Deep Forest-Based Fault Diagnosis Method for Chemical Process

Ding, Jiang; Luo, Qingbo; Jia, Lianyin; You, Jane

doi:10.1155/2020/5281512

Cited by 7 publications

(3 citation statements)

References 36 publications

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“…At present, many researchers have adopted the gcForest model to obtain ideal recognition results. Ding et al presented a multi-grained scanning-based weighted cascade forest that has been applied to fault diagnosis in chemical processes [42]. Lev et al proposed a modified version of the confidence screening mechanism based on an adaptive weighting of every training instance at each cascade level of the deep cascade forest [43].…”

Section: Cascade Forestmentioning

confidence: 99%

Multi-Grained Deep Cascade Learning for ECG Biometric Recognition

Wang¹,

Zhang²

2023

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Recently, electrocardiogram (ECG) biometric recognition has become an emerging area of interest in the identity identification technology. However, robust and accurate of ECG biometric recognition are challenging. Sparse representation-based classification (SRC) and deep neural networks (DNNs) have achieved significant success in biometric recognition, but there are some problems, and SRC-based learning methods are one-step models where the latent discriminative information cannot be fully exploited. DNNs are complex models and require large amounts of training data. The proposed method describes the design of a multi-grained deep cascade learning for ECG biometric recognition that addresses above problems. First, the global and local features are generated by principal components analysis (PCA) and the use of one-dimensional multi-resolution local binary pattern (1DMRLBP) method. Second, we obtain new features of class coding based on the sparse representation by multi-granularity scanning. Third, we propose an end-to-end deep cascade learning model without back-propagation to seek more discriminative information. This approach not only effectively solves the one-step model problem of sparse representation, but also effectively reduces the ECG signal noise. Extensive experiments are conducted on four ECG datasets. The results demonstrate that the proposed method can outperform other state-ofthe-art methods in terms of both accuracy and efficiency.

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Section: Cascade Forestmentioning

confidence: 99%

Multi-Grained Deep Cascade Learning for ECG Biometric Recognition

Wang¹,

Zhang²

2023

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“…Simultaneously, when a new cascade layer is extended, cross validation is used to evaluate the overall performance. Once there is no significant improvement in performance, the extension process is terminated automatically [20]. As described in [14], the number of cascading layers can be determined adaptively by evaluating the performance of each layer.…”

Section: B Df Modelmentioning

confidence: 99%

“…Then, cascade structures are constructed using different types of random forests, and fault characteristics are a learned step to achieve fault classification. Ding et al (2020) [20] proposed a weighted cascade forest based on multigrained scanning for fault diagnosis in chemical processes. At present, there are few studies on DF models for condenser fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Power Plant Condenser With the Optimized Deep Forest Algorithm

Cui

Xiao

2022

IEEE Access

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As an important component of power plant operation, condenser fault diagnosis plays a vital role in the safe and stable unit performance. However, the precision of most existing diagnostic methods is not high enough for condenser fault diagnosis. It is considerably difficult to diagnose a condenser fault even under various complicated conditions. In this study, a novel classification hybrid model (PCA-DF) combining the Principal Component Analysis (PCA) method with the Deep Forest (DF) model is proposed based on the motivation of improving the diagnosis accuracy of condenser fault. The algorithm of this hybrid model takes the dimension reduction result of the PCA method as the input to the DF model. The multigrained scanning structure and the dimension reduction method are considered to create a good effect. The experimental results verify the feasibility and effectiveness of this method on the historical fault sample data of the condenser. The focus on the work presented of this paper is to optimize the DF model based on PCA and study the fault diagnosis effect of the hybrid model. Results show that (1) the prediction accuracy for the condenser fault diagnosis can be improved by increasing the sample size with the PCA-DF method. (2) The accuracy of the results obtained by proposing the improved hybrid models is 1%-8% higher than the accuracy of the results obtained by directly introducing the DF model. The modified hybrid models still have advantages over the DF model for a small sample. (3) With an increase in the proportion of training sets, the accuracy of the modified hybrid models is improved correspondingly from 88.18% to 99.23%. (4) Compared with the backpropagation neural network, convolutional neural network, relevance vector machine and kernel Fisher discriminant analysis models, the PCA-DF model has higher accuracy. In this study, the proposed models can eliminate the influence of autocorrelation between data, and condenser fault diagnosis based on modified models has the fastest convergence speed and best accuracy. Furthermore, the proposed novel models can be extended to more complex fault diagnosis in other fields.

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