A Novel Bearing Fault Diagnosis of Raw Signals Based on 1D Residual Convolution Neural Network

Li, Chuanjiang; Yu, Liya; Zhang, Ansi; He, Qiang; Yang, Wanli; Duan, Zhongjing

doi:10.1109/hpbdis49115.2020.9130567

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Based on the way the vibration signals are employed, the existing methods for bearing IFD can be roughly classified into methods based on one-dimensional (1D) inputs and two-dimensional (2D) inputs. The first type of method tends to use raw vibration signals as input, which takes advantage of the temporal characteristics of vibration signals as a typical 1D time series, i.e., the temporal fluctuations reflect the occurrence of certain faults [7]. Following such ideas, scholars have proposed a series of IFD approaches.…”

Section: Introductionmentioning

confidence: 99%

A Multi-scale Attention-Based Transfer Model for Cross-bearing Fault Diagnosis

2024

Int J Comput Intell Syst

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Bearings are key components of mechanical equipment, and fault diagnosis is a necessary and important measure to ensure bearing safety. Driven by industrial big data and deep learning (DL), intelligent fault diagnosis (IFD) has made great progress in recent years. However, most of the existing methods mainly focus on the fault diagnosis of individual bearings, and the feature extraction and fault classification rely on traditional networks and expert experience, which cannot meet the diagnostic requirements of cross-bearing conditions. To fill this research gap, this paper proposes a multi-scale attention-based transfer model (MSATM). First, the collected vibration signals are converted into time–frequency maps as samples, and the proposed MSATM employs multi-scale residual learning and attention mechanism to adaptively extract sensitive fault features, and recognizes faults of new bearings by deep transfer learning using the trained MSATM. A large number of experimental results based on a bearing benchmark validate the effectiveness and superiority of the proposed method and provide a promising tool for cross-bearing fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

A Multi-scale Attention-Based Transfer Model for Cross-bearing Fault Diagnosis

2024

Int J Comput Intell Syst

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“…Shokrolahi et al proposed a fault detection method based on deep CNN, which had a strong ability to extract discriminative features [19]. In addition, 1D-CNN with the unique ability to process 1D time series has been widely used in the fields of audio processing [20], bearing fault diagnosis [21][22][23] and structural damage detection [24]. The output signal of the analog circuit is exactly a one-dimensional time series signal, so 1D-CNN has been applied to analog circuit fault diagnosis in recent research.…”

Section: Introductionmentioning

confidence: 99%

A Novel Analog Circuit Fault Diagnosis Method Based on Multi-channel 1D-Resnet and Wavelet Packet Transform

Zhou

Tang

Liang

2022

Preprint

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Fault diagnosis techniques for analog circuits are widely applied in the diagnosis of electronic devices. It is of great significance to accurately and quickly find the fault location and the deviation of fault parameters in analog circuits. In this paper, we propose an innovative fault diagnosis approach for analog circuits. In this method, the wavelet packet transform (WPT) is employed to preprocess the time-domain response signals of analog circuit, and the proposed multi-channel one-dimensional residual networks (MC-1D-ResNet) is utilized for feature mining and fault diagnosis. Specifically, the two-level WPT is first carried out on the measured time-domain response signal to generate one approximate signal and three detailed signals. Additionally, the MC-1D-ResNet is developed by introducing a multi-channel structure based on 1D-ResNet, which can excavate and fuse various features from response signals and realize fault diagnosis. Through the simulation of the Sallen-key bandpass filter circuit and Four-opamp biquad high-pass filter circuit, the diagnostic reliability of this method has been fully verified. The results show that this method is very valuable for diagnosing faults of analog circuits and performs well on both test circuits. Even in the complex Four-opamp biquad high-pass filter circuit, the diagnostic accuracy can be up to 99.74%.

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