Fault Detection, Isolation and Recovery (FDIR)
An IRONHACK/Berlin Machine Learning project by Andres Lucht (github.com/andres2203) and Georgios Papadopoulos (github.com/GeorgiosKP).
Data Source: Condition monitoring of hydraulic systems Data Set
Examination and evaluation of sensor related data of a hydraulic testing rig. Prediction 4 different target values for stable situation/fault detection.
\data: sensor data
\descriptions: Further descriptions
\experimental: experimental notebooks (see experimental)
merged_df.pkl: data file
01-load_and_examine_data.ipynb
- Loading, processing and examination of data
02-regression.ipynb
- Regression models to analyze system dependencies
03-machine-learning.ipynb
- ml part (see applied methods)
- Cross validation on train set
- Choose best performing algorithm, applied methods
- LinearRegression
- LogisticRegression
- RandomForest
- DecisionTree
- KNearestNeighbors
- Feature elimination (RFECV)
- Evaluation on test set
experimental
- Clustering
- PCA
- LDA
- Analysis
- FFT