Degradation Assessment Method Research Articles

A Performance degradation assessment method for complex electromechanical systems based on adaptive evidential reasoning rule

A Performance degradation assessment method for complex electromechanical systems based on adaptive evidential reasoning rule

Degradation assessment of wind turbine based on additional load measurements

In recent years, there has been an increasing focus on the degradation assessment of wind turbines using supervisory control and data acquisition (SCADA) data, establishing condition monitoring as the preferred approach. However, SCADA data primarily focuses on general condition variables, such as temperature, while overlooking critical metrics like load factors for assessing the mechanical properties of wind turbines. The load parameters can be utilized to assess the mechanical stresses and strains experienced by wind turbine components, thereby offering more precise information on degradation. To address this gap, we explore the feasibility of implementing additional load monitoring for the degradation assessment of wind turbines. Specifically, we design a cost-effective load sensor system for collecting load data from wind turbines. On this basis, a novel degradation assessment method that incorporates additional load data and a nonlinear dynamic state-space neural network model is proposed to extract degradation information from wind turbines. Then, we introduce a dynamic degradation index, derived through trend analysis, which effectively captures and quantifies the degradation levels of wind turbines over time. Finally, a case study using a dataset collected from a real wind farm is provided to validate the proposed method. The results demonstrate a significant enhancement in degradation assessment. By incorporating additional load data, the proposed method exhibits heightened responsiveness to degradation levels and more effectively captures the progression of degradation trends.

Assessment of Hull and Propeller Performance Degradation Based on TSO-GA-LSTM

Evaluating the degradation of hull and ship performance and exploring their degradation pathways is crucial for developing scientific and reasonable ship maintenance plans. This paper proposes a two-stage optimization (TSO) algorithm that combines the Genetic Algorithm (GA) and Long Short-Term Memory (LSTM) network, capable of simultaneously optimizing input features and model parameters to enhance the accuracy and generalization ability of speed prediction models. Additionally, a performance degradation assessment method based on speed loss is provided, aimed at evaluating the degradation of hull and propeller performance, as well as extracting the performance degradation paths. The results indicated that the proposed TSO-LSTM-GA algorithm significantly outperformed existing baseline models. Furthermore, the provided performance degradation assessment method demonstrated certain effectiveness on the target ship data, with a measured degradation rate of 0.00344 kn/d and a performance degradation of 9.569% over 478 days, corresponding to an annual speed loss of 1.257 kn.

Performance degradation assessment of rolling bearing under vibration signal monitoring based on optimized variational mode decomposition and improved fuzzy support vector data description

Performance degradation assessment methods for rolling bearings under vibration signal monitoring typically involve extracting signal degradation features and inputting them directly into unoptimized assessment models. However, this method often fails to characterize degradation trends and detect early faults in bearings effectively. Moreover, it is susceptible to interference from outliers and false signal fluctuations, posing challenges for accurate performance degradation assessment. To solve the above problems, a novel performance degradation assessment method of rolling bearings based on optimized variational mode decomposition to construct the maximum information degradation feature set and improved hippopotamus optimization algorithm optimized fuzzy support vector data description is proposed. This method effectively suppresses the signal degradation feature pollution caused by the redundancy of irrelevant frequency components, realizes the screening of effective degradation feature sets, and further improves the accuracy of bearing performance degradation assessment. Through experimental verification, this research method uses early healthy rolling bearing samples to establish an assessment model, which can adaptively determine the starting point and degradation trend of bearing degradation. In comparison with other methods for performance degradation assessment under vibration monitoring, it offers distinct advantages.

Performance degradation assessment of rolling bearings based on the comprehensive characteristic index and improved SVDD

Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing.

