Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions

• URL: http://arxiv.org/abs/2311.18547v1
• Date: Thu, 30 Nov 2023 13:30:00 GMT
• Title: Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions
• Authors: Tuomas Jalonen, Mohammad Al-Sa'd, Serkan Kiranyaz, and Moncef Gabbouj
• Abstract summary: This paper presents an efficient real-time convolutional neural network (CNN) for diagnosing multiple bearing faults. We conducted experiments on both healthy bearings and bearings afflicted with inner race, outer race, and roller ball faults.
• Score: 15.613528945524791
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures. However, many existing techniques are developed and tested under strictly controlled conditions, limiting their adaptability to the diverse and dynamic settings encountered in practical applications. This paper presents an efficient real-time convolutional neural network (CNN) for diagnosing multiple bearing faults under various noise levels and time-varying rotational speeds. Additionally, we propose a novel Fisher-based spectral separability analysis (SSA) method to elucidate the effectiveness of the designed CNN model. We conducted experiments on both healthy bearings and bearings afflicted with inner race, outer race, and roller ball faults. The experimental results show the superiority of our model over the current state-of-the-art approach in three folds: it achieves substantial accuracy gains of up to 15.8%, it is robust to noise with high performance across various signal-to-noise ratios, and it runs in real-time with processing durations five times less than acquisition. Additionally, by using the proposed SSA technique, we offer insights into the model's performance and underscore its effectiveness in tackling real-world challenges.

Related papers

• Towards Robust Transcription: Exploring Noise Injection Strategies for Training Data Augmentation [55.752737615873464]
  This study investigates the impact of white noise at various Signal-to-Noise Ratio (SNR) levels on state-of-the-art APT models. We hope this research provides valuable insights as preliminary work toward developing transcription models that maintain consistent performance across a range of acoustic conditions.
  arXiv  Detail & Related papers  (2024-10-18T02:31:36Z)
• DOCTOR: Dynamic On-Chip Temporal Variation Remediation Toward Self-Corrected Photonic Tensor Accelerators [5.873308516576125]
  Photonic tensor accelerators offer unparalleled speed and energy efficiency. Off-chip noise-aware training and on-chip training have been proposed to enhance the variation tolerance of optical neural accelerators. We propose a lightweight dynamic on-chip framework, dubbed DOCTOR, providing adaptive, in-situ accuracy recovery against temporally drifting noise.
  arXiv  Detail & Related papers  (2024-03-05T06:17:13Z)
• Quadratic Time-Frequency Analysis of Vibration Signals for Diagnosing Bearing Faults [15.613528945524791]
  This paper presents a fusion of time-frequency analysis and deep learning techniques to diagnose bearing faults under varying noise levels. We design a time-frequency convolutional neural network (TF-CNN) to diagnose various faults in rolling-element bearings.
  arXiv  Detail & Related papers  (2024-01-02T12:02:50Z)
• Not All Steps are Equal: Efficient Generation with Progressive Diffusion Models [62.155612146799314]
  We propose a novel two-stage training strategy termed Step-Adaptive Training. In the initial stage, a base denoising model is trained to encompass all timesteps. We partition the timesteps into distinct groups, fine-tuning the model within each group to achieve specialized denoising capabilities.
  arXiv  Detail & Related papers  (2023-12-20T03:32:58Z)
• A Novel Transfer Learning Method Utilizing Acoustic and Vibration Signals for Rotating Machinery Fault Diagnosis [12.631120583797518]
  Fault diagnosis of rotating machinery plays a important role for the safety and stability of modern industrial systems. There is a distribution discrepancy between training data and data of real-world operation scenarios. This paper proposed a transfer learning based method utilizing acoustic and vibration signal to address this distribution discrepancy.
  arXiv  Detail & Related papers  (2023-10-20T10:50:14Z)
• A Closer Look at Bearing Fault Classification Approaches [1.9531938396288886]
  Rolling bearing fault diagnosis has garnered increased attention in recent years. Recent technological advances have enabled monitoring the health of these assets at scale. Rolling bearing fault diagnosis has garnered increased attention in recent years.
  arXiv  Detail & Related papers  (2023-09-29T06:11:11Z)
• Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative Study [71.84852429039881]
  Investigation focuses on the models' ability to handle a range of perturbations, such as sensor faults and noise. We test the generalization and transfer learning capabilities of these models by exposing them to out-of-distribution (OOD) samples.
  arXiv  Detail & Related papers  (2023-06-13T12:43:59Z)
• Machine learning enabled experimental design and parameter estimation for ultrafast spin dynamics [54.172707311728885]
  We introduce a methodology that combines machine learning with Bayesian optimal experimental design (BOED) Our method employs a neural network model for large-scale spin dynamics simulations for precise distribution and utility calculations in BOED. Our numerical benchmarks demonstrate the superior performance of our method in guiding XPFS experiments, predicting model parameters, and yielding more informative measurements within limited experimental time.
  arXiv  Detail & Related papers  (2023-06-03T06:19:20Z)
• Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization [58.720142291102135]
  Recent has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of chaotic dynamical systems. In the absence of mitigating techniques, this technique can result in artificially rapid error growth, leading to inaccurate predictions and/or climate instability. We introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training.
  arXiv  Detail & Related papers  (2022-11-09T23:40:52Z)
• Robust Ultra-wideband Range Error Mitigation with Deep Learning at the Edge [0.0]
  Multipath effects, reflections, refractions, and complexity of the indoor radio environment can introduce a positive bias in the ranging measurement. This article proposes an efficient representation learning methodology that exploits the latest advancement in deep learning and graph optimization techniques. Channel Impulse Response (CIR) signals are directly exploited to extract high semantic features to estimate corrections in either NLoS or LoS conditions.
  arXiv  Detail & Related papers  (2020-11-30T10:52:21Z)
• Training Generative Adversarial Networks by Solving Ordinary Differential Equations [54.23691425062034]
  We study the continuous-time dynamics induced by GAN training. From this perspective, we hypothesise that instabilities in training GANs arise from the integration error. We experimentally verify that well-known ODE solvers (such as Runge-Kutta) can stabilise training.
  arXiv  Detail & Related papers  (2020-10-28T15:23:49Z)

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.