Manivannan / Industry 4.0 - PredictiveMaintenance - Fan
This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.
About this project
DataSet: This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII dataset). It contains the sounds generated from four types of industrial machines, i.e. valves, pumps, fans, and slide rails. Each type of machine includes seven individual product models*1, and the data for each model contains normal sounds (from 5000 seconds to 10000 seconds) and anomalous sounds (about 1000 seconds). To resemble a real-life scenario, various anomalous sounds were recorded (e.g., contamination, leakage, rotating unbalance, and rail damage). Also, the background noise recorded in multiple real factories was mixed with the machine sounds. The sounds were recorded by eight-channel microphone array with 16 kHz sampling rate and 16 bit per sample. The MIMII dataset assists benchmark for sound-based machine fault diagnosis. Users can test the performance for specific functions e.g., unsupervised anomaly detection, transfer learning, noise robustness, etc. The detail of the dataset is described in [1][2].
[1] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” arXiv preprint arXiv:1909.09347, 2019.
[2] Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019.
Citation : Purohit, Harsh, Tanabe, Ryo, Ichige, Kenji, Endo, Takashi, Nikaido, Yuki, Suefusa, Kaori, & Kawaguchi, Yohei. (2019). MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (public 1.0) [Data set]. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019 Workshop), New York, USA. Zenodo. https://doi.org/10.5281/zenodo.3384388
Hackster Project : https://www.hackster.io/manivannan/industry-4-0-predictive-maintenance-3bb415
YouTube tutorial : https://www.youtube.com/watch?v=GKu3tVDuA70
Contact : manivannan0212@gmail.com
Creating your first impulse (100% complete)
Acquire data
Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.
Design an impulse
Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.
Deploy
Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.
Download block output
| Title | Type | Size | |
|---|---|---|---|
| Spectrogram training data | NPY file | 3087 windows | |
| Spectrogram training labels | NPY file | 3087 windows | |
| Spectrogram testing data | NPY file | 756 windows | |
| Spectrogram testing labels | NPY file | 756 windows | |
| NN Classifier model | TensorFlow Lite (float32) | 75 KB | |
| NN Classifier model | TensorFlow Lite (int8 quantized) | 23 KB | |
| NN Classifier model | TensorFlow SavedModel | 92 KB | |
| NN Classifier model | Keras h5 model | 70 KB |
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Summary
Data collected
1h 11m 10sProject info
| Project ID | 103735 |
| Project version | 3 |
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