A data-driven digital-twin prognostics method for proton exchange membrane fuel cell remaining useful life prediction

Prognostics and health management of proton exchange membrane fuel cell (PEMFC) systems have driven increasing research attention in recent years as the durability of PEMFC stack remains as a technical barrier for its large-scale commercialization. To monitor the health state during PEMFC operation, digital twin (DT), as a smart manufacturing technique, is applied in this paper to establish an ensemble remaining useful life prediction system. A data-driven DT is constructed to integrate the physical knowledge of the system and a deep transfer learning model based on stacked denoising autoencoder is used to update the DT with online measurement. A case study with experimental PEMFC degradation data is presented where the proposed data-driven DT prognostics method has applied and reached a high prediction accuracy. Furthermore, the predicted results are proved to be less affected even with limited measurement data.     

Electrodeposited CuMnS and CoMnS electrodes for high-performance asymmetric supercapacitor devices

The transition metal sulfides have gained extensive interest in energy storage devices owing to their unique features. However, the research-based on cobalt, copper and manganese sulfide composites is limited while they are considered as promising contenders for supercapacitor electrodes. The simplest and facile one-step electrodeposition technique was adopted for the direct growth of CuMnS and CoMnS on a Ni-substrate. The electrochemical properties of CuMnS and CoMnS electrodes were investigated and maximum specific capacitances of 1691 and 2290 F/g, respectively, were obtained at 10 A/g current density. Further, these electrodes are investigated with activated carbon (AC) electrode to fabricate asymmetric supercapacitor devices where CoMnS//AC exhibited superior energy density values than CuMnS//AC device. However, both the devices show a relatively uniform capacitance retention rate (~94%) after 2500 charging-discharging cycles. Furthermore, the role of capacitive- and diffusive-controlled contributions in the charge storage phenomenon of supercapacitor devices are explicitly scrutinized by employing Dunn's model. Co-electrodeposition of transition metal sulfides has great potential as electrode material for highly effective supercapacitor devices.     

A novel PEM fuel cell remaining useful life prediction method based on singular spectrum analysis and deep Gaussian processes

Accurate remaining useful life (RUL) prediction of proton exchange membrane fuel cells (PEMFCs) can assess the reliability of fuel cells to determine the occurrence of failures and mitigate their operational risk. However, is it quite challenging to design a high-precision prediction method because the implicit degradation details of PEMFCs are difficult to learn well from the measurement data with high-frequency noise. Recognizing this, a novel RUL prediction method based on singular spectrum analysis (SSA) and deep Gaussian process (DGP) is proposed in this paper. The SSA-based method is firstly employed to preprocess the measurement data, which can strengthen the effective information of PEMFC degradation data at the same time remove the noise and spikes that interfere with degradation prediction. As a deep structural model, DGP has strong feature learning ability which can represent the nonlinear details of degradation data and give more accurate prediction results. At the same time, it serves as a probabilistic model that can provide the confidence interval to enhance reliability of RUL prediction. The effectiveness of the proposed method is evaluated by experimental data of the PEMFCs under steady-state conditions, and the results show that the SSA-DGP method has higher accuracy and reliability than conventional methods.     

Performance degradation prediction of proton exchange membrane fuel cell using a hybrid prognostic approach

The proton exchange membrane fuel cell has been widely used for industrial systems; however, its performance gradually degrades during use. Therefore, the study on the performance degradation prediction of fuel cells is helpful to extend its lifespan. In this paper, a novel hybrid approach using a combination of model-based adaptive Kalman filter and data-driven NARX neural network is proposed to predict the degradation of fuel cells. The overall degradation trend (i.e., irreversible degradation process) is captured by an empirical aging model and adaptive Kalman filter. Meanwhile, the detail degradation information (i.e., reversible degradation process) is depicted by the NARX neural network. Moreover, the correlation analysis of the reversible voltage time series is carried out to obtain the number of delays of the NARX neural network based on the autocorrelation function and the partial autocorrelation function. Then, the total degradation prediction is the sum of the overall degradation prediction and the detail degradation prediction. Finally, the prognostic capability of the proposed method is verified by two aging datasets, and the results show the effectiveness and superiority of the proposed method which can provide accurate degradation forecasting and remaining useful life.     

Impact of applied cell voltage on the performance of a microbial electrolysis cell fully catalysed by microorganisms

The effect of the operating voltage on the performance of a microbial electrolysis cell (MEC) equipped with both a bioanode and a biocathode for hydrogen production is reported. Chronoamperometry tests ranged between 0.3 and 2.0 V were carried out after both bioelectrodes were developed. A maximum current density up to 1.6 A m⁻² was recorded at 1.0 V with hydrogen production rate of nearly 6.0 ± 1.5 L m⁻² cathode day⁻¹. Trace amounts of methane, acetone and formate were detected in cathode's headspace and catholyte which followed the same trend as hydrogen production rate. Meanwhile substrate consumption in anolyte also followed the trend of hydrogen production and current density changes. The bioanode could utilise up to 95% of acetate in the tested voltage ranges, however, at a cell voltage of 2.0 V the bioanode's activity stopped due to oxygen evolution from water hydrolysis. Cyclic voltammograms revealed that the bioanode activity was vital to maintain the functionality of the whole system. The biocathode relied on the bioanode to maintain its potential during the hydrogen evolution. The overall energy efficiency recovered from both bioanode and external power in terms of hydrogen production at the cathode was determined as 29.4 ± 9.0%, within which substrate oxidation contributed up to nearly 1/3 of the total energy marking the importance of bioanode recovering energy from wastewater to reduce the external power supply.     

