Predictive Maintenance of Turbofan Engine

Using time series data and asking RNN ‘When does the next fault occur ?’

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Predictive maintenance is very important for manufacturers as well as the maintainers, which lowers maintenance cost, extend equipment life, reduce downtime and improve production quality by addressing problems before they cause equipment failures.

“Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed” — source Wikipedia

_In this post, I would like to demonstrate that the use of __RNN(Recurrent Neural Network)/LSTM(Long Short Term Memory) __architecture is not only __more accurate but it performs better in classifying the results accurately __when compared to the previous __CNN (Convolution Neural Network) _approach, written by Marco Cerliani (read here).


This post uses the C-MAPSS datasetfor the predictive maintenance of the Turbofan Engine. Here the challenge is to determine the **Remaining Useful Life (RUL) **until next fault that occur in the engine.

The dataset can be found (here), here’s a brief on the dataset,

“The engine is operating normally at the start of each time series, and develops a fault at some point during the series.

In the training set, the fault grows in magnitude until system failure.

In the test set, the time series ends some time prior to system failure.

The following are the conditions of the engine that are used in the training of the model

Train trjectories: 100

Test trajectories: 100

Conditions: ONE (Sea Level)

Fault Modes: ONE (HPC Degradation)

Understanding the Dataset

Once we load the dataset, we obtain the time series data of 100 engines that contains the operational settings and sensor readings of each 100 engines with different senarios where the fault occurs and a total of 20631 training examples. To illustrate, below are the first 5 training examples of our training dataset.


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Fig 1 : Training Data

To further understand the data given, (see Fig 2) describes that for a given engine how many cycles are left before the next fault occurs.

Example 1 : Engine id number 69 (farthest left) approximately has 360 cycles remaining before fault.

Example 2 : Engine id number 39 (farthest right) approximately has 110 cycles remaining before fault.

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Fig 2: Engine with their respective Remaining Useful Cycles until fault

The following (Fig3 and Fig 4) are time series data for engine whose id is 69,

engine_id = train_df[train_df['id'] == 69]

ax1 = engine_id[train_df.columns[2:]].plot(subplots=True, sharex=True, figsize=(20,30))

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Fig 3: Time series readings of Operational setting 1,2 and 3 and sensors reading of s2 up until s7

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Fig 4: Time series readings for the sensors s8 up until s20

*The images (fig2, fig 3 and fig4) are obtained by using the source code from GitHub Notebook (here), by Marco Cerliani.

#predictive-analytics #deep-learning #deep learning

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Predictive Maintenance of Turbofan Engine
Rahim Makhani

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Wanda  Huel

Wanda Huel


Time series analysis for predictive maintenance of turbofan engines

<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.>

Welcome to another installment of the ‘Exploring NASA’s turbofan dataset’ series. This will be the third analysis on FD001, where all engines run on the same operating condition and develop the same fault.

Initially we assumed the Remaining Useful Life (RUL) of the engines to decline linearly. In my last post we re-examined this assumption by clipping any values above 125. Clipping the RUL improved the baseline linear regression by 31% (from an RMSE of 31.95 to an RMSE of 21.90). We then switched to a Support Vector Regression and squeezed out another 6% improvement for a total RMSE of 20.54.

Today, we’ll focus on time series analysis to forecast when the engines are due for maintenance. But, before getting into the time series part, we first have to recap a few processing steps. Let’s get started!

Loading data

First, we’ll import some libraries and read the data.

import numpy as np
	import pandas as pd
	import matplotlib.pyplot as plt

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

	%matplotlib inline

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

#machine-learning #predictive-maintenance #timeseries #data-science #exploring-nasa-turbofan

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