Abstract 2323: Deciphering RNA degradation: Insights from a comparative analysis of paired fresh frozen/FFPE total RNA-seq

Abstract Background: Fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) samples are primary resources for archival tissues in cancer studies. Despite the advantages of easy storage and cost-effectiveness, FFPE samples have the disadvantage of inevitable chemical-induced RNA degradation. Although, in mRNA-seq data, the 3' bias due to the characteristics of the mRNA-seq platform allows the measure of RNA degradation levels, for total RNA-seq and FFPE samples, there is still no clear measure for RNA degradation. Methods: Our study, utilizing The Cancer Genome Atlas (TCGA) pilot data comprised of 26 paired samples of fresh frozen mRNA-seq [FFM], fresh frozen total RNA-seq [FFT], and FFPE total RNA-seq [PET], investigated RNA degradation patterns in FFPE samples. Upon investigating the read coverage of transcripts in FFPE data, we observed increased noise parameters in selected samples compared to others, suggesting a method for assessment of RNA degradation in FFPE. To measure these noise patterns, we developed a method called windowCV (wCV) utilizing coefficient of variance (CV) along the transcript length. Also, based on reports that membrane-surrounded RNA such as lncRNA and mitochondrial RNA (mtRNA) might be less affected by formalin fixation, we developed the simple measure dividing expression value of other proteins with certain lncRNA or mtRNA inferring the degree of RNA degradation. Results: We first compared the shape of transcript read coverage from 3 different RNA-seq data and observed 3’bias pattern in FFM which was not observed in FFT and PET. For FFT and PET, we applied wCV and executed hierarchical clustering. We observed that a subset of samples and genes were clustered by high wCV value suggesting notable RNA degradation. To support the hypothesis of RNA degradation, we utilized Trimmed Mean of M values (TMM) expression ratio of paired FFT and PET to determine which samples and genes were associated with underestimation of expression in PET. Interestingly, we observed that PET/FFT TMM ratio showed negative correlation with wCV. Additionally, when we investigated hierarchical clustering result of PET/FFT TMM ratio, we found that lncRNA and mtRNA were less affected by FFPE. We then calculated expression ratio between membrane-surrounded RNA and a set of protein coding genes largely affected by FFPE. As a result, we found that this ratio positively correlated with paired PET/FFT TMM ratio. We conclude that this simple measurement can provide information of FFPE induced underestimated expression with limited transcriptome data as well as RNA degradation. Conclusion: Our method can provide gene-level CV while avoiding false positive variance signal originating from transcript model and simple way to detect the degree of RNA degradation across FFPE samples. Citation Format: Wonyoung Choi, Miyeon Yeon, Hyo Young Choi, David Neil Hayes. Deciphering RNA degradation: Insights from a comparative analysis of paired fresh frozen/FFPE total RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2323.

A novel performance degradation assessment method for rotating machinery based on the fault information and the dynamic simulation

The performance degradation assessment (PDA) of key components such as gears and rolling bearings is the core technology of prognostics and health management for rotating machinery. Most existing PDA methods may have two deficiencies: (1) the assessment indicator constructed does not consider capturing fault characteristics directly; (2) feature selection is generally based on the measured data of different fault levels, which is difficult to obtain in actual processes; moreover, the selection results lack universality and are difficult to extend to other equipment. To address these issues, this paper proposes a novel PDA method based on fault information and dynamic simulation. First, anomaly detection is performed using four well-known indicators in combination with Mahalanobis distance. Secondly, fault identification is performed using envelope spectrum analysis on anomaly signals to determine the fault type, e.g. gear fault or outer race fault. Thirdly, based on the fault type information, the candidate feature set including fault-domain indicators is selected based on the established dynamic simulation signals to obtain a preliminary assessment vector for the first stage. The stability of the fault domain indicators which capture fault characteristics directly is tested through actual measured normal data. It is used as the second stage of selecting to obtain the assessment vector. Finally, the PDA indicator is calculated based on the assessment vector and Mahalanobis distance. Four experiment case studies demonstrate the proposed PDA method can effectively isolate faults with different defect sizes as well as track the whole performance degradation. The above analysis indicates that the proposed PDA method is expected to be used for the actual rotating machinery.

