Explainable 1DCNN with demodulated frequency features method for fault diagnosis of rolling bearing under time-varying speed conditions

Lu, Feiyu; Tong, Qingbin; Feng, Ziwei; Wan, Qingzhu; an, Guoping; Li, Yilei; Wang, Meng; Cao, Junci; Guo, Tao

doi:10.1088/1361-6501/ac78c5

Cited by 13 publications

(7 citation statements)

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“…A deep convolutional neural network (DCNN) [26,27] is constructed by the alternating combination of multiple convolutional and pooling layers. The computational process of the DCNN is as follows: (1) , b (1) ), W (2) , b (2)…”

Section: Cnnmentioning

confidence: 99%

The research on fault diagnosis of rolling bearing based on current signal CNN-SVM

Wang,

Meng,

Wang

et al. 2023

Meas. Sci. Technol.

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This article proposes a novel approach to address the issues of low accuracy in fault diagnosis and the difficulty in installing sensors on rolling bearings in mechanical and electrical equipment systems. To accomplish fault diagnosis of rolling bearings, a network structure algorithm based on Convolutional Neural Network (CNN) and Support Vector Machine (SVM) is presented, which incorporates the electric motor current signal. Firstly, the collected electric motor current signal is subjected to a wavelet filter with a soft-hard threshold to eliminate the noise. Secondly, the processed data is fed as input to a one-dimensional Convolutional Neural Network (1D-CNN) to perform feature extraction and dimensionality reduction. Finally, the dimensionality-reduced features are processed by a Support Vector Machine to diagnose rolling bearing faults. The research results indicate that the proposed method significantly improves the accuracy of rolling bearing fault diagnosis compared to other approaches, with an accuracy of up to 99.01%. This study introduces an innovative approach that can be applied to the field of rolling bearing fault diagnosis, offering valuable insights for research and application in this domain.

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Section: Cnnmentioning

confidence: 99%

The research on fault diagnosis of rolling bearing based on current signal CNN-SVM

Wang,

Meng,

Wang

et al. 2023

Meas. Sci. Technol.

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“…Since the input data is single-dimensional, 1D Convolution is used instead of 2D Convolution. This is because 1D convolutional neural network (1DCNN) [29] requires less computational power and fewer layers for extracting helpful insights than 2D convolution.…”

Section: Feature Extractionmentioning

confidence: 99%

An explainable deep learning approach for detection and isolation of sensor and machine faults in predictive maintenance paradigm

Sinha,

Das

2023

Meas. Sci. Technol.

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The predictive health maintenance techniques identify the machine faults by analyzing the data collected by low-cost sensors assuming that sensors are free from any faults. However, aging and environmental condition cause sensors also be faulty, leading to incorrect interpretations of the collected data and subsequently resulting in erroneous machine health predictions. To mitigate this problem, this paper proposes a hybrid model that can differentiate between sensor and system faults. The data used for training the model is collected from a power system hardware setup by experimental procedures. A Convolutional Neural Network (CNN) model is used to extract optimized features from the raw data automatically, which are then fed to the XGBoost model for sensor and machine fault isolation with an overall accuracy of 98.15%. The data having sensor fault was then fed to a Deep Autoencoder (DAE), which eliminated the sensor fault components and reconstructed the data with an average RMSE of 0.0576. Thereafter, the corrected signal was used to detect the system fault using the hybrid CNN-XGBoost model with 99.77% accuracy. Therefore, by isolating the sensor faults, the proposed technique establishes better confidence in predictive maintenance. Further, Explainable AI has been utilized to interpret the model prediction in human-understandable terms in order to increase trustworthiness. 

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“…Reference [44] introduced five methods (FEE-SVM, DFF-MLP, DFF-1DCNN, DFF-TICNN, and DFF-Lightweight 1DCNN) for diagnosing fault conditions in variable speed data. The accuracy rates for these techniques were reported as 45.60%, 55.16%, 90.08%, 93.06%, and 96.26%, respectively.…”

Section: Case I: Variable-speed Vibration Datasetmentioning

confidence: 99%

Efficient DCNN-LSTM Model for Fault Diagnosis of Raw Vibration Signals: Applications to Variable Speed Rotating Machines and Diverse Fault Depths Datasets

Ahsan

Salah

2023

Symmetry

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Bearings are the backbone of industrial machines that can shut down or damage the whole process when a fault occurs in them. Therefore, health diagnosis and fault identification in the bearings are essential to avoid a sudden shutdown. Vibration signals from the rotating bearings are extensively used to diagnose the health of industrial machines as well as to analyze their symmetrical behavior. When a fault occurs in the bearings, deviations from their symmetrical behavior can be indicative of potential faults. However, fault identification is challenging when (1) the vibration signals are recorded from variable speeds compared to the constant speed and (2) the vibration signals have diverse fault depths. In this work, we have proposed a highly accurate Deep Convolution Neural Network (DCNN)–Long Short-Term Memory (LSTM) model with a SoftMax classifier. The proposed model offers an innovative approach to fault diagnosis, as it obviates the need for preprocessing and digital signal processing techniques for feature computation. It demonstrates remarkable efficiency in accurately diagnosing fault conditions across variable speed vibration datasets encompassing diverse fault conditions, including but not limited to outer race fault, inner race fault, ball fault, and mixed faults, as well as constant speed datasets with varying fault depths. The proposed method can extract the features automatically from these vibration signals and, hence, are excellent to enhance the performance and efficiency to diagnose the machine’s health. For the experimental study, two different datasets—the constant speed with different fault depths and variable speed rotating machines—are considered to validate the performance of the proposed method. The accuracy achieved for the variable speed rotating machine dataset is 99.40%, while for the diverse fault dataset, the accuracy reaches 99.87%. Furthermore, the experimental results of the proposed method are compared with the existing methods in the literature as well as the artificial neural network (ANN) model.

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Explainable 1DCNN with demodulated frequency features method for fault diagnosis of rolling bearing under time-varying speed conditions

Cited by 13 publications

References 50 publications

The research on fault diagnosis of rolling bearing based on current signal CNN-SVM

The research on fault diagnosis of rolling bearing based on current signal CNN-SVM

An explainable deep learning approach for detection and isolation of sensor and machine faults in predictive maintenance paradigm

Efficient DCNN-LSTM Model for Fault Diagnosis of Raw Vibration Signals: Applications to Variable Speed Rotating Machines and Diverse Fault Depths Datasets

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