June 2019
Intermediate to advanced
308 pages
7h 21m
English
Content preview from Hands-On Deep Learning for IoT
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Calculating cut-off times
Let's consider the FD004 dataset, that contains as much as 249 engines (engine_no) monitored over time (time_in_cycles). Each engine has operational_settings and sensor_measurements recorded for each cycle:
data_path = 'train_FD004.txt'data = utils.load_data(data_path)
To train a model that will predict RUL, we can simulate real predictions by choosing a random point in the life of the engine and only using the data from before that point. We can create features with that restriction easily by using cut-off times:
def make_cutoff_times(data): gb = data.groupby(['unit_id']) labels = [] for engine_no_df in gb: instances = engine_no_df[1].shape[0] label = [instances - i - 1 for i in range(instances)] labels += label ...
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