Signal-Transformer: A Robust and Interpretable Method for Rotating Machinery Intelligent Fault Diagnosis Under Variable Operating Conditions

Tang, Jian; Zheng, Guodi; Wei, Chao; Huang, Wenbin; Ding, Xiaoxi

doi:10.1109/tim.2022.3169528

Cited by 29 publications

(5 citation statements)

References 25 publications

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“…In (11), the calculation process of y l i in formula x contains the feature information of the length from x m i to x m i +3n . In formula (13), ŷl i contains the feature information of the length from x l i to x l i +n(d1+d2+d3) , and (1 + 2 + 5) > 3.…”

Section: Feature Aggregation Modulementioning

confidence: 99%

See 1 more Smart Citation

Interpretable parallel channel encoding convolutional neural network for bearing fault diagnosis

Tong,

Du,

Jiang

et al. 2024

Meas. Sci. Technol.

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Interpretability plays a crucial role in the application of neural networks for fault diagnosis. Integrating preprocessing methods into neural networks can enhance interpretability while preserving their "end-to-end" characteristics. However, when only redesigning the first layer, subsequent structures still exhibit limited transparency. Additionally, traditional convolution structure is ill-suited for analyzing the readable feature maps derived from vibration signals. To address these challenges, this paper proposes a novel convolution structure for the parameterized signal processing function of the first-layer convolution kernel. This structure incorporates channel mixing for feature augmentation, designs a condensed feature encoder for aggregating and compressing features channel-by-channel, ensures the interpretability of feature map processing, and obtains condensed feature codes to propose SA-LRP (Smooth Activation Layer-wise Relevance Propagation) method that to perform interpretability analysis. Additionally, cubic feature screening is implemented for diagnostic classification to improve structural fitness. We design experiments using multiple datasets to test various indicators of the structure. The results confirm that connecting our convolution architecture for subsequent analysis outperforms other convolution architectures for the convolution kernel of the first-layer parameterized signal processing function. The interpretability of the model is evaluated through SA-LRP method and validates the interpretability of the model.

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Section: Feature Aggregation Modulementioning

confidence: 99%

“…Article [10] proposed the gradient score CAM (GS-CAM) method to make it more suitable for the proposed attention mechanism. Article [11] improves the traditional explanation method to explain the feature map of each layer of attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

Interpretable parallel channel encoding convolutional neural network for bearing fault diagnosis

Tong,

Du,

Jiang

et al. 2024

Meas. Sci. Technol.

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“…The typical transformer mainly consists of multi-head selfattention (MSA), layer normalization (LN), feed-forward neural network (FNN), and residual connection (RC) [52][53][54]. Among them, as presented in figure 6, MSA aims to conduct multiple attention relation calculations and enables it to focus on features of multiple positions simultaneously.…”

Section: Transformermentioning

confidence: 99%

A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication

Liu,

Cui,

Cheng

2023

Meas. Sci. Technol.

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Planetary gearboxes have various merits in mechanical transmission, but their complex structure and intricate operation modes bring large challenges in terms of fault diagnosis. Deep learning has attracted increasing attention in intelligent fault diagnosis and has been successfully adopted for planetary gearbox fault diagnosis, avoiding the difficulty in manually analyzing complex fault features with signal processing methods. This paper presents a comprehensive review of deep learning-based planetary gearbox health state recognition. First, the challenges caused by the complex vibration characteristics of planetary gearboxes in fault diagnosis are analyzed. Second, according to the popularity of deep learning in planetary gearbox fault diagnosis, we briefly introduce six mainstream algorithms, i.e., autoencoder (AE), deep Boltzmann machine (DBM), convolutional neural network (CNN), transformer, generative adversarial network (GAN), and graph neural network (GNN), and some variants of them. Then, the applications of these methods to planetary gearbox fault diagnosis are reviewed. Finally, the research prospects and challenges in this research are discussed. According to the challenges, a dataset is introduced in this paper to facilitate future investigations. We expect that this paper can provide new graduate students, institutions and companies with a preliminary understanding of methods used in this field. The dataset can be downloaded from https://github.com/Liudd-BJUT/WT-planetary-gearbox-dataset.

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“…Liu et al [12] combined the advantages of long short-term memory (LSTM) network with statistical process analysis to predict the fault of aero-engine bearing and obtained ideal accuracy. Tang et al [13] proposed signal embedding to solve the problem of transformer application in mechanical vibration signals, which has outstanding performance in terms of diagnostic accuracy under unknown operating conditions in a robustness way. Chen et al [14] explored the compound fault of industrial robots and proposed an efficient convolutional transformer.…”

Section: Introductionmentioning

confidence: 99%

Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis

Fan,

Deng,

et al. 2023

Biomimetics

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Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and lightweight AlexNet. Initially, one-dimensional time series sensor signals are converted into two-dimensional images using the Gramian angular field method with sliding window augmentation. Next, weighted fusion methods are employed to combine Gramian angular summation field images and Gramian angular difference field images, allowing for the full utilization of image information. Subsequently, a lightweight AlexNet is developed to extract features and classify fused images for fault diagnosis with fewer parameters and a shorter running time. To improve diagnosis accuracy, the particle swarm optimization algorithm is used to optimize the weighted fusion coefficient. The results indicate that the proposed method achieves a fault diagnosis accuracy of 99.72% when the weighted fusion coefficient is 0.276. These findings demonstrate the effectiveness of the proposed method for diagnosing depth sensor faults in robotic fish.

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Signal-Transformer: A Robust and Interpretable Method for Rotating Machinery Intelligent Fault Diagnosis Under Variable Operating Conditions

Cited by 29 publications

References 25 publications

Interpretable parallel channel encoding convolutional neural network for bearing fault diagnosis

Interpretable parallel channel encoding convolutional neural network for bearing fault diagnosis

A review on deep learning in planetary gearbox health state recognition: methods, applications, and dataset publication

Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis

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