29.8px;">- B.Sc. INF: Informatik - Wahlpflicht  
- B.Sc. INF: Studienprofil: Künstliche Intelligenz  
- B.Sc. CV: Informatik - Wahlpflicht  
- B.Sc. INGINF: Informatik - Wahlpflicht  
- B.Sc. WIF: Gestalten und Anwenden - Wahlpflicht  
- B.Sc. INF (bilingual): Informatik - Wahlpflicht  
  
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**Intended learning outcomes:** When successfully completing this seminar, the students can:

- read scientific articles given to them on introductory topics of predictive maintenance
- discuss these articles and reflect on them
- compare the methods that appear in these articles and discuss the pros and contra of each method in comparison to other methods
- summarize article contents on predictive maintenance in a report and present them
- formulate questions on these articles and answer questions formulated by other seminar participants

  
**Content:** In this seminar, the participants will learn about

- challenges and methods for data acquisition in industrial processing
- data analysis tools in predictive maintenance
- process modeling, fault detection, and state prediction

**Workload:** 28 h in presence (consultation meetings, presentations) / 96 h self-study for:

- Reading and Understanding of Provided Papers
- Writing an essay
- Presentation and question answering

  
<table border="1" id="bkmrk-studien-%2Fpr%C3%BCfungslei" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 32.0641%;"></col><col style="width: 34.9204%;"></col><col style="width: 33.0155%;"></col></colgroup><tbody><tr><td>**Pre-examination requirements:**</td><td>**Type of examination:**</td><td>**Teaching method / lecture hours per week (SWS):**</td></tr><tr><td></td><td>Referat

</td><td>Seminar (2 SWS)

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<table border="1" id="bkmrk-voraussetzungen-nach" style="font-family: var(--font-body); font-size: 14px; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Prerequisites according to examination regulations:**</td><td>**Recommended prerequisites:**</td></tr><tr><td>none

</td><td>- Linear Algebra and Calculus
- Underpinnings of data analytics
- Underpinnings of signal processing

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<table border="1" id="bkmrk-medienformen%3A-litera" style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 50%;"></col><col style="width: 50%;"></col></colgroup><tbody><tr><td>**Media:**</td><td>**Literature:**</td></tr><tr><td></td><td>Scientific articles on Predictive Maintenance;  
choice of articles depends on topic assignment and is given to each student together with the assignment

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**Comments:**