Assessment of hybrid machine learning models for non-linear system identification of fatigue test rigs

• URL: http://arxiv.org/abs/2107.03645v4
• Date: Fri, 6 Oct 2023 08:27:23 GMT
• Title: Assessment of hybrid machine learning models for non-linear system identification of fatigue test rigs
• Authors: Leonhard Heindel, Peter Hantschke and Markus K\"astner
• Abstract summary: The prediction of system responses for a given fatigue test bench drive signal is a challenging task. A novel hybrid model is suggested, which augments existing approaches using Long Short-Term Memory networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The prediction of system responses for a given fatigue test bench drive signal is a challenging task, for which linear frequency response function models are commonly used. To account for non-linear phenomena, a novel hybrid model is suggested, which augments existing approaches using Long Short-Term Memory networks. Additional virtual sensing applications of this method are demonstrated. The approach is tested using non-linear experimental data from a servo-hydraulic test rig and this dataset is made publicly available. A variety of metrics in time and frequency domains, as well as fatigue strength under variable amplitudes, are employed in the evaluation.

Related papers

• A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests [14.37149160708975]
  This paper proposes a novel hybrid framework combiningHierarchical Temporal Memory (HTM) and Sequential Probability Ratio Test (SPRT) for real-time data drift detection and anomaly identification. Experimental evaluations demonstrate that the proposed method outperforms conventional drift detection techniques like the Kolmogorov-Smirnov (KS) test, Wasserstein distance, and Population Stability Index (PSI) in terms of accuracy, adaptability, and computational efficiency.
  arXiv  Detail & Related papers  (2025-04-24T18:23:18Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• Deep Long-Short Term Memory networks: Stability properties and Experimental validation [0.20999222360659603]
  We show that suitable sufficient conditions on the weights of the network can be leveraged to setup a training procedure able to learn provenly-$delta$ISS LSTM models from data. The proposed approach is tested on a real brake-by-wire apparatus to identify a model of the system from input-output experimentally collected data.
  arXiv  Detail & Related papers  (2023-04-06T10:02:17Z)
• Decision Forest Based EMG Signal Classification with Low Volume Dataset Augmented with Random Variance Gaussian Noise [51.76329821186873]
  We produce a model that can classify six different hand gestures with a limited number of samples that generalizes well to a wider audience. We appeal to a set of more elementary methods such as the use of random bounds on a signal, but desire to show the power these methods can carry in an online setting.
  arXiv  Detail & Related papers  (2022-06-29T23:22:18Z)
• Efficient Test-Time Model Adaptation without Forgetting [60.36499845014649]
  Test-time adaptation seeks to tackle potential distribution shifts between training and testing data. We propose an active sample selection criterion to identify reliable and non-redundant samples. We also introduce a Fisher regularizer to constrain important model parameters from drastic changes.
  arXiv  Detail & Related papers  (2022-04-06T06:39:40Z)
• Data-driven detector signal characterization with constrained bottleneck autoencoders [0.0]
  deep learning in the form of constrained bottleneck autoencoders can be used to learn the underlying unknown detector response model directly from data. The trained algorithm can be used simultaneously to perform estimations on the physical parameters of the model, simulate the detector response with high fidelity and to denoise detector signals.
  arXiv  Detail & Related papers  (2022-03-09T09:46:10Z)
• Passive learning to address nonstationarity in virtual flow metering applications [0.0]
  This paper explores how learning methods can be applied to sustain the prediction accuracy of steady-state virtual flow meters. Two passive learning methods, periodic batch learning and online learning, are applied with varying calibration frequency to train virtual flow meters. The results are two-fold: first, in the presence of frequently arriving measurements, frequent model updating sustains an excellent prediction performance over time; second, in the presence of intermittent and infrequently arriving measurements, frequent updating is essential to increase the performance accuracy.
  arXiv  Detail & Related papers  (2022-02-07T14:42:00Z)
• Time-Series Anomaly Detection with Implicit Neural Representation [0.38073142980733]
  Implicit Neural Representation-based Anomaly Detection (INRAD) is proposed. We train a simple multi-layer perceptron that takes time as input and outputs corresponding values at that time. Then we utilize the representation error as an anomaly score for detecting anomalies.
  arXiv  Detail & Related papers  (2022-01-28T06:17:24Z)
• Wave-based extreme deep learning based on non-linear time-Floquet entanglement [0.7614628596146599]
  Complex neuromorphic computing tasks, which require strong non-linearities, have so far remained out-of-reach of wave-based solutions. Here, we demonstrate the relevance of Time-Floquet physics to induce a strong non-linear entanglement between signal inputs at different frequencies. We prove the efficiency of the method for extreme learning machines and reservoir computing to solve a range of challenging learning tasks.
  arXiv  Detail & Related papers  (2021-07-19T00:18:09Z)
• Learning summary features of time series for likelihood free inference [93.08098361687722]
  We present a data-driven strategy for automatically learning summary features from time series data. Our results indicate that learning summary features from data can compete and even outperform LFI methods based on hand-crafted values.
  arXiv  Detail & Related papers  (2020-12-04T19:21:37Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• Automatic Recall Machines: Internal Replay, Continual Learning and the Brain [104.38824285741248]
  Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective. Instead the implicit memory of learned samples within the assessed model itself is exploited.
  arXiv  Detail & Related papers  (2020-06-22T15:07:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.