Generalized autoencoder-based fault detection method for traction systems with performance degradation

Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To solve this problem,this paper proposes a fault detection method developed by a Generalized Autoencoder(GAE)for systems with performance degradation.The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation.Regardless of the probability distribution,it can handle any data,and the GAE has extremely high sensitivity in anomaly detection.Finally,the effectiveness of this method is verified through the Traction Drive Control System(TDCS)platform.At different performance degradation levels,our method’s experimental results are superior to traditional methods.

Performance Degradation and Reliability Analysis for Redundant Actuation System

Redundant actuator is the key component of Fly-By-Wire （FBW） system in which exists the inherent force fighting among different redundant channels at colligation point, This paper establishes the mathematical model of quad redundant actuator （QRA）, investigates the force equalization algorithm and carries out the performance degradation simulation and reliability analysis under the first failure and the second failure. The results indicate that the optimal equalization algorithm can solve the force fighting effectively, and the QRA can operate at degradation performance continuously under the first failure and the second failure. With the dynamic fault tree analysis, this paper calculates the reliability based on the performance of QRA and proves that the redundant actuator has very high reliability and safety.

Rolling bearing performance degradation evaluation by VMD and embedding selection-based NPE

In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding(ESNPE)method is proposed.Firstly,the acquired vibration signals are decomposed by variational mode decomposition(VMD),and the singular value and relative energy of each intrinsic mode function(IMF)are extracted to form a high-dimensional feature set.Then,the NPE manifold learning method is used to extract the embedded features in the feature space.Considering the problem that useful embedding information is easily suppressed in NPE,an embedding selection strategy is built based on the Spearman correlation coefficient.The effectiveness of embeddings is measured by the coefficient absolute value,and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method.Finally,the degradation index is established using the support vector data description(SVDD)model and bearing performance degradation evaluation is achieved.The proposed method was tested with the whole life experiment data of a rolling bearing,and the result was compared with the feature extraction methods of traditional principal component analysis(PCA)and NPE.The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index.

Application of a Novel Method for Machine Performance Degradation Assessment Based on Gaussian Mixture Model and Logistic Regression

The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data collected in different conditions.However,failure data are always hard to acquire,thus making those techniques hard to be applied.In this paper,a novel method which does not need failure history data is introduced.Wavelet packet decomposition（WPD） is used to extract features from raw signals,principal component analysis（PCA） is utilized to reduce feature dimensions,and Gaussian mixture model（GMM） is then applied to approximate the feature space distributions.Single-channel confidence value（SCV） is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of single-channel.Furthermore,multi-channel confidence value（MCV）,which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression（LR） and that the task of decision-level sensor fusion is also completed.Both SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine breakdown.The method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this task.The proposed method has engineering significance for machine performance degradation assessment.

Prediction of fuel cell performance degradation using a combined approach of machine learning and impedance spectroscopy

Accurate prediction of performance degradation in complex systems such as solid oxide fuel cells is crucial for expediting technological advancements.However,significant challenges still persist due to limited comprehension of degradation mechanisms and difficulties in acquiring in-situ features.In this study,we propose an effective approach that integrates long short-term memory(LSTM) neural network and dynamic electrochemical impedance spectroscopy(DEIS).This integrated approach enables precise prediction of future evolutions in both current-voltage and EIS features using historical testing data,without prior knowledge of degradation mechanisms.For short-term predictions spanning hundreds of hours,our approach achieves a prediction accuracy exceeding 0.99,showcasing promising prospects for diagnostic applications.Additionally,for long-term predictions spanning thousands of hours,we quantitatively determine the significance of each degradation mechanism,which is crucial for enhancing cell durability.Moreover,our proposed approach demonstrates satisfactory predictive ability in both time and frequency domains,offering the potential to reduce EIS testing time by more than half.

Fuel-optimal trajectory design using solar electric propulsion under power constraints and performance degradation

The fuel-optimal transfer trajectories using solar electric propulsion are designed considering the power constraints and solar array performance degradation.Three different performance degradation models including linear,positive and negative exponential degradations are used in the analysis of three typical rendezvous missions including Apophis,Venus and Ceres,respectively.The optimal control problem is formulated using the calculus of variations and Pontryagin’s maximum principle,which leads to a bang-bang control that is solved by indirect method combined with a homotopic technique.In demonstrating the effects of the power constraints and solar array performance degradation on the power budget and fuel consumption,the time histories of the power profile and the fuel consumptions are compared for the three missions.This study indicates that it is necessary to consider the power constraints and solar array performance degradation for the SEP-based low-thrust trajectory design,espacially for long-duration outbound flights.

