Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluation

• URL: http://arxiv.org/abs/2207.10732v1
• Date: Thu, 21 Jul 2022 19:57:36 GMT
• Title: Explainable AI Algorithms for Vibration Data-based Fault Detection: Use Case-adadpted Methods and Critical Evaluation
• Authors: Oliver Mey and Deniz Neufeld
• Abstract summary: Analyzing vibration data using deep neural network algorithms is an effective way to detect damages in rotating machinery at an early stage. This work investigates the application of explainable AI (XAI) algorithms to convolutional neural networks for vibration-based condition monitoring.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Analyzing vibration data using deep neural network algorithms is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution because the cause of classifications is not comprehensible to humans. Therefore, this work investigates the application of explainable AI (XAI) algorithms to convolutional neural networks for vibration-based condition monitoring. For this, various XAI algorithms are applied to classifications based on the Fourier transform as well as the order analysis of the vibration signal. The results are visualized as a function of the revolutions per minute (RPM), in the shape of frequency-RPM maps and order-RPM maps. This allows to assess the saliency given to features which depend on the rotation speed and those with constant frequency. To compare the explanatory power of the XAI methods, investigations are first carried out with a synthetic data set with known class-specific characteristics. Then a real-world data set for vibration-based imbalance classification on an electric motor, which runs at a broad range of rotation speeds, is used. A special focus is put on the consistency for variable periodicity of the data, which translates to a varying rotation speed of a real-world machine. This work aims to show the different strengths and weaknesses of the methods for this use case: GradCAM, LRP and LIME with a new perturbation strategy.

Related papers

• Learning to Predict Structural Vibrations [2.4746345679821617]
  In mechanical structures like airplanes, cars and houses, noise is generated and transmitted through vibrations. To take measures to reduce this noise, vibrations need to be simulated with expensive numerical computations. We present a benchmark on the task of predicting the vibration of harmonically excited plates.
  arXiv  Detail & Related papers  (2023-10-09T07:26:35Z)
• ExoplANNET: A deep learning algorithm to detect and identify planetary signals in radial velocity data [0.0]
  A neural network is proposed to replace the computation of the significance of the signal detected with the radial velocity method. The algorithm is trained using synthetic data of systems with and without planetary companions. It achieves 28 % fewer false positives and execution time is five orders of magnitude faster than the traditional method.
  arXiv  Detail & Related papers  (2023-03-16T14:16:19Z)
• Optimal Algorithms for the Inhomogeneous Spiked Wigner Model [89.1371983413931]
  We derive an approximate message-passing algorithm (AMP) for the inhomogeneous problem. We identify in particular the existence of a statistical-to-computational gap where known algorithms require a signal-to-noise ratio bigger than the information-theoretic threshold to perform better than random.
  arXiv  Detail & Related papers  (2023-02-13T19:57:17Z)
• A Machine Learning approach for correcting radial velocities using physical observables [0.0]
  Using simulations and real data, we investigate the capabilities of a Deep Neural Network approach to produce activity free Doppler measurements of stars. We demonstrate that the RV-independent approach can drastically reduce spurious Doppler variability from known physical effects. We observe a good match of the correction with the observed variability, but we also find that the noise reduction is not as good as in the simulations.
  arXiv  Detail & Related papers  (2023-01-30T13:25:00Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge [110.7678032481059]
  We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings.
  arXiv  Detail & Related papers  (2022-09-22T16:44:59Z)
• 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)
• Simpler is better: spectral regularization and up-sampling techniques for variational autoencoders [1.2234742322758418]
  characterization of the spectral behavior of generative models based on neural networks remains an open issue. Recent research has focused heavily on generative adversarial networks and the high-frequency discrepancies between real and generated images. We propose a simple 2D Fourier transform-based spectral regularization loss for the Variational Autoencoders (VAEs)
  arXiv  Detail & Related papers  (2022-01-19T11:49:57Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Autoencoder-based Condition Monitoring and Anomaly Detection Method for Rotating Machines [0.19116784879310028]
  We propose an autoencoder model-based method for condition monitoring of rotating machines by using an anomaly detection approach. The proposed method can directly extract the salient features from raw vibration signals. Experimental results on two real-world datasets indicate that our proposed solution gives promising results.
  arXiv  Detail & Related papers  (2021-01-27T16:49:49Z)
• Detection of gravitational-wave signals from binary neutron star mergers using machine learning [52.77024349608834]
  We introduce a novel neural-network based machine learning algorithm that uses time series strain data from gravitational-wave detectors. We find an improvement by a factor of 6 in sensitivity to signals with signal-to-noise ratio below 25. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert.
  arXiv  Detail & Related papers  (2020-06-02T10:20:11Z)

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.