Remaining useful life models#

BaseRemainingUsefulLifeEstimation#

class ice.remaining_useful_life_estimation.models.base.BaseRemainingUsefulLifeEstimation(window_size: int, stride: int, batch_size: int, lr: float, num_epochs: int, device: str, verbose: bool, name: str, random_seed: int, val_ratio: float, save_checkpoints: bool)[source]#

Bases: BaseModel, ABC

Base class for all RUL models.

Parameters:

  • window_size (int) – The window size to train the model.

  • stride (int) – The time interval between first points of consecutive sliding windows in training.

  • batch_size (int) – The batch size to train the model.

  • lr (float) – The larning rate to train the model.

  • num_epochs (float) – The number of epochs to train the model.

  • device (str) – The name of a device to train the model. cpu and cuda are possible.

  • verbose (bool) – If true, show the progress bar in training.

  • name (str) – The name of the model for artifact storing.

  • random_seed (int) – Seed for random number generation to ensure reproducible results.

  • val_ratio (float) – Proportion of the dataset used for validation, between 0 and 1.

  • save_checkpoints (bool) – If true, store checkpoints.

MLP#

class ice.remaining_useful_life_estimation.models.mlp.MLP(window_size: int = 32, stride: int = 1, hidden_dim: int = 512, batch_size: int = 64, lr: float = 0.0001, num_epochs: int = 15, device: str = 'cpu', verbose: bool = True, name: str = 'mlp_cmapss_rul', random_seed: int = 42, val_ratio: float = 0.15, save_checkpoints: bool = False)[source]#

Bases: BaseRemainingUsefulLifeEstimation

Multilayer Perceptron (MLP) consists of input, hidden, output layers and ReLU activation. Each sample is reshaped to a vector (B, L, C) -> (B, L * C) where B is the batch size, L is the sequence length, C is the number of sensors.

Parameters:

  • window_size (int) – The window size to train the model.

  • stride (int) – The time interval between first points of consecutive sliding windows.

  • hidden_dim (int) – The dimensionality of the hidden layer in MLP.

  • batch_size (int) – The batch size to train the model.

  • lr (float) – The larning rate to train the model.

  • num_epochs (float) – The number of epochs to train the model.

  • device (str) – The name of a device to train the model. cpu and cuda are possible.

  • verbose (bool) – If true, show the progress bar in training.

  • name (str) – The name of the model for artifact storing.

  • random_seed (int) – Seed for random number generation to ensure reproducible results.

  • val_ratio (float) – Proportion of the dataset used for validation, between 0 and 1.

  • save_checkpoints (bool) – If true, store checkpoints.

LSTM#

class ice.remaining_useful_life_estimation.models.lstm.LSTM(window_size: int = 32, stride: int = 1, hidden_dim: int = 512, hidden_size: int = 256, num_layers: int = 2, dropout_value: float = 0.5, batch_size: int = 64, lr: float = 0.0001, num_epochs: int = 35, device: str = 'cpu', verbose: bool = True, name: str = 'mlp_cmapss_rul', random_seed: int = 42, val_ratio: float = 0.15, save_checkpoints: bool = False)[source]#

Bases: BaseRemainingUsefulLifeEstimation

Long short-term memory (LSTM) model consists of the classical LSTM architecture stack and two-layer MLP with SiLU nonlinearity and dropout to make the final prediction.

Each sample is moved to LSTM and reshaped to a vector (B, L, C) -> (B, hidden_size, C) Then the sample is reshaped to a vector (B, hidden_size, C) -> (B, hidden_size * C)

Parameters:

  • window_size (int) – The window size to train the model.

  • stride (int) – The time interval between first points of consecutive sliding windows.

  • hidden_dim (int) – The dimensionality of the hidden layer in MLP.

  • hidden_size (int) – The number of features in the hidden state of the model.

  • num_layers (int) – The number of stacked reccurent layers of the classic LSTM architecture.

  • batch_size (int) – The batch size to train the model.

  • lr (float) – The larning rate to train the model.

  • num_epochs (float) – The number of epochs to train the model.

  • device (str) – The name of a device to train the model. cpu and cuda are possible.

  • verbose (bool) – If true, show the progress bar in training.

  • name (str) – The name of the model for artifact storing.

  • random_seed (int) – Seed for random number generation to ensure reproducible results.

  • val_ratio (float) – Proportion of the dataset used for validation, between 0 and 1.

  • save_checkpoints (bool) – If true, store checkpoints.

IR#

class ice.remaining_useful_life_estimation.models.ir.IR(window_size: int = 32, stride: int = 1, noise: float = 0.012097363825546333, num_layers: int = 3, dropout_value: float = 0.2, batch_size: int = 256, lr: float = 0.004091793998895119, num_epochs: int = 27, device: str = 'cpu', verbose: bool = True, name: str = 'ir_model', random_seed: int = 42, val_ratio: float = 0.15, save_checkpoints: bool = False, dim_feedforward: int = 154)[source]#

Bases: BaseRemainingUsefulLifeEstimation

Long short-term memory (LSTM) model consists of the classical LSTM architecture stack and two-layer MLP with SiLU nonlinearity and dropout to make the final prediction.

Each sample is moved to LSTM and reshaped to a vector (B, L, C) -> (B, hidden_size, C) Then the sample is reshaped to a vector (B, hidden_size, C) -> (B, hidden_size * C)

Parameters:

  • window_size (int) – The window size to train the model.

  • stride (int) – The time interval between first points of consecutive sliding windows.

  • noise (float) – The input noise for model training

  • hidden_dim (int) – The dimensionality of the hidden layer in MLP.

  • hidden_size (int) – The number of features in the hidden state of the model.

  • dropout_value (float) – Dropout probability in model layers

  • num_layers (int) – The number of stacked reccurent layers of the classic LSTM architecture.

  • batch_size (int) – The batch size to train the model.

  • lr (float) – The larning rate to train the model.

  • num_epochs (float) – The number of epochs to train the model.

  • device (str) – The name of a device to train the model. cpu and cuda are possible.

  • verbose (bool) – If true, show the progress bar in training.

  • name (str) – The name of the model for artifact storing.

  • random_seed (int) – Seed for random number generation to ensure reproducible results.

  • val_ratio (float) – Proportion of the dataset used for validation, between 0 and 1.

  • save_checkpoints (bool) – If true, store checkpoints.

  • dim_feedforward (int) – The dimension of feedforward transformer encoder layer