Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings

• URL: http://arxiv.org/abs/2204.11845v1
• Date: Sat, 23 Apr 2022 03:11:02 GMT
• Title: Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings
• Authors: Zhenhua Tan, Jingyu Ning, Kai Peng, Zhenche Xia, and Danke Wu
• Abstract summary: We propose a novel fast fault diagnosis method for rolling bearings, based on extreme learning machine (ELM) and logistic mapping. We conduct extensive experiments on the rolling bearing vibration signal dataset of the Case Western Reserve University (CWRU) Bearing Data Centre. The proposed approach outperforms existing SOTA comparison methods in terms of the predictive accuracy, and the highest accuracy is 100% in seven separate sub data environments.
• Score: 0.7409922717686698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The fault diagnosis of rolling bearings is a critical technique to realize predictive maintenance for mechanical condition monitoring. In real industrial systems, the main challenges for the fault diagnosis of rolling bearings pertain to the accuracy and real-time requirements. Most existing methods focus on ensuring the accuracy, and the real-time requirement is often neglected. In this paper, considering both requirements, we propose a novel fast fault diagnosis method for rolling bearings, based on extreme learning machine (ELM) and logistic mapping, named logistic-ELM. First, we identify 14 kinds of time-domain features from the original vibration signals according to mechanical vibration principles and adopt the sequential forward selection (SFS) strategy to select optimal features from them to ensure the basic predictive accuracy and efficiency. Next, we propose the logistic-ELM for fast fault classification, where the biases in ELM are omitted and the random input weights are replaced by the chaotic logistic mapping sequence which involves a higher uncorrelation to obtain more accurate results with fewer hidden neurons. We conduct extensive experiments on the rolling bearing vibration signal dataset of the Case Western Reserve University (CWRU) Bearing Data Centre. The experimental results show that the proposed approach outperforms existing SOTA comparison methods in terms of the predictive accuracy, and the highest accuracy is 100% in seven separate sub data environments. The relevant code is publicly available at https://github.com/TAN-OpenLab/logistic-ELM.

Related papers

• 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)
• SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models [76.43625653814911]
  Diffusion models have gained popularity for accelerated MRI reconstruction due to their high sample quality. They can effectively serve as rich data priors while incorporating the forward model flexibly at inference time. We introduce SURE-based MRI Reconstruction with Diffusion models (SMRD) to enhance robustness during testing.
  arXiv  Detail & Related papers  (2023-10-03T05:05:35Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• Novel features for the detection of bearing faults in railway vehicles [88.89591720652352]
  We introduce Mel-Frequency Cepstral Coefficients (MFCCs) and features extracted from the Amplitude Modulation Spectrogram (AMS) as features for the detection of bearing faults.
  arXiv  Detail & Related papers  (2023-04-14T10:09:50Z)
• 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)
• Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
  This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
  arXiv  Detail & Related papers  (2022-08-24T07:50:43Z)
• A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis [5.811146610419976]
  We propose a data-driven diagnostic algorithm based on the characteristics of bearing vibrations called multi-size kernel based adaptive convolutional neural network (MSKACNN) MSKACNN provides vibration feature learning and signal classification capabilities to identify and analyze bearing faults.
  arXiv  Detail & Related papers  (2022-03-29T06:43:30Z)
• Intelligent Bearing Fault Diagnosis Method Combining Mixed Input and Hybrid CNN-MLP model [0.0]
  This paper proposes a novel hybrid CNN-MLP model-based diagnostic method which combines mixed input to perform rolling bearing diagnostics. The method successfully detects and localizes bearing defects using acceleration data from a shaft-mounted wireless acceleration sensor.
  arXiv  Detail & Related papers  (2021-12-16T07:26:22Z)
• SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection [63.253850875265115]
  Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples. We propose a modular acceleration system, called SUOD, to address it.
  arXiv  Detail & Related papers  (2020-03-11T00:22:50Z)

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.