softsensor 0.1.0rc1

A general toolbox for the development of Vibrational Soft-Sensors

vibration-soft-sensor

A general toolbox for the development of vibrational softsensors. You can find a lot of functionality for data preprocessing, model training and evaluation for linear and non linear methods. We have also adapt the general approach to a variety of signal types.

The main paper Stabilised Auto-Regressive Neural Networks (S-Arnns) for Data Driven Prediction of Forced Nonlinear Systems can be found on http://dx.doi.org/10.2139/ssrn.4720155.

Table of Contents

• Content
• Maintainers
• License
• 3rd Party Licenses

Content

The vibration-soft-sensor library provides comprehensive tools for analyzing vibration data and building soft sensors. Key components include:

Core Components

• Model Types
  • autoreg_models: Autoregressive models including ARNN
  • recurrent_models: Recurrent neural networks for time series prediction
  • linear_methods: Transfer function and other linear methods
  • model: Base model implementations (LSTM, CNN)
  • arx: Tools for ARX modeling with sliding window approaches

Analysis Tools

• frequency_methods: Frequency domain analysis including FDS (Fatigue Damage Spectrum)
• visualization: Extensive plotting utilities for sensitivity analysis and model evaluation
• eval_tools: Model evaluation and error computation functionalities

Data Handling

• data_gen: Simulated data generation with various excitation signals (sine, sweep, white noise)
• meas_handling: Measurement data preprocessing, including filtering

Advanced Features

• stab_scheduler: Stability scoring and schedulers for neural networks
• losses: Custom loss functions including PSD-based losses
• ensemble_wrappers: Ensemble model implementations (Sync/AsyncEnsemble)
• hyperparameter_optimization: Tools for optimizing model parameters

Sensitivity Analysis

The library offers multiple sensitivity analysis methods:

• Gradient-based methods
• SmoothGrad
• Integrated gradients
• Perturbation analysis
• Uncertainty quantification

Maintainers

Tobias Westmeier

Daniel Kreuter

License

softsensor is open-sourced under the Apache-2.0 license. See the LICENSE file for details.

For a list of other open source components included in pyLife, see the file 3rd-party-licenses.txt.