Structure Dictionary Learning-Based Multimode Process Monitoring and its Application to Aluminum Electrolysis Process

Huang, Keke; Wu, Yiming; Yang, Chunhua; Peng, Gongzhuang; Shen, Weiming

doi:10.1109/tase.2020.2984334

Cited by 62 publications

(33 citation statements)

References 38 publications

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“…Industrial systems generally operate under multiple modes due to changing of raw materials, market demands, etc [9]- [11]. Therefore, multimode process monitoring has undergone tremendous development recently [12]- [14], which can be divided into single-model schemes and multiple-model approaches [10], [15]. Most single-model methods remove the multimodality features by a transformation function [11], [16]- [18] and then establish a single monitoring model.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, enough data are required to update the model adaptively when a novel or similar mode arrives [21], [22]. Multiple-model schemes have been widely researched and in general these simply extend the aforementioned (single mode) dynamic monitoring algorithms where a local model is built within each corresponding mode [14], [23]- [25]. For instance, structure dictionary learning was investigated for process monitoring and mode identification, where the common pattern and mode-specific pattern of each mode were extracted [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Continual Learning for Multimode Dynamic Process Monitoring With Applications to an Ultra–Supercritical Thermal Power Plant

Zhang

Zhou

Chen

et al. 2023

IEEE Trans. Automat. Sci. Eng.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Continual Learning for Multimode Dynamic Process Monitoring With Applications to an Ultra–Supercritical Thermal Power Plant

Zhang

Zhou

Chen

et al. 2023

IEEE Trans. Automat. Sci. Eng.

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“…One of the major drawbacks in most of the abovementioned methods is that the diagnosis operation is not performed accurately and the proposed solutions have an error due to the high-dimension of monitored data. To solve this problem, in [32], [33], solutions based on dimension reduction and data processing are presented.…”

Section: Introductionmentioning

confidence: 99%

High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions

et al. 2021

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Accurate and fast fault detection in transmission lines is of high importance to maintain the reliability of power systems. Most of the existing methods suffer from false detection of high-impedance faults. In this paper, the transfer function (TF) method is introduced to evaluate the effect of impedance and location of faults by analyzing the voltage and current signals in the frequency domain. Interpretation of the results of the TF method is considered as a weakness of this method. In order to alleviate this problem, a convolutional neural network (CNN) and the hybrid model of deep reinforcement learning (DRL) are utilized to identify and locate single-phase to ground short circuit faults in transmission lines. Single-phase to ground short circuit faults with various fault impedances are applied on an IEEE standard transmission line system. Then, the TF traces are calculated and are collected as input datasets for the proposed models. The fault location results for each network are evaluated via various statistical performance metrics such as correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE). The R-value of the CNN and DRL models in fault identification is presented as 96.12% and 98.04%, respectively. Finally, in the early detection of single-phase to ground short circuit fault location (high impedance), the results revealed the efficiency of the DRL model with R=96.61% compared to CNN with R=95.21%. INDEX TERMSTransmission line faults, single-phase to ground short circuit, transfer function, deep reinforcement learning, convolutional neural network.

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“…The traditional wind power plant maintenance strategy relies heavily on regular maintenance and after-maintenance, and the deployment of spare parts has a long cycle, which leads to the high cost of failure maintenance and has a huge impact on the operation and maintenance economy of wind power plants [7]. Therefore, how to make early warning before the occurrence of wind turbine failure is of great significance to reduce the operation and maintenance cost of wind farms and the longterm development of wind energy industry [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Diagnosis of Wind Turbine Based on SCADA Data

Liu

Wang

2020

IEEE Access

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Effective early warning of wind turbine failures is of great significance to reduce the operation and maintenance costs of wind farms and improve power generation efficiency. At present, most wind farms are installed with supervisory control and data acquisition (SCADA) system, and SCADA data contains a lot of hidden information, which can be used for fault early warning. This paper uses the generator temperature and gearbox oil temperature in the SCADA data as the entry point for fault warning. Firstly, the eXtreme gradient boosting (XGBoost) algorithm is used to establish the normal temperature regression prediction model of wind turbine components. Then, the residual between the predicted value and the actual value is calculated, and the change trend of the residual is monitored by the principle of exponentially weighted moving-average (EWMA) control chart. Finally, by setting an appropriate threshold, the variation trend of the residual is judged to determine the occurrence and development of the fault. This paper uses two fault detection methods: fixed threshold and dynamic threshold based on adaptive algorithm, and compares the advantages and disadvantages of the two methods. Based on the SCADA data of a wind farm in Inner Mongolia (China), this paper designs the fault early warning test of the wind turbine generator and gearbox. The experimental results show that for the generator, the fixed fault threshold method can give the fault alarm 3 hours in advance, while the dynamic fault threshold determination method can give fault alarm 4.25 hours in advance. For gearbox, the fixed fault threshold method can give the fault alarm 2 hours in advance, while the dynamic threshold fault diagnosis method can send out the fault alarm 2.75 hours in advance. INDEX TERMS Wind turbine; Fault warning; eXtreme gradient boosting (XGBoost); exponentially weighted moving-average (EWMA); supervisory control and data acquisition (SCADA)

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Structure Dictionary Learning-Based Multimode Process Monitoring and its Application to Aluminum Electrolysis Process

Cited by 62 publications

References 38 publications

Continual Learning for Multimode Dynamic Process Monitoring With Applications to an Ultra–Supercritical Thermal Power Plant

Continual Learning for Multimode Dynamic Process Monitoring With Applications to an Ultra–Supercritical Thermal Power Plant

High Impedance Single-Phase Faults Diagnosis in Transmission Lines via Deep Reinforcement Learning of Transfer Functions

Research on Fault Diagnosis of Wind Turbine Based on SCADA Data

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