Jenny Plunkett / Fan Monitoring - Advanced Anomaly Detection Public

Jenny Plunkett / Fan Monitoring - Advanced Anomaly Detection

This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.

About this project

This project, fan monitoring using an embedded device's onboard accelerometer, was created to showcase Edge Impulse's Advanced Anomaly Detection Feature Importance addition. Check out the full blog post!

Advanced Anomaly Detection & Feature Importance

Select automatically suggested features by importance for your industrial sensor data by using your custom digital signal processing block or one of the default DSP blocks provided in Edge Impulse, allowing you to create even more accurate anomaly detection models for new and unseen data based on the most important features of your model's training data.

For example, collect training data for a fan vibration monitoring system where your fan is continuously performing nominally. It would be costly and impractical for an industrial-sized fan to purposefully break or get obstructed only to collect training/testing data for a model to detect future fan failure. Instead, collect data of the fan vibrations where the machine operates nominally and when powered off in normal conditions. Upload these frequency and time-based samples into your Edge Impulse project, add a Spectral Analysis DSP block, or your custom DSP code. Generate the features based on your uploaded training data with this code/block. Edge Impulse Studio will then output a Feature Importance graphic that will help you determine which axes and values generated from your DSP block are most significant to analyze when you want to do anomaly detection.

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.


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
Spectral features training data NPY file 350 windows
Spectral features training labels NPY file 350 windows
Spectral features testing data NPY file 560 windows
Spectral features testing labels NPY file 560 windows
NN Classifier model TensorFlow Lite (float32) 8 KB
NN Classifier model TensorFlow Lite (int8 quantized) 4 KB
NN Classifier model TensorFlow SavedModel 13 KB
NN Classifier model Keras h5 model 8 KB
Anomaly detection model JSON 8 KB

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Data collected
9m 6s

Project info

Project ID 111788
Project version 5