Evaluation and analysis of target interpretation capability for novel rotating synthetic aperture system

The novel rotating synthetic aperture imaging system has the advantages of short processing cycle, light weight, low cost, and convenient transport, which is a potential development direction for future optical satellite payloads with ultra-large aperture. However, influenced by the coupling effects of the rectangular primary mirror, rotating imaging, and link perturbation, images from this system may have unique spatial and temporal variability with resolution anisotropy, resulting in the dynamics and weak features of targets. The insufficient recognition of system characteristics seriously restricts the application ability of corresponding target interpretation methods. To address this problem, we start with the analysis of system imaging characteristics and target interpretation mechanisms. Considering calculating texture, grayscale, and edge feature parameters in evaluation regions of different sizes, we propose a degradation assessment method based on multi-scale feature characterization. Then, combined with the theoretical calculation and practical target detection experiments, the influence of crucial parameters such as the aspect ratio and rotation angle of the primary mirror on the target interpretation capability is analyzed. The results will provide guidance for the integrated design and optimization of optical imaging systems and intelligent image processing methods.

Bearing performance degradation assessment using adversarial fusion convolutional autoencoder based on multi-source information

Bearing operation states will directly determine the performance of the equipment; thus, monitoring operation status and degradation indicators is the key to ensuring continuous and healthy operation of the equipment. However, most of the research uses single-source information data, which makes it difficult to model when dealing with multi-source information, complex data distribution, and noise. In this paper, a bearing performance degradation assessment method based on multi-source information is proposed to comprehensively utilize the data signals of different structures, spaces, types, and sources. First, the adversarial fusion convolutional autoencoder is constructed for obtaining the degradation index of the bearing, while the adversarial learning strategy is applied to achieve the effect of enhancing the robustness and sensitivity of the degradation indicators extracted by the network. Then the degradation index is input into the support vector data description to determine the fault anomalies of the degradation index adaptively and the fuzzy c-means algorithms to obtain the final rolling bearing performance degradation evaluation results. Through the verification results of two experiment datasets, it is found that the proposed model can achieve accurate evaluation and quantitative analysis of the performance degradation process of bearings. As a result, the entire network ensures the reconstruction accuracy of normal samples while simultaneously stretching the reconstruction error of abnormal samples to achieve accurate monitoring of degradation onset.

Research on Performance Degradation Assessment and Abnormal Health Status Detection of Quayside Crane Lifting Gearbox Based on Information Fusion

In order to solve the problems of subjectivity in the extraction of traditional degradation features and incomplete degradation information contained in a single sensor signal, a performance degradation assessment and abnormal health status detection method based on information fusion for the quayside crane lifting gearbox is proposed. Firstly, the correlation between the vibration and temperature of the gearbox is analyzed; secondly, the Convolutional Neural Network (CNN) and entropy degradation features from the full fault cycle vibration and temperature data of the lifting gearbox are extracted respectively; then, the final degradation indicators of the vibration and temperature data are obtained, respectively, through feature optimization, and the fusion degradation indicator is obtained by combining the two indicators; finally, the performance degradation assessment and abnormal health status detection of the gearbox are carried out. The effectiveness and superiority of the proposed method in the performance degradation evaluation of the gearbox are verified by comparison, and the proposed method can identify the initial degradation point of the gearbox earlier than the method based on the single vibration degradation index and the method based on the fusion of the traditional vibration degradation feature and the temperature entropy degradation feature.

Mechanical Equipment Performance Degradation Assessment Method Based on Improved Degradation Feature Extraction and Sample Completeness

Performance degradation assessment (PDA), as an important part of health management, is playing a crucial role in evaluating the degenerate state of mechanical equipment. To evaluate the performance degradation state more effectively by using monitoring data of mechanical equipment, a PDA method based on improved degradation feature extraction technology and considering sample completeness is proposed in this paper. Firstly, multiple degradation features, including statistical features and intrinsic energy features, are extracted to construct a high-dimensional feature set calculated by time-domain analysis and the EMD method. Then, a sensitive feature set, which has significant robustness, correlation, and monotonicity in the degenerate process, is reduced and processed by the improved PCA method to obtain the final health index. Secondly, the degradation assessment model is established by considering the completeness of the samples. As for the complete sample dataset, which has both normal and failure state data, a logistic regression model (LRM) is built to assess the performance degradation status. In addition, an SVDD model is established for the incomplete sample dataset, which only has normal state data. Finally, the reliability of the proposed method is verified by the public XJTU-SY dataset, and the comparative experiment further verifies the superiority of this method.