A note on diagnosis and performance degradation detection in automatic control systems towards functional safety and cyber security

This note addresses diagnosis and performance degradation detection issues from an integrated viewpoint of functionality maintenance and cyber security of automatic control systems.It calls for more research attention on three aspects:(i)application of control and detection uni ed framework to enhancing the diagnosis capability of feedback control systems,(ii)projection-based fault detection,and complementary and explainable applications of projection-and machine learning-based techniques,and(iii)system performance degradation detection that is of elemental importance for today's automatic control systems.Some ideas and conceptual schemes are presented and illustrated by means of examples,serving as convincing arguments for research e orts in these aspects.They would contribute to the future development of capable diagnosis systems for functionality safe and cyber secure automatic control systems.

Storage life of power switching transistors based on performance degradation data

NPN-type small and medium power switching transistors in 3DK series are used to conduct analyses and studies of accelerating degradation. Through three group studies of accelerating degradation in different temperature-humidity constant stresses, the failure sensitive parameters of transistors are identified and the lifetime of samples is extrapolated from the performance degradation data. Average lifetimes in three common distributions are given, when, combined with the Hallberg-Peck temperature-humidity model, the storage lifetime of transistor samples in the natural storage condition is extrapolated between 105-10^7 h. According to its definition, the accelerating factor is 1462 in 100 ℃/100% relative humidity （RH） stress condition, and 25 ℃/25% RH stress con- dition. Finally, the degradation causes of performance parameters of the test samples are analyzed. The findings can provide certain references for the storage reliability of domestic transistors.

Degradation Performance and Biodiversity of an Anaerobic Polyvinyl Alcohol-Degrading Microbial Community

Polyvinyl alcohol( PVA) is a water-soluble synthetic polymer that is hard to biodegrade. PVA-degrading microorganisms were previously reported as unitary bacteria and most of them have been identified as aerobes. In this work,a microbial community was cultured anaerobically and its degradation performance and biodiversity were analyzed. The microbial community was cultured for more than 40 d,which represents a highly efficient degradation performance with a chemical oxygen demand removal efficiency of 88. 48%. Operational taxonomic unit-based analysis of the sequences revealed a highly diverse community in the reactor. To note,metagenome 16s rDNA sequencing delineated 19 phyla and 41 classes. Specifically, proteobacteria, chlamydiae, bacteroidetes,firmicutes,and planctomycetes play key roles in the biodegradation processes. Moreover,the betaproteobacteria class belonging to the proteobacteria phylum was the predominant bacterial members in this community. Our results demonstrated that anaerobic treatment of PVA wastewater is feasible and confers degradation by a highly diverse microbial community.

Accelerated Degradation Reliability Modeling and Test Data Statistical Analysis of Aerospace Electrical Connector

As few or no failures occur during accelerated life test,it is difficult to assess reliability for long-life products with traditional life tests.Reliability assessment using degradation data of product performance over time becomes a significant approach.Aerospace electrical connector is researched in this paper.Through the analysis of failure mechanism,the performance degradation law is obtained and the statistical model for degradation failure is set up; according to the research on statistical analysis methods for degradation data,accelerated life test theory and method for aerospace electrical connector based on performance degradation is proposed by improving time series analysis method,and the storage reliability is assessed for Y11X series of aerospace electrical connector with degradation data from accelerated degradation test.The result obtained is basically consistent with that obtained from accelerated life test based on failure data,and the two estimates of product＇s characteristic life only have a difference of 8.7%,but the test time shortens about a half.As a result,a systemic approach is proposed for reliability assessment of highly reliable and long-life aerospace product.

