Fine-grained transfer learning based on deep feature decomposition for rotating equipment fault diagnosis

Dong, Jian; Su, Depeng; Gao, Yubo; Wu, Xiaoxin; Jiang, Hongyu; Chen, Tao

doi:10.1088/1361-6501/acc04a

Cited by 12 publications

(6 citation statements)

References 47 publications

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“…The generated energy-time-frequency representations are used to visualize 2D heatmaps that map the signal's energy across time-frequency instants. Accordingly, computer vision and DL techniques are leveraged where these heatmaps serve as input images [200]- [208], [212], [217], [222], [223], [227], [228], [230], [232], [233], [238], [239], [241]. 3) Transformation coefficients as signal representations: In these approaches, the generated mappings are treated as transformed representations of the signal.…”

Section: B Transform-based Methodsmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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Recent advancements in sensing, measurement, and computing technologies have significantly expanded the potential for signal-based applications, leveraging the synergy between signal processing and Machine Learning (ML) to improve both performance and reliability. This fusion represents a critical point in the evolution of signal-based systems, highlighting the need to bridge the existing knowledge gap between these two interdisciplinary fields. Despite many attempts in the existing literature to bridge this gap, most are limited to specific applications and focus mainly on feature extraction, often assuming extensive prior knowledge in signal processing. This assumption creates a significant obstacle for a wide range of readers. To address these challenges, this paper takes an integrated article approach. It begins with a detailed tutorial on the fundamentals of signal processing, providing the reader with the necessary background knowledge. Following this, it explores the key stages of a standard signal processing-based ML pipeline, offering an in-depth review of feature extraction techniques, their inherent challenges, and solutions. Differing from existing literature, this work offers an application-independent review and introduces a novel classification taxonomy for feature extraction techniques. Furthermore, it aims at linking theoretical concepts with practical applications, and demonstrates this through two specific use cases: a spectral-based method for condition monitoring of rolling bearings and a wavelet energy analysis for epilepsy detection using EEG signals. In addition to theoretical contributions, this work promotes a collaborative research culture by providing a public repository of relevant Python and MATLAB signal processing codes. This effort is intended to support collaborative research efforts and ensure the reproducibility of the results presented.

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Section: B Transform-based Methodsmentioning

confidence: 99%

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

Aburakhia,

Shami,

Karagiannidis

2024

Preprint

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“…The core concept of this approach is to maximize the utilization of available sample information in situations with limited data, thereby improving model training and prediction. Dong et al [29] introduced a fine-grained classification algorithm with deep feature decomposition to mitigate the interference of redundant features by decomposing the data into features, followed by a fine-grained classifier for cross-domain fault diagnosis in data without target domains. Zheng et al [30] constructed a primary consistent meaning across domains using prior knowledge and learned discriminative and domaininvariant fault features, leading to improved classification results across multiple datasets.…”

Section: Introductionmentioning

confidence: 99%

A novel algorithm for complex transfer conditions in bearing fault diagnosis

Dong,

Su,

Jiang

et al. 2024

Meas. Sci. Technol.

Self Cite

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Transfer learning in bearing fault diagnosis can effectively improve model generalization and accelerate the practical application of fault diagnosis algorithms. However, previous algorithms primarily focused on simple transfer conditions like known target domain data or the same device. In industrial practice, the conditions for algorithm transfer are more complex. Therefore, cross-domain fault diagnosis under complex transfer conditions is a challenging task with significant practical value. This paper proposes a new bearing fault diagnosis algorithm based on attention mechanism and feature enhancement, which provides better feature extraction capabilities. The main approach involves performing deep aliasing on deep features and training the model to identify domain-invariant classification features under extreme conditions for effective fault diagnosis. Additionally, our network performs well in handling low signal-to-noise ratio problems. Extensive experiments were conducted on three different bearing case studies to validate the effectiveness of the proposed method, showing superior performance compared to other deep transfer learning methods.

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“…Transfer learning (TL) technology has been introduced to facilitate fault diagnosis and expand the application scope of contemporary intelligent fault diagnosis methods based on deep learning technology [16,17]. Transfer learning models enable the transmission of knowledge acquired from one domain to another, such as domain adversarial neural networks (DANN) [18], domain separation networks [19], maximum mean discrepancy (MMD) [20], and D-coral [21], thereby facilitating classification, detection, and other tasks in the target domain. Wu et al [22] proposed a diagnostic method for rotating machinery under variable working conditions based on a DANN, wherein the attention mechanism module was integrated into the feature extractor.…”

Section: Introductionmentioning

confidence: 99%

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

Yu,

Liu,

Wang

et al. 2024

Meas. Sci. Technol.

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High-quality labeled data are crucial prerequisites for ensuring the effectiveness of fault diagnosis methods based on deep learning technology. However, in practical scenarios, providing abundant training data with accurate labels for these approaches is unfeasible owing to the constraints imposed by the operating and working conditions. To tackle this realistic challenge, we propose an innovative feature separation simulation-assisted transfer framework (FSSATF) for the fault diagnosis of rotating machinery. The primary concept of FSSATF is to leverage dynamic simulation-assisted data as a surrogate for the labeled data of actual equipment and integrate the feature separation network to explicitly extract domain-independent and fault-discriminative features from the simulated and actual domains, facilitating knowledge transfer and enhancing fault diagnosis capabilities. Specifically, we design a feature separation network consisting of two feature extractors. The special feature extractor is trained with the proposed target domain classification loss to explicitly separate the noisy features from the actual data. Moreover, our proposed domain adaptive loss function effectively narrows the distribution discrepancy between the simulated and actual data, promoting the shared feature extractor to capture domain-invariant and fault-discriminative features. Additionally, clustering learning is embedded into FSSATF to minimize the distance between samples of the same category, strengthening the model's capabilities for feature extraction, and improving its performance in real machinery fault diagnosis. Artificially damaged and run-to-failure datasets were employed to validate the effectiveness and superiority of FSSATF. The comparative analysis results demonstrate that the fault diagnosis performance surpasses those of other advanced transfer learning fault diagnosis methods.

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Fine-grained transfer learning based on deep feature decomposition for rotating equipment fault diagnosis

Cited by 12 publications

References 47 publications

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

On the Intersection of Signal Processing and Machine Learning: A Use Case-Driven Analysis Approach

A novel algorithm for complex transfer conditions in bearing fault diagnosis

A feature separation simulation-assisted transfer framework for rotating machinery fault intelligent diagnosis

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