Photometric Method to Determine Membrane Degradation in Polymer Electrolyte Fuel Cells

A new method for measuring membrane degradation in polymer electrolyte fuel cells (PEFCs) is proposed. The method is based on the detection of fluoride ions in effluent water from the cathode- and anode outlet of the PEFC using photometry (PM). The fluoride emission rate (FER) is an indicator of the membrane’s state of health (SoH) and can be used to measure the chemical membrane degradation. Commercial catalyst-coated membranes (CCMs) have been tested at 80 °C and 90 °C at 30% relative humidity (RH) to investigate the reliability of the developed method for fuel cell effluent samples. To verify the measurement, a mean-difference plot was created by measuring the same data with a fluorine selective electrode. The average difference was at ±0.13 nmol h−1 cm−2, which indicates good agreement between the two methods. These new findings imply that PM is a promising method for quick and simple assessment of membrane degradation in PEM technology.

Hob performance degradation assessment method based on cyclic statistical energy

Hob performance degradation assessment method based on cyclic statistical energy

A Novel Method for the Non-Destructive Assessment of Strength Degradation and Re-Use Potential of Weathered Float Glass From Facades: A Proof of Concept Study

A Novel Method for the Non-Destructive Assessment of Strength Degradation and Re-Use Potential of Weathered Float Glass From Facades: A Proof of Concept Study

Magnetic nanoparticle loaded biodegradable vascular stents for magnetic resonance imaging and long-term visualization.

Benefiting from their good biosafety and bioabsorbability, polymeric biodegradable stents (BDSs) have promising application prospects in the treatment of cardiovascular diseases. However, due to the low density of the polymer itself, it is difficult to visualize with medical imaging techniques such as CT and MRI, which leads to difficulties in accurate BDS localization and subsequent non-invasive evaluation. Therefore, modification of BDSs to adapt to monitoring techniques for clinical use without affecting their biocompatibility and mechanical properties is a promising strategy to support the clinical translation of BDSs. In this study, Fe3O4 superparamagnetic iron oxide nanoparticles (SPIONs) were synthesized to modify the BDS by ultrasonic spraying. Due to the T2-weighted MR imaging enhancement capability of SPIONs, the fabricated SPION-BDS can be entirely visualized and long-term monitored under MR imaging. Further, a stent degradation assessment method based on the analysis of image gray value changes was established. In conclusion, the constructed SPION-BDS provides a possible solution for precise localization of BDSs after implantation, and furthermore, opens up opportunities for long-term non-invasive monitoring of in vivo BDS degradation and multimodal imaging assessment of vascular endothelial remodeling.

A valid and efficient mechanical performance degradation assessment method for cushion member material EPDM based on DQ NMR inspection and static compression test

The aim of this study is to establish a valid and efficient inspection method for assessing macroscopic mechanical property degradation of ethylene-propylene-diene-monomer (EPDM) by examining crosslinked density variation of microstructure. Inspection methods of mechanical behavior and crosslinked density are based on static compression and double quantum nuclear magnetic resonance ( DQ NMR), respectively. After introducing the hyperelastic constitutive model proposed by Yuhai Xiang and designing different conditions of heat-aging and fatigue load, the significant mechanical parameter variation is observed for the hyperelastic constitutive model. The significant impacted factors include heat-aging time and fatigue load except for heat-aging compression strain. The phenomenon is consistent with DQ NMR test results. We further analyze relationship between macro and micro properties of EPDM. After importing fitting assumption and limitation of DQ NMR data to control fitting error, the fractions of dangling and sol chains and residual dipolar couplings constants in DQ NMR parameters show moderate negative () and high positive () correlation with of constitutive model parameters, respectively. Thus the validity and precision of inspection method is proven to estimate behavior variation of EPDM and hopefully the method can be extended to analyze other rubber-like materials.

