Failure mode and effects analysis (FMEA) is an effective model that identifies the potential risk in the management process. In FMEA, the priority of the failure mode is determined by the risk priority number. There is enormous uncertainty and ambiguity in the traditional FMEA because of the divergence between expert assessments. To address the uncertainty of expert assessments, this work proposes an improved method based on the belief divergence measure. This method uses the belief divergence measure to calculate the average divergence of expert assessments, which is regarded as the reciprocal of the average support of assessments. Then convert the relative support among different experts into the relative weight of the experts. In this way, we will obtain a result with higher reliability. Finally, two practical cases are used to verify the feasibility and effectiveness of this method. The method can be used effectively in practical applications. 

https://www.nature.com/articles/s41598-022-10828-2.pdf

Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure

Permutation Jensen-Shannon divergence for Random Permutation Set

Vibration signals contain abundant information that reflects the health status of wind turbine high-speed shaft bearings ((HSSBs). Accurate health assessment and remaining useful life (RUL) prediction are the keys to the scientific maintenance of wind turbines. In this paper, a method based on the combination of a comprehensive evaluation function and a self-organizing feature map (SOM) network is proposed to construct a health indicator (HI) curve to characterizes the health state of HSSBs. Considering the difficulty in obtaining life cycle data of similar equipment in a short time, the exponential degradation model is selected as the degradation trajectory of HSSBs on the basis of the constructed HI curve, the Bayesian update model, and the expectation–maximization (EM) algorithm are used to predict the RUL of HSSBs. First, the time domain, frequency domain, and time–frequency domain degradation features of HSSBs are extracted. Second, a comprehensive evaluation function is constructed and used to select the degradation features with good performance. Third, the SOM network is used to fuse the selected degradation features to construct a one-dimensional HI curve. Finally, the exponential degradation model is selected as the degradation trajectory of HSSBs, and the Bayesian update and EM algorithm are used to predict the RUL of the HSSB. The monitoring data of a wind turbine HSSB in actual operation is used to validate the model. The HI curve constructed by the method in this paper can better reflect the degradation process of HSSBs. In terms of life prediction, the method in this paper has better prediction accuracy than the SVR model.

https://www.mdpi.com/1996-1073/14/15/4612/pdf

Health Assessment and Remaining Useful Life Prediction of Wind Turbine High-Speed Shaft Bearings

/pdf/managing-uncertainty-of-experts-assessment-in-fmea-with-the-3rsa1ijn.pdf

To address the problems of low recognition accuracy, slow computing speed, large memory consumption, high requirements for hardware facilities of traditional image target recognition algorithms and the deep learning method data set collection difficulties. In this paper, we propose an improved fast Fourier descriptors that can recognize target patterns quickly and accurately. The method first performs image enhancement and other pre-processing on the acquired image. Then uses the butterfly operation algorithm to accelerate the Fourier transform, calculates the Fourier descriptors of the target shape and improves the operation speed by reducing the number of key points of the target shape. Finally, the Fourier descriptors contours of the pattern to be detected and the template pattern are matched to determine the authenticity of the target recognition, and the OpenCV library is used for experimental simulation. The experimental results show that the method can accurately identify the target pattern and improve the recognition speed. 

Target Recognition Based on Improved Fourier Descriptors

Conflicting evidence affects the final target recognition results. Thus, managing conflicting evidence efficiently can help to improve the belief degree of the true target. In current research, the existing approaches based on belief entropy use belief entropy itself to measure evidence conflict. However, it is not convincing to characterize the evidence conflict only through belief entropy itself. To solve this problem, we comprehensively consider the influences of the belief entropy itself and mutual belief entropy on conflict measurement, and propose a novel approach based on an improved belief entropy and entropy distance. The improved belief entropy based on pignistic probability transformation function is named pignistic probability transformation (PPT) entropy that measures the conflict between evidences from the perspective of self-belief entropy. Compared with the state-of-the-art belief entropy, it can measure the uncertainty of evidence more accurately, and make full use of the intersection information of evidence to estimate the degree of evidence conflict more reasonably. Entropy distance is a new distance measurement method and is used to measure the conflict between evidences from the perspective of mutual belief entropy. Two measures are mutually complementary in a sense. The results of numerical examples and target recognition applications demonstrate that our proposed approach has a faster convergence speed, and a higher belief degree of the true target compared with the existing methods.

Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance

Dempster–Shafer (D–S) evidence theory is more and more extensively applied in multi-sensor data fusion. However, it is still an open issue that how to effectively combine highly conflicting evidenc...

https://journals.sagepub.com/doi/pdf/10.1177/15501477211031473

A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion:

Extracting robust feature representation is one of the key challenges for person re-identification (ReID) task. Although convolution neural network (CNN)-based methods have achieved great success, they still cannot handle the part occlusion and misalignment caused by limited receptive field. Recently, pure transformer models have shown its power in the person ReID task. However, current transformer models adopt patches of equal-scale as input, and cannot solve the problem of cross-scale interaction properly. To overcome this problem, an adaptive cross-scale transformer from a perspective of the graph signal, named ACSFormer, is proposed. Specifically, the self-attention module is first treated as an undirected fully connected graph. And then, “node variation” is introduced as an indicator to adaptively merge neighbourhood tokens. To the best of the authors’ knowledge, their ACSFormer is the first work to attempt to combine pure transformers and graph signal processing in the field of person ReID. Extensive evaluations are conducted on three person ReID datasets to validate the performance of ACSFormer. Experiments demonstrate that this ACSFormer performs on par with state-of-the-art CNN-based methods and consistently improves transformer-based baseline, for example, surpassing ViT-baseline by 2.5%, 2.7% and 4.8% mAP on Market1501, DukeMTMC-reID and MSMT17, respectively. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/ipr2.12794

An adaptive cross‐scale transformer based on graph signal processing for person re‐identification

The weighted average is an efficient way to address conflicting evidence combination in the Dempster-Shafer evidence theory. However, it is an open issue how to determine the evidence weights reasonably. Although many traditional conflicting evidence combination solutions based on evidence distance or entropy have been put forward, the evidence weights are determined with a single aspect, and no comprehensive consideration of other useful information affects the weights. Thus, it does not ensure that determination of weights is the most reasonable. By introducing deep learning into conflicting evidence combination, this paper proposes a comprehensive method for determining the evidence weights based on a convolutional neural network. Taking the evidence as the network input and the corresponding weight as the output, it utilizes convolutional neural network to fully mine potentially useful information that affects the evidence weights, in order to determine the weights comprehensively. Additionally, we define a weight loss function. The weights are continuously optimized through back propagation and achieve the optimal when the weight loss function value is the minimum. Classification experimental results demonstrate that the proposed method outperforms traditional ones based on evidence distance or entropy and can be flexibly extended to other application fields as a decision-making fusion method.

/pdf/determination-of-evidence-weights-based-on-convolutional-mk6lcr8r.pdf

Determination of Evidence Weights Based on Convolutional Neural Network for Classification Problem

The negation of probability distribution is a new perspective from which to obtain information. Dempster–Shafer (D–S) evidence theory, as an extension of possibility theory, is widely used in decision-making-level fusion. However, how to reasonably construct the negation of basic probability assignment (BPA) in D–S evidence theory is an open issue. This paper proposes a new negation of BPA, logarithmic negation. It solves the shortcoming of Yin’s negation that maximal entropy cannot be obtained when there are only two focal elements in the BPA. At the same time, the logarithmic negation of BPA inherits the good properties of the negation of probability, such as order reversal, involution, convergence, degeneration, and maximal entropy. Logarithmic negation degenerates into Gao’s negation when the values of the elements all approach 0. In addition, the data fusion method based on logarithmic negation has a higher belief value of the correct target in target recognition application.

/pdf/logarithmic-negation-of-basic-probability-assignment-and-its-3c243f6n.pdf

Shijun Xu

Papers

Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance

A novel divergence measure in Dempster–Shafer evidence theory based on pignistic probability transform and its application in multi-sensor data fusion:

An adaptive cross‐scale transformer based on graph signal processing for person re‐identification

Determination of Evidence Weights Based on Convolutional Neural Network for Classification Problem

Logarithmic Negation of Basic Probability Assignment and Its Application in Target Recognition