A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis

• URL: http://arxiv.org/abs/2203.15275v1
• Date: Tue, 29 Mar 2022 06:43:30 GMT
• Title: A Multi-size Kernel based Adaptive Convolutional Neural Network for Bearing Fault Diagnosis
• Authors: Guangwei Yu, Gang Li, Xingtong Si, and Zhuoyuan Song
• Abstract summary: 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.
• Score: 5.811146610419976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bearing fault identification and analysis is an important research area in the field of machinery fault diagnosis. Aiming at the common faults of rolling bearings, we propose a data-driven diagnostic algorithm based on the characteristics of bearing vibrations called multi-size kernel based adaptive convolutional neural network (MSKACNN). Using raw bearing vibration signals as the inputs, MSKACNN provides vibration feature learning and signal classification capabilities to identify and analyze bearing faults. Ball mixing is a ball bearing production quality problem that is difficult to identify using traditional frequency domain analysis methods since it requires high frequency resolutions of the measurement signals and results in a long analyzing time. The proposed MSKACNN is shown to improve the efficiency and accuracy of ball mixing diagnosis. To further demonstrate the effectiveness of MSKACNN in bearing fault identification, a bearing vibration data acquisition system was developed, and vibration signal acquisition was performed on rolling bearings under five different fault conditions including ball mixing. The resulting datasets were used to analyze the performance of our proposed model. To validate the adaptive ability of MSKACNN, fault test data from the Case Western Reserve University Bearing Data Center were also used. Test results show that MSKACNN can identify the different bearing conditions with high accuracy with high generalization ability. We presented an implementation of the MSKACNN as a lightweight module for a real-time bearing fault diagnosis system that is suitable for production.

Related papers

• Machine Learning for Pre/Post Flight UAV Rotor Defect Detection Using Vibration Analysis [54.550658461477106]
  Unmanned Aerial Vehicles (UAVs) will be critical infrastructural components of future smart cities. In order to operate efficiently, UAV reliability must be ensured by constant monitoring for faults and failures. This paper leverages signal processing and Machine Learning methods to analyze the data of a comprehensive vibrational analysis to determine the presence of rotor blade defects.
  arXiv  Detail & Related papers  (2024-04-24T13:50:27Z)
• 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)
• Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network [3.673613706096849]
  This paper presents an anomaly detection method based on Deep Convolutional Auto-encoding Neural Networks (DCAN) for addressing the issue of ball mill bearing fault detection. The proposed approach leverages vibration data collected during normal operation for training, overcoming challenges such as labeling issues and data imbalance often encountered in supervised learning methods. The paper describes the practical deployment of the DCAN-based anomaly detection model for bearing fault detection, utilizing data from the ball mill bearings of Wuhan Iron & Steel Resources Group and fault data from NASA's bearing vibration dataset.
  arXiv  Detail & Related papers  (2023-11-09T17:49:07Z)
• 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)
• The role of noise in denoising models for anomaly detection in medical images [62.0532151156057]
  Pathological brain lesions exhibit diverse appearance in brain images. Unsupervised anomaly detection approaches have been proposed using only normal data for training. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes.
  arXiv  Detail & Related papers  (2023-01-19T21:39:38Z)
• Logistic-ELM: A Novel Fault Diagnosis Method for Rolling Bearings [0.7409922717686698]
  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.
  arXiv  Detail & Related papers  (2022-04-23T03:11:02Z)
• Investigation of Different Calibration Methods for Deep Speaker Embedding based Verification Systems [66.61691401921296]
  This paper presents an investigation over several methods of score calibration for deep speaker embedding extractors. An additional focus of this research is to estimate the impact of score normalization on the calibration performance of the system.
  arXiv  Detail & Related papers  (2022-03-28T21:22:22Z)
• 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)
• On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine [54.650189434544146]
  A data-driven fault diagnosis and isolation scheme is explicitly developed for failure in the fuel supply system and sensor measurements. A model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.
  arXiv  Detail & Related papers  (2021-10-17T13:42:37Z)
• FaultNet: A Deep Convolutional Neural Network for bearing fault classification [7.148679715851955]
  We analyze vibration signal data of mechanical systems with bearings by combining different signal processing methods. We propose a convolutional neural network FaultNet which can effectively determine the type of bearing fault with a high degree of accuracy.
  arXiv  Detail & Related papers  (2020-10-05T16:50:08Z)

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.