PCA-LMNN-Based Fault Diagnosis Method for Ironmaking Processes with Insufficient Faulty Data

Zhang, Tongshuai; Ye, Hao; Hai-feng, Zhang; Li, Mingliang

doi:10.2355/isijinternational.isijint-2016-101

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…Auto-encoders (AE) are among the most promising deep learning techniques for automatic feature extraction of mechanical signals. They have been adopted in a variety of FDD problems in the semiconductor industry [10], foundry processes [11], gearboxes [12] and rotating machinery [13,14]. Ref.…”

Section: Review Of Current Modelsmentioning

confidence: 99%

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

et al. 2022

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Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem: (1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data preprocessing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial with Gradient Penalty Networks (WGAN-GP) to generate synthetic samples to populate the rare fault class and enrich the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is also proposed. To verify the effectiveness of the developed framework, different bearing datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM significantly outperforms the other state-of-the-art FDD frameworks.

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Section: Review Of Current Modelsmentioning

confidence: 99%

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Auto-encoders (AE) are among the most promising DL techniques for automatic feature extraction of mechanical signals. They have been adopted in a variety of FDD problems in the semiconductor industry [10], foundry processes [11], gearboxes [12] and rotating machinery [13], [14]. [15] employed the "stacked" variation of AE to initialize the weights and offsets of a multi-layer neural network and to provide an expert knowledge for spacecraft conditions.…”

Section: Review Of Current Modelsmentioning

confidence: 99%

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Jalayer,

Kaboli,

Orsenigo

et al. 2022

Preprint

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Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem:(1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data pre-processing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial Networks (WGAN) to generate synthetic samples to populate the rare fault class and enhance the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is also proposed. To verify the effectiveness of the developed framework, different bearing datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM significantly outperforms the other state-of-the-art FDD frameworks.

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“…Due to the lack of data from practical iron and steel manufacturing processes, most previous work used experimental or simulation data to construct models, which could deliver desired performances only when comprehensive rules and sufficient historical information were available. 22) Collecting data in real manufacturing production pro-cesses is a challenging task, as public network access is not allowed for security purposes. Figure 1 illustrates the configuration of the constant speed axial blast furnace blower, in which the location of the sensors for outlet flow rate, pressure and GVO detection is designated (blue arrows).…”

Section: Collection Of Real Blast Furnace Datamentioning

confidence: 99%

Guide Vane Opening Prediction for Constant Speed Axial Blowers in Blast Furnace Ironmaking with Variation Information

et al. 2021

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Determining appropriate guide vane openings (GVOs) of axial blowers under varying industrial conditions is vital for smooth operations in blast furnace ironmaking. This work analyses the influence GVO variations have on outlet air flow rate and pressure by using data taken from operating, industrial blast furnaces, based on which a support vector machine (SVM)-based GVO prediction model is developed. Outcomes reveal that the change status of GVOs, i.e., whether the GVO angle increases or decreases, is critical in determining the relationship between air flow and pressure. By introducing the change status and removing the transition outliers, predictions for the optimal GVOs required to meet the desired air flow rate and pressure in real time can be more accurately determined. The measured values of GVOs range from 0% to 100%, and the SVM-based model developed in this work predicted the GVOs with an RMSE of 0.2480%, significantly improving upon the baseline model which had an RMSE of 0.6047%. The resulting method can provide insights into the operation of complex ironmaking processes, enabling a more efficient adjustment of GVOs.

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PCA-LMNN-Based Fault Diagnosis Method for Ironmaking Processes with Insufficient Faulty Data

Cited by 9 publications

References 23 publications

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

Guide Vane Opening Prediction for Constant Speed Axial Blowers in Blast Furnace Ironmaking with Variation Information

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