Step-stress Accelerated Degradation Test Modeling and Statistical Analysis Methods

In order to get a rapid assessment on the storage reliability of high-reliable and long-life products within the storage period, accelerated degradation test data with a large amount of reliability information of product is adopted. Conducting a constant-stress accelerated degradation test（CSADT） is generally very costly as it requires a large sample size and long time for test. To overcome this problem, it is necessary to carry out research on modeling and statistical analysis methods of step-stress accelerated degradation test （SSADT）. Taking electrical connectors as the object, a research is conducted on statistical model and assessment method for SSADT. On the basis of mixed-effect degradation path model, the statistical model of SSADT for electrical connectors is presented, the maximum likelihood method for SSADT data based on mixed-effect degradation model is proposed. SSADT accelerated by temperature stress is conducted to Y11X-1419 type of electrical connectors, and the storage reliability is assessed with the SSADT data. Compared with the result obtained from accelerated life test, the reliability estimation of 32-year storage period for electrical connectors obtained from S SADT data only have a difference of 0.869%, which validates the accuracy of the degradation model and the feasibility of the test data statistic analysis method put forward.

Cycle life prediction of traction battery based on degradation data

The traction battery cycle life prediction method using performance degradation data was proposed. The example battery was a commercialized lithium-ion cell with LiMn2O4/Graphite cell system. The capacity faded with cycle number follows a traction function path. Two cycle life predicting models were established. The possible cycle life was extrapolated, which follows normal distribution well. The distribution parameters were estimated and the battery reliability was evaluated. The models＇ precision was validated and the effect of the cycle number on the predicting precision was analysed. The cycle life models and reliability evaluation method resolved the difficulty of battery life appraisal, such as long period and high cost.

Reliability Evaluation and Sensitivity Analysis for Multi-state System Based on Time Degradation Measures

The performance and state of multi-state system depend on its structure and different state combinations of the components. In order to evaluate the reliability of multi-state system effectively,a reliability evaluation and sensitivity analysis method based on the time degradation measures was proposed. The equivalence sets of the multi-state system under different output performances were established. The state combinations were classified according to the performance level. The degradation probability models under different states were established,and the new reliability measures,such as dynamic probability of multi-state system,holding time in each state,dynamic expectation function and integrated expectation function of the performance,were proposed and used to implement the dynamic reliability evaluation and sensitivity analysis. A certain diesel engine fuel feeding system was taken as an application example to illustrate the proposed method. The results show that not only the holding time in the desired state of the components and the system can be predicted,but also the best state component in a certain time period can be obtained.

Effects of 1 MeV electron radiation on the AlGaN/GaN high electron mobility transistors

In this study, the effects of 1 MeV electron radiation on the D-mode GaN-based high electron mobility transistors(HEMTs) were investigated after different radiation doses. The changes in electrical properties of the device were obtained, and the related physical mechanisms were analyzed. It indicated that under the radiation dose of 5 × 10^(14) cm^(-2), the channel current cannot be completely pinched off even if the negative gate voltage was lower than the threshold voltage, and the gate leakage current increased significantly. The emission microscopy and scanning electron microscopy were used to determine the damage location. Besides, the radiation dose was adjusted ranging from 5 × 10^(12) to 1 × 10^(14) cm^(-2), and we noticed that the drain-source current increased and the threshold voltage presented slightly negative shift. By calculations, it suggested that the carrier density and electron mobility gradually increased. It provided a reference for the development of device radiation reinforcement technology.

Unified control and detection framework and its applications: a review, some new results, and future perspectives

Initiated three decades ago,integrated design of controllers and fault detectors has continuously attracted research attention.The recent development of the unified control and detection framework with an observer-based residual generator in its core gives a more general form of the previous works.Its applications to residual centred modelling of uncertain control systems,fault detection in feedback control systems with uncertainties,fault-tolerant control(FTC)as well as control performance degradation monitoring,detection and recovery are introduced.In conclusion,some future perspectives are proposed.

Effects of Freeze/Thaw Cycles and Gas Purging Method on Polymer Electrolyte Membrane Fuel Cells

At subzero temperature, the startup capability and performance of polymer electrolyte membrane fuel cell PEMFC deteriorates markedly. The object of this work is to study the degradation mechanism of key compo- nents of PEMFC—membrane-electrode assembly MEA and seek feasible measures to avoid degradation. The ef- fect of freezethaw cycles on the structure of MEA is investigated based on porosity and SEM measurement. The performance of a single cell was also tested before and after repetitious freezethaw cycles. The experimental results indicated that the performance of a PEMFC decreased along with the total operating time as well as the pore size distribution shifting and micro configuration changing. However, when the redundant water had been removed by gas purging, the performance of the PEMFC stack was almost resumed when it experienced again the same subzero temperature test. These results show that it is necessary to remove the water in PEMFCs to maintain stable per- formance under subzero temperature and gas purging is proved to be the effective operation.

