A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function

Zhang, Baoshan; Zhang, Lin; Zhang, Bo; Yang, Bofan; Zhao, Yanchao

doi:10.1109/access.2020.2997368

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…The fuzzy evaluation of each state variable based on membership function can effectively reduce the influence of human subjective factors on reliability evaluation of EPOCN. The membership function is a function that realizes the mapping from the state variable value to the state variable score [14]. According to the value of each state variable in the EPOCN, the membership of the state variable is calculated, and each state variable is scored according to the membership.…”

Section: State Variable Scoring Methods Of Epocn Based On Improved Fahpmentioning

confidence: 99%

Research on digital twin-based reliability evaluation of electric power optical communication network

Liu

Zhu

2023

Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022)

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The reliable and stable operation of Electric Power Optical Communication Network (EPOCN) is the basis for the optimization of distributed energy management. How to evaluate the operation reliability of EPOCN devices and carrying reliability remains a challenge. In this paper, we firstly propose a digital twin-based two-dimensional multi-level reliability assessment indicator system of EPOCN, which consists of device reliability and service reliability. Secondly, an improved fuzzy hierarchical reliability assessment method for EPOCN is proposed to assess the reliability level of EPOCN by scoring each level. Finally, an example is given to verify the superiority of the proposed technique in reliability indicator processing, which can effectively reduce the influence of subjective factors of evaluators and objectively reflect the reliability of EPOCN.

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Section: State Variable Scoring Methods Of Epocn Based On Improved Fahpmentioning

confidence: 99%

Research on digital twin-based reliability evaluation of electric power optical communication network

Liu

Zhu

2023

Eighth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2022)

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“…This article takes the Sallen-Key circuit [38] and CSTV circuit [39] as diagnostic examples. Sallen-Key circuits and CSTV circuits are typical circuits that are often used to analog-circuit fault diagnosis.…”

Section: Acquisition and Process Fault Samples Of Analog Circuitsmentioning

confidence: 99%

TLSCA-SVM Fault Diagnosis Optimization Method Based on Transfer Learning

Zhang

2022

Processes

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In fault-diagnosis classification, a pressing issue is the lack of target-fault samples. Obtaining fault data requires a great amount of time, energy and financial resources. These factors affect the accuracy of diagnosis. To address this problem, a novel fault-diagnosis-classification optimization method, namely TLSCA-SVM, which combines the sine cosine algorithm and support vector machine (SCA-SVM) with transfer learning, is proposed here. Considering the availability of fault data, this thesis uses the data generated by analog circuits from different faults for analysis. Firstly, the data signal is collected from different faults of the analog circuit, and then the characteristic data are extracted from the data signals by the wavelet packets. Secondly, to employ the principal component analysis (PCA) reduces the feature-value dimension. Lastly, as an auxiliary condition, the error-penalty item is added to the objective function of the SCA-SVM classifier to construct an innovative fault-diagnosis model namely TLSCA-SVM. Among them, the Sallen–Key bandpass filter circuit and the CSTV filter circuit are used to provide the data for horizontal- and vertical-contrast classification results. Comparing the SCA with the five optimization algorithms, it is concluded that the performance of SCA optimization parameters has certain advantages in the classification accuracy and speed. Additionally, to prove the superiority of the SCA-SVM classification algorithm, the five classification algorithms are compared with the SCA-SVM algorithm. Simulation results showed that the SCA-SVM classification has higher precision and a faster response time compared to the others. After adding the error penalty term to SCA-SVM, TLSCA-SVM requires fewer fault samples to process fault diagnosis. Ultimately, the method which is proposed could not only perform fault diagnosis effectively and quickly, but also could run effectively to achieve the effect of transfer learning in the case of less failure data.

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“…Currently, trend prediction research methods mainly focus on physics-based modeling and data-driven approaches. Model-based methods require the construction of accurate physical or mathematical models to describe the operational processes of the research object [11]. Model-based methods face significant challenges in constructing accurate models of marine equipment in complex and dynamic environments such as ship engine rooms.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

Sun,

Zeng,

2024

JMSE

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As the core component of a ship’s engine room, the operation of a marine diesel engine (MDE) directly affects the economy and safety of the entire vessel. Predicting the future changes in the status parameters of a MDE helps to understand the operational status, enabling timely warnings to the engine crew, and to ensure the safe navigation of the vessel. Therefore, this paper combines the temporal pattern attention mechanism with the bidirectional long short-term memory (BiLSTM) network to propose a novel trend prediction method for short-term exhaust gas temperature (EGT) forecasting. First, the Pearson correlation analysis (PCA) is conducted to identify input feature variables that are strongly correlated with the EGT. Next, the BiLSTM network models input feature variables such as load, fuel oil pressure, and scavenging air pressure and capture the interrelationships between different vectors from the hidden layer matrix within the BiLSTM network. This allows the selection of valuable information across different time steps. Meanwhile, the temporal pattern attention (TPA) mechanism has the ability to explore complex nonlinear dependencies between different time steps and series. This assigns appropriate weights to the feature variables within different time steps of the BiLSTM hidden layer, thereby influencing the input effect. Finally, the improved slime mold algorithm (ISMA) is utilized to optimize the hyperparameters of the prediction model to achieve the best level of short-term EGT trend prediction performance based on the ISMA-BiLSTM-TPA model. The prediction results show that the mean square error, the mean absolute percentage error, the root mean square error and the coefficient of determination of the model are 0.4284, 0.1076, 0.6545 and 98.2%, respectively. These values are significantly better than those of other prediction methods, thus fully validating the stability and accuracy of the model proposed in this paper.

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A Fault Prediction Model of Adaptive Fuzzy Neural Network for Optimal Membership Function

Cited by 12 publications

References 26 publications

Research on digital twin-based reliability evaluation of electric power optical communication network

Research on digital twin-based reliability evaluation of electric power optical communication network

TLSCA-SVM Fault Diagnosis Optimization Method Based on Transfer Learning

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

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