Real-Time State of Health Estimation for Solid Oxide Fuel Cells Based on Unscented Kalman Filter

The evolution of performance degradation has become a major obstacle to the long-life operation of the Solid Oxide Fuel Cell (SOFC) system. The feasibility of employing degradation resistance to assess the State of Health (SOH) is proposed and verified. In addition, a real-time Unscented Kalman Filter (UKF) based SOH estimation method is further proposed to eliminate the disturbance of calculating the SOH directly utilizing measurement and electric balance model. The results of real-time SOH estimation with an UKF under constant and varying load conditions demonstrate the feasibility and effectiveness of the SOFC performance degradation assessment method.

Two-Stage Degradation Assessment and Prediction Method for Aircraft Engine Based on Data Fusion

Aeroengine is one of the most concerned objects of the relevant aviation industry and researchers, and it is a hard work to assess and predict performance degradation due to the complex structure and the changeable operating condition of the engine. In order to realize the performance degradation assessment and remaining useful life (RUL) prediction of aeroengine, this paper proposes a two-stage assessment and prediction method based on data fusion. First, the standard deviation merged by multiple selected features is used as the health indicator to characterize the engine performance. Second, a sliding window detection method called average local window slope is proposed to determine the current health state of observations by a specified rule. Finally, the RUL prediction is performed on the observation in the two stages, respectively. On the one hand, a similarity-based RUL prediction method is used to engines in the health stage, and on the other hand, for engines in the degradation stage, a RUL prediction method based on a mapping function of the standard deviation and the current using cycle is established. The proposed method has been applied and verified on the NASA’s C-MAPSS simulation data. Results of degradation assessment and prediction show that the proposed method is trustworthy and feasible from the engineering perspective, and it has better performance in the comprehensive indicator compared with other methods.

A performance degradation assessment method using one-dimensional sparse representation self-learning dictionary

Rotating machinery is a widely-used component of factories and enterprises. Unquestionably, rolling bearing is the backbone of rotating machinery and its performance degradation assessment (PDA) is a vital part of condition-based intelligent health management. Therefore, a PDA method based on one-dimensional sparse representation self-learning dictionary is presented to assess the degradation trend of rolling-element bearings. We proposed a novel feature extraction method named one-dimensional K-singular value decomposition to extract the sensitive feature from run-to-failure data. Then, an improved support vector data description is adopted to establish the PDA model, and a novel degradation indicator is obtained. Subsequently, the proposed method adaptively tracks the development of deterioration and identifies the occurrence of an incipient fault. Experimental results on the simulation signal and XJTU-SY bearing datasets verify the effectiveness and outperform the conventional methods.

Bearing performance degradation assessment based on topological representation and hidden Markov model

Bearing performance degradation assessment is essential to avoid abrupt machinery breakdown. However, background noise, outliers, and other interferences in the monitoring data may restrict the accuracy and stability of bearing performance degradation assessment in practical applications. In this study, a bearing performance degradation assessment method based on the topological representation and hidden Markov model is proposed. To construct a robust and representative feature space, the topological representations, specifically, topological meshes of the original features are obtained by self-organizing map, which can represent the general structure of the original feature space and eliminate outliers and other interferences. Then, the weight vectors of topological meshes are used as degradation features. Finally, the hidden Markov model is adopted as the assessment model to evaluate the bearing performance degradation tendency and detect the initial degradation effectively. To validate the effectiveness and superiority of the proposed method, two experimental datasets are analyzed. Compared with peer methods, the performance indicator curve of the proposed method presents a more smooth and accurate degradation tendency than comparative methods. Moreover, initial degradation can be identified accurately.