Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions

He, Ziming; Zhong, Xiang; Zhao, Xianzhu

doi:10.1016/j.knosys.2020.106396

Cited by 188 publications

(76 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, in the fault classification step, the fault features will be trained and classified by neural network algorithms, such as support vector machine (SVM) ( [27]- [28]), artificial neural networks (ANN) [29], CNN [30], [31] [32], random forest [33], Wavelet Transform [34].Benefiting from the ability of feature learning, fault diagnosis algorithms based on deep learning are becoming more intelligent and popular [35]. Ince et al presented a 1D Convolutional Neural Networks method to motor detection [36].…”

Section: Introductionmentioning

confidence: 99%

An Improved Bearing Fault Diagnosis Scheme Based on Hierarchical Fuzzy Entropy and Alexnet Network

et al. 2021

View full text Add to dashboard Cite

Bearings are important part of aerospace equipments. Therefore, bearings fault diagnosis and fault detection is necessary for the safe operation. Generally, the bearing are core component and runs in a complex and heavy background noise environment. Thence, the bearing signals will be overwhelmed by noise. At present, the diagnosis methods of bearings are based on the experience of experts which costs a lot of labor and time-consuming. Consequently, to address these inadequacies, a novel method based on variational mode decomposition (VMD) algorithms and Alexnet neural network has been presented. Firstly, it decomposes the nonstationary bearing signals into intrinsic mode functions adaptively. However, background noise will seriously affect the number of signal decompositions and reduce accuracy even over-decomposition. Thence, this paper proposed the hierarchical fuzzy entropy method to adaptively extract weak fault characteristics of bearings. Then Alexnet neural network has been utilized to learn the relationship of fault features and bearings health conditions. The architecture of Alexnet diagnosis model is convenient and efficient. Finally, two tentative dataset are adopted to verify the effectiveness and feasibility of the method proposed in this article. The results show that the presented method has a higher fault diagnosis accuracy rate than traditional convolutional neural networks.

show abstract

Section: Introductionmentioning

confidence: 99%

An Improved Bearing Fault Diagnosis Scheme Based on Hierarchical Fuzzy Entropy and Alexnet Network

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In an effort to boost the generalization ability and avoid overfitting in cases of lacking labeled samples, Te Han et al proposed an adversarial learning framework [30]. In addition, concerning transfer learning with CNN, Zhiyi He et al proposed a method utilizing multi-channel monitored signals to establish good source models before transferring to target models [31]. With the support of a decision fusion strategy, the method can achieve good performance at the target task only with a few target training samples.…”

Section: A Related Workmentioning

confidence: 99%

Efficient Fault Diagnosis of Rolling Bearings Using Neural Network Architecture Search and Sharing Weights

2021

View full text Add to dashboard Cite

Bearing is one of the most vital components of industrial machinery. The failure of bearing causes severe problems in the machinery. Therefore, continuous monitoring for the bearings is essential rather than regular manual checking, with the requirement for accuracy of prediction and efficiency. This paper proposes a novel intelligent bearing fault condition monitoring and diagnosis method focusing on computation efficiency, which is an important aspect of a continuous monitoring and embedded-based diagnosis device. In the proposed method, acoustic emission signals containing bearing health information are converted into 2-D spectrograms by Constant Q-Transform (CQT) before using a convolutional neural network to infer the bearing state. To reduce the latency while maintaining high accuracy, we propose an efficient search space for neural network architecture search, i.e., a channel distribution search, that automatically obtain the best performing network. Moreover, we present a separation between two processes of condition monitoring and fault diagnosis to save overall computing resources, with a policy of sharing weights in the training process and sharing features in the testing process. The experimental results show that the proposed method reduces about 50% inference time compared to previous methods while achieving an accuracy of 99.82% for eight types of single and compound fault diagnosis for variable rotational speeds.INDEX TERMS Acoustic emission, bearing fault condition monitoring, bearing fault diagnosis, convolutional neural network, neural network architecture search.

show abstract

“…With an appropriate network architecture with gradient based backpropagation, CNNs synthesize a complex decision surface and are capable of classifying high-dimensional patterns [9]. Researchers are exploring ensemble transfer CNNs [10], [11], including implementations based on stochastic pooling and Leaky Rectified Linear Unit (LReLU) on multichannel signals for fault diagnosis [12]. However, such complex models are incompatible with edge devices due to memory and processing speed constraints [13] and hence most diagnostic solutions utilize cloud-based post-processing.…”

Section: Introductionmentioning

confidence: 99%

Light-Weight CNN Enabled Edge-Based Framework for Machine Health Diagnosis

Mukherjee

Tallur

2021

IEEE Access

View full text Add to dashboard Cite

Condition Based Monitoring (CBM) leverages sensor measurements for measuring state of health of an asset and autonomous diagnosis of faults to trigger remedial actions. Countless deep learning architectures are available for cloud-based feature engineering, feature extraction and classification of data for CBM models. However, complex models pose memory and processing speed constraints for edge implementation, while cloud-based computing poses high latency, high cost of data transmission and storage and privacy threats. This calls for a data driven machine health diagnosis system that is effective yet edge-compatible and secure. In this work, we present a model that is light-weight and non-redundant by optimizing the model size and complexity for edge implementation onto resource constrained, low-cost hardware. The model is based on a light-weight, edge implementable Convolutional Neural Network (CNN) algorithm that utilizes vibration sensor measurements for fault event estimation of machines. The model was trained and tested on two publicly available and widely studied vibration datasets for rolling element bearing faults. This CNN based classification system using spectrograms is able to achieve near perfect accuracy for both binary as well as multi-class fault classification. Lastly, the trained model was implemented and tested on a Raspberry Pi single-board computer, thus realizing an edge-compatible implementation which is cost-saving, secure and reliable.INDEX TERMS Condition based monitoring, convolutional neural network, machine health monitoring, edge computing, vibration analysis

show abstract

Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions

Cited by 188 publications

References 59 publications

An Improved Bearing Fault Diagnosis Scheme Based on Hierarchical Fuzzy Entropy and Alexnet Network

An Improved Bearing Fault Diagnosis Scheme Based on Hierarchical Fuzzy Entropy and Alexnet Network

Efficient Fault Diagnosis of Rolling Bearings Using Neural Network Architecture Search and Sharing Weights

Light-Weight CNN Enabled Edge-Based Framework for Machine Health Diagnosis

Contact Info

Product

Resources

About