Wb-az/time-series-anomaly-detection-bilstm-pycaret
Unsupervised anomaly detection in vibration signal using PyCaret vs BiLSTM
This project helps operations engineers and maintenance teams identify abnormal patterns in vibration sensor data from industrial machinery, like bearings. It takes raw vibration time-series data and pinpoints when and where anomalies occur, enabling proactive maintenance and preventing costly equipment failures. The output is an indication of potential issues, allowing users to investigate specific data points or timeframes for early fault detection.
Use this if you need to automatically detect unusual behaviors in machine vibration sensor data for predictive maintenance, especially when historical anomaly labels are scarce.
Not ideal if your anomaly detection needs are outside of industrial vibration data or if you require an approach specifically for anomaly forecasting rather than detection.
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15
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2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Mar 12, 2026
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