Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method

Tang, Yongchuan; Sun, Zhaoxing; Zhou, Deyun; Huang, Yubo

doi:10.1007/s40747-023-01268-0

Cited by 17 publications

(2 citation statements)

References 69 publications

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“…In addition, the uncertainty in the debris flow risk analysis still needs to be further investigated. For example, uncertainty measures such as approximate entropy and correlation coefficients can be used to quantify and manage uncertainty in risk analysis [29,30].…”

Section: Plos Onementioning

confidence: 99%

A combination weighting method for debris flow risk assessment based on t-distribution and linear programming optimization algorithm

Li,

Lin,

Qiang

et al. 2024

PLoS ONE

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Debris flow risk assessment can provide some reference for debris flow prevention and control projects. In risk assessment, researchers often only focus on the impact of objective or subjective indicators. For this purpose, this paper proposed a weight calculation method based on t-distribution and linear programming optimization algorithm (LPOA). Taking 72 mudslides in Beichuan County as an example, this paper used analytic hierarchy process (AHP), entropy weight method (EWM) and variation coefficient method (VCM) to obtain the initial weights. Based on the initial weights, weight intervals with different confidence levels were obtained by t-distribution. Subsequently, the final weights were obtained by LOPA in the 90% confidence interval. Finally, the final weights were used to calculate the risk score for each debris flow, thus delineating the level of risk for each debris flow. The results showed that this paper’s method can avoid overemphasizing the importance of a particular indicator compared to EWM and VCM. In contrast, EWM and VCM ignored the effect of debris flow frequency on debris flow risk. The assessment results showed that the 72 debris flows in Beichuan County were mainly dominated by moderate and light risks. Of these, there were 8 high risk debris flows, 24 medium risk debris flows, and 40 light risk debris flows. The excellent triggering conditions provide favorable conditions for the formation of high-risk debris flows. Slightly and moderate risk debris flows are mainly located on both sides of highways and rivers, still posing a minor threat to Beichuan County. The proposed fusion weighting method effectively avoids the limitations of single weight calculating method. Through comparison and data analysis, the rationality of the proposed method is verified, which can provide some reference for combination weighting method and debris flow risk assessment.

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Section: Plos Onementioning

confidence: 99%

A combination weighting method for debris flow risk assessment based on t-distribution and linear programming optimization algorithm

Li,

Lin,

Qiang

et al. 2024

PLoS ONE

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“…However, Failure Mode and Effects Analysis (FMEA) can also be effectively used in this field [24]. In this regard, Tang et al presented a new FMEA technique based on the improved pignistic probability transformation function and grey relational projection method [25]. This approximation was presented for use in aircraft turbine rotor blades and the steel manufacturing process.…”

Section: Introductionmentioning

confidence: 99%

Advancements in Gas Turbine Fault Detection: A Machine Learning Approach Based on the Temporal Convolutional Network–Autoencoder Model

Fahmi,

Reza Kashyzadeh,

Ghorbani

2024

Applied Sciences

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To tackle the complex challenges inherent in gas turbine fault diagnosis, this study uses powerful machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder model was presented to detect anomalies in vibration data. By synergizing TCN capabilities and Multi-Head Attention (MHA) mechanisms, this model introduces a new approach that performs anomaly detection with high accuracy. To train and test the proposed model, a bespoke dataset of CA 202 accelerometers installed in the Kirkuk power plant was used. The proposed model not only outperforms traditional GRU–Autoencoder, LSTM–Autoencoder, and VAE models in terms of anomaly detection accuracy, but also shows the Mean Squared Error (MSE = 1.447), Root Mean Squared Error (RMSE = 1.193), and Mean Absolute Error (MAE = 0.712). These results confirm the effectiveness of the TCN–Autoencoder model in increasing predictive maintenance and operational efficiency in power plants.

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Research and verification on parameter solution of mixed shock model for common cause failure based on particle swarm algorithm

Hu,

Ge,

et al. 2024

Quality & Reliability Eng

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Mixed shock model is an explicit construction method of failure probability model based on component independent failure, system nonfatal shock, and fatal shock failure, which considers common cause failure (CCF) in redundant system. For aerospace systems, a modified mixed shock model is proposed, which considers several components may fail independently and simultaneously in operation. In order to solve the issue that the parameters of the mixed shock model cannot be solved directly based on the failure probability data, a parameter solving method based on particle swarm optimization (PSO) algorithm is proposed. Additionally, the relationship between the failure probability and the gradient of the parameter change is deduced, and the reduced‐order (RO) solution based on the gradient of the parameter change is proposed to improve the efficiency of the solution. A fitness function construction method based on the relative error of the solution probability and the true probability is proposed to improve the probability solution accuracy of multicomponent failure. The nonlinear inertia factor optimization method combined with fitness change is studied to improve the particle swarm dynamics. The accuracy of the results of different parameters solving sequence and different PSO methods are compared, and the effectiveness of the RO solution is verified. The results of the mixed shock model before and after modification are compared with the different CCF data, which verifies the effectiveness and wide applicability of the modified mixed shock model. The results show that the modified mixed shock model for CCF and its parameter solution method can significantly improve the probability solution accuracy of all components failure, and also provide a new theoretical basis and solution method for the quantitative analysis of multiredundant system failure.

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Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method

Cited by 17 publications

References 69 publications

A combination weighting method for debris flow risk assessment based on t-distribution and linear programming optimization algorithm

A combination weighting method for debris flow risk assessment based on t-distribution and linear programming optimization algorithm

Advancements in Gas Turbine Fault Detection: A Machine Learning Approach Based on the Temporal Convolutional Network–Autoencoder Model

Research and verification on parameter solution of mixed shock model for common cause failure based on particle swarm algorithm

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