14: Industrie 4.0, Vorlesung, WS 2018/19, 01.02.2019
Author
Editor
Participating institute
Institut für Anthropomatik und Robotik (IAR)
Genre
Description
- 0:00:00 Start
- 0:01:24 Syllabus - Winter Term 2018/19
- 0:01:50 Variants of Anomaly Detection Problem
- 0:03:24 Unsupervised Anomaly Detection
- 0:04:34 Statistical Outlier Detection
- 0:07:21 Classification Based Techniques
- 0:17:31 Manipulating Data Records
- 0:21:01 Predictive Maintenance Example in MATLAB
- 0:22:33 Predicitive Maintance Done by Humans
- 0:23:44 References for Today's Class
- 0:24:05 Predictive Maintenance Software
- 0:25:46 Where to Find Data Analyze? (and Play with)
- 0:27:34 Recap Resources
- 0:28:01 Rolling Element Bearing Fault Diagnosis
- 0:28:46 Ball Pass Frequency
- 0:29:48 Rolling Element Bearing Fault Diagnosis
- 0:30:04 MFPT Data Set
- 0:30:28 Inner Race Fault Data
- 0:30:50 Time Domain - Closer Lock
- 0:31:08 Single Envelope Spectrum Analysis
- 0:31:46 MFPT Data Set
- 0:38:46 MATLAB Examples
Duration (hh:mm:ss)
01:06:07
Series
Industrie 4.0, Vorlesung, WS 2018/19
Published on
04.02.2019
Subject area
License
| Resolution | 1280 x 720 Pixel |
| Aspect ratio | 16:9 |
| Audio bitrate | 128000 bps |
| Audio channels | 2 |
| Audio Codec | aac |
| Audio Sample Rate | 48000 Hz |
| Total Bitrate | 934309 bps |
| Color Space | yuv420p |
| Container | mov,mp4,m4a,3gp,3g2,mj2 |
| Media Type | video/mp4 |
| Duration | 3967 s |
| Filename | DIVA-2019-118_hd.mp4 |
| File Size | 463.269.505 byte |
| Frame Rate | 25 |
| Video Bitrate | 800221 bps |
| Video Codec | h264 |
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Industrie 4.0, Vorlesung, WS 2018/19
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