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# Prediction of Remaining Useful Life (RUL) of JET engine

The main goal of this post is to detail my development of a model for doing predictive maintenance on commercial turbofan engines. The predictive maintenance method utilized here is a data-driven method, which means that data from the operating jet engine is used to simulate predictive maintenance.

The project’s goal is to develop a prediction model for estimating a jet engine’s Remaining Useful Life ( RUL) based on run-to-failure data from a fleet of comparable jet engines.

Overview of the data set

The Prognostics and Health Management PHM08 Challenge Data Set was developed by NASA and is now available to the public. The data collection is used to forecast jet engine problems over time. The Prognostics CoE at NASA Ames contributed the data set.

The data set for the jet engine comprises time-series measurements of different pressures, temperatures, and rotating equipment speeds. In a commercial contemporary turbofan engine, these metrics are routinely taken. Although all engines are of the same kind, each one begins with varying degrees of early wear and manufacturing process differences that are undisclosed to the user. Each machine has three alternative settings that may be utilised to alter its performance. Each engine contains 21 sensors that gather data about the engine’s status while it’s running.

Let’s make time series plots for two units, so we can get a sense of the data.

We can see that the different unit numbers have a different number of total cycles before they fail. Let’s look at this more closely:

`count 100.000000`

mean 206.310000

std 46.342749

min 128.000000

25% 177.000000

50% 199.000000

75% 229.250000

max 362.000000

Name: time_in_cycles, dtype: float64

From this we can infer the the remaining useful life (RUL) of each unit in the training data is `time_in_cycles.max()`

minus the current time in cycles, for any given cycle. Let’s add that as our dependent variable. (For the testing data, the RUL is in a separate file.)

We can check to make sure the RUL looks like we would expect:

Model Construction & Training

Model: "sequential_3"

_________________________________________________________________

Layer (type) Output Shape Param #

=================================================================

normalization_3 (Normalizati (None, None, 14) 29

_________________________________________________________________

lstm_3 (LSTM) (None, 32) 6016

_________________________________________________________________

dense_3 (Dense) (None, 1) 33

_________________________________________________________________

lambda_3 (Lambda) (None, 1) 0

=================================================================

Total params: 6,078

Trainable params: 6,049

Non-trainable params: 29Epoch 1/499

10/10 [==============================] - 0s 6ms/step - loss: 10796.3320 - root_mean_squared_error: 103.9054

Epoch 2/499

10/10 [==============================] - 0s 6ms/step - loss: 7954.4033 - root_mean_squared_error: 89.1875

Epoch 3/499

10/10 [==============================] - 0s 6ms/step - loss: 6400.0864 - root_mean_squared_error: 80.0005

Epoch 4/499

10/10 [==============================] - 0s 4ms/step - loss: 4885.1406 - root_mean_squared_error: 69.8938

Epoch 5/499

10/10 [==============================] - 0s 4ms/step - loss: 4010.2659 - root_mean_squared_error: 63.3267

Epoch 6/499

10/10 [==============================] - 0s 4ms/step - loss: 3276.9802 - root_mean_squared_error: 57.2449

Epoch 7/499

10/10 [==============================] - 0s 4ms/step - loss: 3066.4707 - root_mean_squared_error: 55.3757

Epoch 8/499

10/10 [==============================] - 0s 4ms/step - loss: 2769.1394 - root_mean_squared_error: 52.6226

Epoch 9/499

10/10 [==============================] - 0s 4ms/step - loss: 2351.2549 - root_mean_squared_error: 48.4897

Epoch 10/499

10/10 [==============================] - 0s 4ms/step - loss: 2101.9841 - root_mean_squared_error: 45.8474

Epoch 11/499

10/10 [==============================] - 0s 4ms/step - loss: 1967.6283 - root_mean_squared_error: 44.3580

Epoch 12/499

10/10 [==============================] - 0s 4ms/step - loss: 2045.3716 - root_mean_squared_error: 45.2258

Epoch 13/499

10/10 [==============================] - 0s 4ms/step - loss: 1932.9287 - root_mean_squared_error: 43.9651

Epoch 14/499

10/10 [==============================] - 0s 4ms/step - loss: 1792.9589 - root_mean_squared_error: 42.3433

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Epoch 491/499

10/10 [==============================] - 0s 4ms/step - loss: 62.6952 - root_mean_squared_error: 7.9180

Epoch 492/499

10/10 [==============================] - 0s 4ms/step - loss: 58.7008 - root_mean_squared_error: 7.6616

Epoch 493/499

10/10 [==============================] - 0s 4ms/step - loss: 48.0873 - root_mean_squared_error: 6.9345

Epoch 494/499

10/10 [==============================] - 0s 4ms/step - loss: 78.2315 - root_mean_squared_error: 8.8449

Epoch 495/499

10/10 [==============================] - 0s 4ms/step - loss: 84.7828 - root_mean_squared_error: 9.2078

Epoch 496/499

10/10 [==============================] - 0s 4ms/step - loss: 64.7266 - root_mean_squared_error: 8.0453

Epoch 497/499

10/10 [==============================] - 0s 4ms/step - loss: 68.3690 - root_mean_squared_error: 8.2686

Epoch 498/499

10/10 [==============================] - 0s 4ms/step - loss: 61.5877 - root_mean_squared_error: 7.8478

Epoch 499/499

10/10 [==============================] - 0s 4ms/step - loss: 53.9489 - root_mean_squared_error: 7.3450

Evaluation

Conclusion

The model created is able to predict RUL with RMSE ~7.

Platform : cAInvas

Code: Here

Written By: Dheeraj Perumandla

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