Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN

Ahang, Maryam; Jalayer, Masoud; Shojaeinasab, Ardeshir; Ogunfowora, Oluwaseyi; Charter, Todd; Najjaran, Homayoun

doi:10.3390/s22145413

Cited by 16 publications

(6 citation statements)

References 57 publications

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“…The proposed XAI framework can be effectively applied to assess the synthetic samples made by GAN models. This paper includes a detailed evaluation of the Conditional Generative Adversarial Network (CGAN) introduced by [37]. Their work brings forth a unique data augmentation approach tailored to fault data synthesis.…”

Section: Application Ii: Evaluating Generative Modelsmentioning

confidence: 99%

Unveiling the Black Box: A Unified XAI Framework for Signal-Based Deep Learning Models

Shojaeinasab,

Jalayer,

Baniasadi

et al. 2024

Machines

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Condition monitoring (CM) is essential for maintaining operational reliability and safety in complex machinery, particularly in robotic systems. Despite the potential of deep learning (DL) in CM, its ‘black box’ nature restricts its broader adoption, especially in mission-critical applications. Addressing this challenge, our research introduces a robust, four-phase framework explicitly designed for DL-based CM in robotic systems. (1) Feature extraction utilizes advanced Fourier and wavelet transformations to enhance both the model’s accuracy and explainability. (2) Fault diagnosis employs a specialized Convolutional Long Short-Term Memory (CLSTM) model, trained on the features to classify signals effectively. (3) Model refinement uses SHAP (SHapley Additive exPlanation) values for pruning nonessential features, thereby simplifying the model and reducing data dimensionality. (4) CM interpretation develops a system offering insightful explanations of the model’s decision-making process for operators. This framework is rigorously evaluated against five existing fault diagnosis architectures, utilizing two distinct datasets: one involving torque measurements from a robotic arm for safety assessment and another capturing vibration signals from an electric motor with multiple fault types. The results affirm our framework’s superior optimization, reduced training and inference times, and effectiveness in transparently visualizing fault patterns.

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Section: Application Ii: Evaluating Generative Modelsmentioning

confidence: 99%

Unveiling the Black Box: A Unified XAI Framework for Signal-Based Deep Learning Models

Shojaeinasab,

Jalayer,

Baniasadi

et al. 2024

Machines

Self Cite

View full text Add to dashboard Cite

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“…The effectiveness of the approach is demonstrated through a case study in aerospace shell parts spinning, showcasing precise root cause identification and determination of intervention degree. To further delve into the field of causal discovery in manufacturing and condition monitoring, a comprehensive review can be found in [23] and [24].…”

Section: Causal Discovery In Manufacturing Industrymentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Preprint

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In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoders, showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

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“…Bearings, as common components in the industrial field, are also crucial parts of rotating machinery. About 44% of machine failures in the industrial field are related to bearing faults [1]. Therefore, research on bearing anomaly detection is essential.…”

Section: Introductionmentioning

confidence: 99%

Research on online anomaly detection methods for bearing degradation

Jin,

Xu,

et al. 2024

Meas. Sci. Technol.

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In industrial applications, rolling bearings operate under conditions of high precision and high speed, and their physical and mechanical characteristics change with the increase in operating time. Traditional diagnostic methods struggle to adapt well to the changing characteristics of bearings for online anomaly detection. Therefore, this research proposes an online anomaly detection method for rolling bearings based on Time-density-weighted Incremental SupportVector Data Description (TISVDD). A classification strategy is proposed to prevent sample misclassification in the updating process. The Detection Boundary is established based onSVDD decision boundary to enhance the recognition of abnormal samples in the process of model updating. A dual-screening mechanism update strategy for support vectors is proposed.It involves establishing a preliminary screening mechanism based on the Elimination Boundary.On this basis, an in-depth screening mechanism based on time-density weight is introduced by considering spatiotemporal characteristics of samples, enhancing the real-time performance of online anomaly detection for bearings. Building upon the fused dual-boundary SVDD, a Time-density-weighted Incremental SVDD (TISVDD) framework for online anomaly detection is proposed, enabling the detection model to dynamically update in response to data changes over time. To validate the effectiveness of the proposed method, experi-ments were conducted using the XJTU-SY bearing dataset and real-time datasets collected on an online hardware platform.The results demonstrate the effectiveness and superiority of the method in practical applications.

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Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN

Cited by 16 publications

References 57 publications

Unveiling the Black Box: A Unified XAI Framework for Signal-Based Deep Learning Models

Unveiling the Black Box: A Unified XAI Framework for Signal-Based Deep Learning Models

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Research on online anomaly detection methods for bearing degradation

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