Input Feature Mappings-Based Deep Residual Networks for Fault Diagnosis of Rolling Element Bearing With Complicated Dataset

Hou, Liangsheng; Jiang, Ruizheng; Tan, Yulin; Zhang, Jundong

doi:10.1109/access.2020.3028465

Cited by 30 publications

(12 citation statements)

References 39 publications

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“…Finally, we compared the result between the proposed model and other works from previous authors working with the same dataset. Other works such as [28] employing Multi-Layer Perceptron (MLP) and Deep Belief Network (DBN); TrainingInterference for CNN (TICNN) [29]; Wasserstein distance guided representation learning for domain adaptation (WDGRL) and triplet loss guided adversarial domain adaptation method (TLADA) [30]. The complete comparison is depicted in figure 11.…”

Section: Results Comparisonmentioning

confidence: 99%

Concise convolutional neural network model for fault detection

Firdausi

Ahmad

2022

CST

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Fault detection is an urgent need for maintenance to obtain the optimal scheduling of production activities, improve system reliability, and reduce operation and maintenance costs. Many studies published in recent years focus on machine learning models to detect any system anomalies in line with the era of big data and the fourth industrial revolution (Industry 4.0). Say, a working condition of bearing can be monitored and then any fault can be detected using the vibration analysis of bearing acceleration data. Most of the published works are presented based upon the knowledge of signal processing in which the result depends heavily on feature extraction. It becomes a challenge then to apply a machine learning algorithm directly to the raw acceleration data as it has been successfully applied to raw data in other science and engineering domains. In this article, a concise Convolutional Neural Networks-based deep learning model is proposed for bearing fault detection. The proposed model was concise with 98% less number of parameters compared to other well-known models. It produced 21.21% and 7.03% better accuracy and fault detection rate, respectively. The model was also tested in different operating parameter environments and still gave an excellent result. Since the proposed concise architecture of the model needed short training time, it is deemed suitable for application on manufacturing floor where the pace of production moves fast and the change of the production machine configuration likely occurs.

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Section: Results Comparisonmentioning

confidence: 99%

Concise convolutional neural network model for fault detection

Firdausi

Ahmad

2022

CST

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“…L Hou et al proposed an input feature map (IFM) combined with the residual network (ResNet). The IFM method can extract features without preset parameters [38]. D Wang et al proposed an attention-based multi-dimensional concatenated convolutional neural network (AMDC-CNN).…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions

Lee

Zhuo

Le³

2022

Sensors

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This study proposes a new intelligent diagnostic method for bearing faults in rotating machinery. The method uses a combination of nonlinear mode decomposition based on the improved fast kurtogram, gramian angular field, and convolutional neural network to detect the bearing state of rotating machinery. The nonlinear mode decomposition based on the improved fast kurtogram inherits the advantages of the original algorithm while improving the computational efficiency and signal-to-noise ratio. The gramian angular field can construct a two-dimensional image without destroying the time relationship of the signal. Therefore, the proposed method can perform fault diagnosis on rotating machinery under complex operating conditions. The proposed method is verified on the Paderborn dataset under heavy noise and multiple operating conditions to evaluate its effectiveness. Experimental results show that the proposed model outperforms wavelet denoising and the traditional adaptive decomposition method. The proposed model achieves over 99.6% accuracy in all four operating conditions provided by this dataset, and 93.8% accuracy in a strong noise environment with a signal-to-noise ratio of −4 dB.

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“…While CNN is superior in function, it is also accompanied by the problem of gradient disappearance as the number of layers deepens. According to paper [101] and [100], the residual network can solve the problem of CNN gradient disappearance.…”

Section: ) Bearing Faultsmentioning

confidence: 99%

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

Zhang

Deng

et al. 2022

IEEE Access

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Electric powertrain is constituted by electric machine transmission unit, inverter and battery packs, etc., is a highly-integrated system. Its reliability and safety are not only related to industrial costs, but more importantly to the safety of human life. This review is the first contribution to comprehensively summarize both the feature engineering methods and artificial intelligence (AI) algorithms (including machine learning, neural networks and deep learning) in electric powertrain condition monitoring and fault diagnosis approaches. Specifically, this paper systematically divides the AI-supported method into two main steps: feature engineering and AI approach. On the one hand, it introduces the data and feature processing in AI-supported methods, and on the other hand it summarizes input signals, feature methods and AI algorithms included in the AI method in cases. Therefore, firstly this review is to guide how to choose the appropriate feature engineering method in further research. Secondly, the up-to-date AI algorithms adopted for powertrain health monitoring are presented in detail. Finally, such current approaches are discussed and future trends are proposed.

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Input Feature Mappings-Based Deep Residual Networks for Fault Diagnosis of Rolling Element Bearing With Complicated Dataset

Cited by 30 publications

References 39 publications

Concise convolutional neural network model for fault detection

Concise convolutional neural network model for fault detection

A Robust Deep Neural Network for Rolling Element Fault Diagnosis under Various Operating and Noisy Conditions

Feature Engineering and Artificial Intelligence-Supported Approaches Used for Electric Powertrain Fault Diagnosis: A Review

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