The importance of problem framing for supervised predictive maintenance solutions

The importance of problem framing for supervised predictive maintenance solutions

Revisiting our assumption of Remaining Useful Life & Support Vector Regression. Today we’ll re-examine our assumption of RUL to improve our accuracy and fit a Support Vector Regression (SVR) in an attempt to further improve upon our results.

<disclaimer: I aim to showcase the effect of different methods and choices made during model development. These effects are often shown using the test set, something which is considered (very) bad practice but helps for educational purposes.>

In my last post we explored NASA’s FD001 turbofan degradation dataset. To quickly recap, sensors 1, 5, 6, 10, 16, 18 and 19 held no information related to Remaining Useful Life (RUL). After removing these from the data we fitted a baseline Linear Regression model with an RMSE of 31.95. Today we’ll re-examine our assumption of RUL to improve our accuracy and fit a Support Vector Regression (SVR) in an attempt to further improve upon our results. Let’s get started!

Loading data

First, we’ll load the data and inspect the first few rows to confirm it loaded correctly.

%matplotlib inline
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import seaborn as sns; sns.set()

    from sklearn.linear_model import LinearRegression
    from sklearn.svm import SVR
    from sklearn.metrics import mean_squared_error, r2_score

define filepath to read data

dir_path = './CMAPSSData/'

## define column names for easy indexing
index_names = ['unit_nr', 'time_cycles']
setting_names = ['setting_1', 'setting_2', 'setting_3']
sensor_names = ['s_{}'.format(i) for i in range(1,22)] 
col_names = index_names + setting_names + sensor_names

## read data
train = pd.read_csv((dir_path+'train_FD001.txt'), sep='\s+', header=None, names=col_names)
test = pd.read_csv((dir_path+'test_FD001.txt'), sep='\s+', header=None, names=col_names)
y_test = pd.read_csv((dir_path+'RUL_FD001.txt'), sep='\s+', header=None, names=['RUL'])

## inspect first few rows
train.head()

```

exploring-nasa-turbofan data-science problem-framing machine-learning

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