Effect of extrusion process on the mechanical and in vitro degradation performance of a biomedical Mg-Zn-Y-Nd alloy

A new type of biomedical Mg-Zn-Y-Nd alloy was developed and thermal extruded by different processes to investigate the effect of extrusion ratio and extrusion pass on its microstructure,mechanical property and degradation performance.The results show that the increase of extrusion ratio could promote the dynamic recrystallization(DRX)process and led to the coarsening of DRXed grains.While the increase of extrusion pass also contributes to the DRX process but refines the DRXed grains.The simultaneous increasing of extrusion ratio and extrusion pass refines the secondary phases obviously.The increase of extrusion ratio has reduced the tensile strength but improved the elongation of the alloy significantly.However,the increase of extrusion pass could enhance the tensile strength and elongation simultaneously,especially the strength.The degradation performance has been optimized effectively through increasing the extrusion ratio and extrusion pass.

Modeling of reliability and performance assessment of a dissimilar redundancy actuation system with failure monitoring

Actuation system is a vital system in an aircraft, providing the force necessary to move flight control surfaces. The system has a significant influence on the overall aircraft performance and its safety. In order to further increase already high reliability and safety, Airbus has imple- mented a dissimilar redundancy actuation system （DRAS） in its aircraft. The DRAS consists of a hydraulic actuation system （HAS） and an electro-hydrostatic actuation system （EHAS）, in which the HAS utilizes a hydraulic source （HS） to move the control surface and the EHAS utilizes an elec- trical supply （ES） to provide the motion force. This paper focuses on the performance degradation processes and fault monitoring strategies of the DRAS, establishes its reliability model based on the generalized stochastic Petri nets （GSPN）, and carries out a reliability assessment considering the fault monitoring coverage rate and the false alarm rate. The results indicate that the proposed reli- ability model of the DRAS, considering the fault and redundancy degradation process and identify monitoring, can express its fault logical relation potential safety hazards.

Erosion degradation characteristics of a linear electro-hydrostatic actuator under a high-frequency turbulent flow field

The paper proposes a performance degradation analysis model based on dynamic erosion wear for a novel Linear Electro-Hydrostatic Actuator（LEHA）. Rather than the traditional statistical methods based on degradation data, the method proposed in this paper firstly analyzes the dominant progressive failure mode of the LEHA based on the working principle and working conditions of the LEHA. The Computational Fluid Dynamics（CFD） method, combining the turbulent theory and the micro erosion principle, is used to establish an erosion model of the rectification mechanism. The erosion rates for different port openings, under a time-varying flow field, are obtained. The piecewise linearization method is applied to update the concentration of contaminated particles within the LEHA, in order to gain insight into the erosion degradation process at various stages of degradation. The main contribution of the proposed model is the application of the dynamic concentration of contamination particles in erosion analysis of Electro-Hydraulic Servo Valves（EHSVs）, throttle valves, spool valves, and needle valves. The effects of system parameters and working conditions on component wear are analyzed by simulations. The results of the proposed model match the expected degradation process.

SAR performance-based fault diagnosis for electrohydraulic control system:A novel FDI framework for closed-loop system

Model-based fault diagnosis serves as an efficient and powerful technique in addressing fault detection and isolation(FDI)issues for control systems.However,the standard methods and their modifications still encounter some difficulties in algorithm design and application for complex higher-order systems.To avoid these difficulties,a novel fault diagnosis framework based on multiple performance indicators of closed-loop control system is proposed.Under this framework,a socalled performance residual vector is constructed to measure the differences between the real system and the nominal model in terms of system stability,accuracy,and rapidity(SAR)respectively.The criteria for quantification,normalization of the SAR residuals and the explicit mappings between the thresholds and the required performance are given.FDI can be easily achieved simultaneously by monitoring the normalized residual vector length and direction in the SAR performance residual space.A case study on electro-hydraulic servo control system of turbofan engine is adopted to demonstrate the effectiveness of the proposed method.