Interaction analysis–based information modeling of complex electromechanical systems in the processing industry

Wang, Rongxi; Gao, Jianmin; Gao, Zhiyong; Gao, Xu; Jiang, Hongquan; Liang, Zeming

doi:10.1177/0959651817718454

Cited by 3 publications

(2 citation statements)

References 45 publications

(52 reference statements)

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“…Notably, performance quality control of large-scale electromechanical systems contains four parts: monitoring, evaluation, fault root cause tracing, and supportability. Detailed information on and the technologies that use the first three parts can be found in the literature [2,[24][25][26][27][28][29][30]. As shown in FIGURE 1, monitoring forms the foundation of supportability, and both evaluation and fault root cause tracing trigger dynamic supportability activities.…”

Section: Contents Of the Dynamic Supportability Mechanism Of Perfmentioning

confidence: 99%

Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems

Wang

Gao

et al. 2020

IEEE Access

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Scientific and effective supportability mechanisms have a profound impact on large-scale electromechanical systems and improve their performance quality, while decreasing costs. However, the contradiction between the static supportability strategy and time-varying performance quality conditions remains not sufficiently resolved. To solve this problem, this paper proposes a performance quality condition-based dynamic supportability mechanism. First, model-driven risk identification and control chart-based pattern recognition were used to trigger the dynamic allocation of the limit supportability resource. Second, the performance quality mode and effect analysis and the multiagent collaborative chain were defined to identify the resource lists of maintenance activities. Third, a general control framework was constructed to evaluate the compliance and applicability of maintenance activities. This study formulated a dynamic supportability mechanism for performance quality with a closed loop of activity triggering, resource guarantee, and maintenance effectiveness evaluation. As an expansion and improvement of condition-based maintenance, the dynamic supportability mechanism overcomes the several drawbacks of the existing supportability mechanisms of large-scale electromechanical systems. The proposed mechanism also advances the integration of management and technology in comprehensive supportability. Therefore, the proposed mechanism and methods can be flexibly and efficiently used for the dynamic supportability of electromechanical systems. Moreover, this study provides insights into risk identification, macro pattern recognition and process control and can be used for other engineering applications.

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Section: Contents Of the Dynamic Supportability Mechanism Of Perfmentioning

confidence: 99%

Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems

Wang

Gao

et al. 2020

IEEE Access

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“…Compressor group dataset is obtained from the practical compressor group of plant (https://pan.baidu.com/s/1xdiyrMzId3ULA1lB33cu3g fetch code: c7cq). The time series of the compressor group monitoring included the multi-dimensional monitoring data such as pressure, temperature, flow rate, rotating speed, vibration, and so on (Wang et al 2017c). The 16-dimensional monitoring time series of the compressor group dataset are sampled for 120 h, so that a total of 7200 data samples are obtained at the sampling frequency of 1/60 Hz.…”

Section: Compressor Group Datasetmentioning

confidence: 99%

A dilated convolution network-based LSTM model for multi-step prediction of chaotic time series

et al. 2019

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Aiming to solve the problems of low accuracy of multi-step prediction and difficulty in determining the maximum number of prediction steps of chaotic time series, a multi-step time series prediction model based on the dilated convolution network and long short-term memory (LSTM), named the dilated convolution-long short-term memory (DC-LSTM), is proposed. The dilated convolution operation is used to extract the correlation between the predicted variable and correlational variables. The features extracted by dilated convolution operation and historical data of predicted variable are input into LSTM to obtain the desired multi-step prediction result. Furthermore, cross-correlation analyses (CCA) are applied to calculate the reasonable maximum prediction steps of chaotic time series. Actual applications of multi-step prediction were studied to demonstrate the effectiveness of the proposed model which has superiorities in RMSE, MAE and prediction accuracy because of the extraction of correlation between the predicted variable and correlational variables. Moreover, the proposed DC-LSTM model provides a new method for prediction of chaotic time series and lays a foundation for scientific data analysis of chaotic time series monitoring systems.

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An Improved Deng Entropy and Its Application in Pattern Recognition

et al. 2019

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Interaction analysis–based information modeling of complex electromechanical systems in the processing industry

Cited by 3 publications

References 45 publications

Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems

Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems

A dilated convolution network-based LSTM model for multi-step prediction of chaotic time series

An Improved Deng Entropy and Its Application in Pattern Recognition

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