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The evidence reasoning (ER) rule has been widely used in various fields to deal with both quantitative and qualitative information with uncertainty. However, when analyzing dynamic systems, the importance of various indicators frequently changes with time and working conditions, such as performance degradation assessment of complex electromechanical systems, and the weights of the traditional evidence reasoning rules cannot be appropriately adjusted. To solve this problem, this paper proposes an adaptive evidence reasoning (AER) rule that can adjust weights according to different times and working conditions. The AER rule has two unique features: adaptive weight operation under time division and adaptive weight operation under working-condition division, which are used to solve the problem of dynamic weight adjustment under different times and working conditions. The CMA-ES algorithm is used to optimize the model parameters. Two case studies of performance degradation assessment are established to prove the advantage of the AER rule: a computer numerical control experiment and a simulation experiment of turbofan aeroengine. The results verify the effectiveness and practicability of the proposed method., Competing Interests: Declaration of Competing Interest The authors declare that they have no Declaration of Interest Statement. We declare that we do not have any competing financial interests or personal relationships., (Copyright © 2024 ISA. Published by Elsevier Ltd. All rights reserved.)

In the existing rolling bearing performance degradation assessment methods, the input signal is usually mixed with a large amount of noise and is easily disturbed by the transfer path. The time information is usually ignored when the model processes the input signal, which affects the effect of bearing performance degradation assessment. To solve the above problems, an end-to-end performance degradation assessment model of railway axle box bearing based on a deep residual shrinkage network and a deep long short-term memory network (DRSN-LSTM) is proposed. The proposed model uses DRSN to extract local abstract features from the signal and denoises the signal to obtain the denoised feature vector, then uses deep LSTM to extract the time-series information of the signal. The healthy time-series signal of the rolling bearing is input into the DRSN-LSTM reconstruction model for training. Time-domain, frequency-domain, and time-frequency-domain features are extracted from the signal both before and after reconstruction to form a multi-domain features vector. The mean square error of the two feature vectors is used as the degradation indicator to implement the performance degradation assessment. Artificially induced defects and rolling bearings life accelerated fatigue test data verify that the proposed model is more sensitive to early failures than mathematical models, shallow networks or other deep learning models. The result is similar to the development trend of bearing failures.

In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed by outliers and false fluctuations in the signal, this study proposes a method of assessing rolling bearings' performance in terms of degradation using adaptive sensitive feature selection and multi-strategy optimized support vector data description (SVDD). First, a high-dimensional feature set of vibration signals from rolling bearings was extracted. Second, a method combining the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and K-medoids was used to comprehensively evaluate the features with multiple evaluation indicators and to adaptively select better degradation features to construct the sensitive feature set. Next, multi-strategy optimization of the SVDD model was carried out by introducing the autocorrelation kernel regression (AAKR) and a multi-kernel function to improve the ability of the evaluation model to overcome outliers and false fluctuations. Through validation, it could be seen that the method in this study uses samples of rolling bearings in the healthy early stage to establish the evaluation model, which can adaptively determine the starting point of the bearing's degradation. The stability and accuracy of the model were effectively improved.

Bearing performance degradation assessment (PDA), as an important part of prognostics and health management (PHM), is significant to prevent major accidents and economic losses in industry. For the data-driven PDA, the extraction and selection of features is quite important. To better integrate the degradation information, the bearing performance degradation assessment based on SC-RMI and Student's t-HMM is proposed in this article. Firstly, spectral clustering was used as a preprocessing step to cluster features with similar degradation curves. Then, rank mutual information, which is more suitable for trendability estimation of long time series, was utilized to select the optimal feature from each cluster. The feature selection method based on these two steps is called SC-RMI for short. With the selected features, Student's t-HMM, which is more robust to outliers, was utilized for performance degradation modeling and assessment. The verifications based on an accelerated life test and the public XJTU-SY dataset showed the superiority of the proposed method.

The aircraft environmental control system (AECS) plays a vital role in the normal operation of an airplane; thus, performance degradation of the AECS may result in failure and even catastrophic consequences. Because performance degradation is inevitable, performance degradation assessment is critical to provide information for appropriate maintenance actions. This paper proposed an integrated framework for performance degradation assessment of AECS by introducing the visual cognition theory into the specific method. First, an observer is used to generate a residual error, which contains degradation information of the AECS. Second, the generated residual error is transformed into a two-dimensional image based on a permutation method. Third, inspired by the multichannel characteristic (MCC), the transformed image is decomposed into several subbands based on the nonsubsampled contourlet transform (NCST) for multiscale and multidirectional feature extraction, thus capturing the precise characteristics of degradation. Then, inspired by the manifold sensing characteristic (MSC), the manifold space is established based on diffusion maps (DM) to reveal the intrinsic evolutionary law of health degradation. Finally, the geodesic distance is employed to represent the deviation between the current state and health baseline. As a health indicator, the confidence value (CV), which is derived from the geodesic distance, is capable of representing the degradation trend of the AECS. The whole process of degradation can be tracked in real time based on the proposed method. An AECS simulation model was established in MATLAB/Simulink, and two typical faults were introduced. The results based on simulation data demonstrate that the proposed assessment method can effectively reflect the degradation process of the AECS and the constructed health indicator is reasonable., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 ISA. Published by Elsevier Ltd. All rights reserved.)

According to the characteristics of the constant deceleration braking system of hoist, a variable step-size fruit fly optimization algorithm-complex Gaussian wavelet support vector data description method is proposed in this article to evaluate the performance of the brake system. This method takes the pressure-time curve of safety braking as the characteristic data and extracts the candidate characteristic parameters from it. After the comprehensive evaluation of the characteristic parameters based on correlation, monotonicity, and predictability, the characteristic parameters are selected to evaluate the overall performance of the brake system. The proposed variable step-size fruit fly optimization algorithm-complex Gaussian wavelet support vector data description model avoids the situation where the classical Drosophila optimization algorithm is easy to fall into the local optimum. The reliability of the proposed method is verified by the simulation data, and the practicability of the proposed method is verified by the test-bed data. The performance evaluation model of variable step-size fruit fly optimization algorithm-complex Gaussian wavelet support vector data description is constructed, which realizes the quantitative evaluation of the brake system health state and provides technical support for the condition-based maintenance and intelligent maintenance of the hoist brake system.

Performance degradation assessment based on condition monitoring plays an important role in ensuring reliable operation of equipment, reducing production downtime and saving maintenance costs, yet performance degradation has strong fuzziness, and the dynamic information is random and fuzzy, making it a challenge how to assess the fuzzy bearing performance degradation. This study proposes a monotonic degradation assessment index of rolling bearings using fuzzy support vector data description (FSVDD) and running time. FSVDD constructs the fuzzy-monitoring coefficient ε⁻ which is sensitive to the initial defect and stably increases as faults develop. Moreover, the parameter ε⁻ describes the accelerating relationships between the damage development and running time. However, the index ε⁻ with an oscillating trend disagrees with the irreversible damage development. The running time is introduced to form a monotonic index, namely damage severity index (DSI). DSI inherits all advantages of ε⁻ and overcomes its disadvantage. A run-to-failure test is carried out to validate the performance of the proposed method. The results show that DSI reflects the growth of the damages with running time perfectly.