Role of Nd-Ni on structural,spectral and dielectric properties of strontium-barium based nano-sized X-type ferrites

The influence of neodymium and nickel substitution on structural and dielectric parameters was investigated in strontium-barium X-type hexagonal ferrites having composition SrBaCu_2?xNi_xNd_yFe_28?yO₄₆ (x = 0, 0.2, 0.4, 0.6, 0.8, 1 and y = 0, 0.02, 0.04, 0.06, 0.08, 0.1). Sol-gel method was employed for synthesizing these hexagonal ferrites. The XRD plots of all studied materials which were annealed at 1250 °C show single phase characteristics. Lattice parameter ‘c’ increased as a consequence of larger radius of rare earth ion (Nd³⁺) as compared to (Fe³⁺), while lattice parameter ‘a’ showed very small variation. The cell volume was obtained in the range 2508.32–2523.75 (ų). The inclusion of Nd-Ni also affected X-ray density, bulk density and porosity. The FTIR spectroscopy indicated the particular absorption peaks of hexagonal ferrites and it was performed in the range of 500–700 cm^?1. On account of Nd-Ni doping, the dielectric constant, dielectric loss and AC-conductivity showed decreasing trend. The occupancy of Nd³⁺ ions at octahedral site impedes the valence alternation of Fe³⁺; therefore there was decrease in dielectric permittivity. Ac conductivity has been decreased from 9.14 to 6.49 (Ω cm)^?1 at frequency of 2.7 GHz. The Cole-Cole plots of synthesized materials noticeably revealed grain boundary contribution. The appearance of single semi-circle in impedance Cole-Cole graphs confirms the exceptional role of grain boundaries in the conduction process. The considerably lower dielectric parameters of investigated nano X-type ferrites propose their feasibility for high-frequency applications (phase shifters, dielectric resonators, stealth technology etc).     

基于特征贡献率的机械故障分类方法

为了使7075铝合金薄壁件表面喷丸加工应力得到松弛和均化，运用ANSYS建立平台式振动时效有限元模型，通过振动模态和谐响应分析，获得最佳激振频率和试样装夹位置。在此基础上，将薄壁框架件置于平台上进行振动时效处理，以验证仿真分析结果，评价其对试样表面应力作用效果。结果表明：在亚共振频率为112 Hz时，试样在平台零振幅和最大弯曲位置处分别对称布置，时效后表面均形成应力松弛，松弛率分别为29.5%和33.3%，不均匀波动分别为20.3%和20.7%，而沿振动方向松弛程度不均匀，说明激振力造成了材料表面组织屈服，且屈服程度与应力强度有关。提出再时效-位置交叠方法，使应力松弛不均匀度分别下降到9.9%和15.9%，应力均化得到改善，该研究工作为薄壁类零件表面应力均匀化提供了参考。     

Remaining useful life prediction for degrading systems with random shocks considering measurement uncertainty

Degradation data have been widely used for the remaining useful life (RUL) prediction of systems. Most existing works apply a preset model to capture the degradation process and focus on the degradation process without shocks or constant shock effects. More generally, the actual degradation path is unobservable due to the existence of measurement uncertainty, which interferes with the determination of the degradation model. Besides, the effect of random shocks is usually fluctuating. Given these problems, a general degradation model with the random shock fluctuant effects considering the measurement uncertainty is first developed to describe the degradation process, and a two-step approach combining the arithmetic average filter and the Bayesian information criterion is adopted to identify the degradation path. Subsequently, the transfer processes of the actual degradation state and the abrupt change caused by shocks are depicted using a two-dimensional state-space model, and an expectation-maximization algorithm combined with the particle filtering is developed for parameter estimation. Furthermore, the explicit solution of RUL distribution is obtained when only considering harmful shocks, while a simulation method of RUL distribution is provided when both harmful and beneficial shocks exist. Finally, the effectiveness of the proposed method is verified by a numerical example and two practical case studies.     

奶酪中内酯类物质风味贡献及其生物合成调控进展

奶酪中的内酯类物质主要包括γ-内酯和δ-内酯两大类，它们是奶酪关键气味物质之一，对于奶酪整体风味的形成有着重要作用。本文在查阅相关文献的基础上，对常见奶酪中内酯类物质的组成、香气贡献以及主要内酯物质的浓度范围进行了系统的综述；介绍了GC外标法、峰面积归一化法、超高效液相色谱-串联质谱法、GC-MS内标法、选择离子流动管质谱法、稳定同位素稀释分析法六种内酯类物质主要的定量测定方法；总结了奶酪中内酯类物质的形成机理以及研究者们提出的生物合成调控策略；并对当前研究中存在的问题以及未来研究方向做了归纳与展望。为全面认知奶酪中内酯类香气化合物及其形成和调控，开发更适合国人口味原制奶酪，实现我国奶酪产业转型和升级提供理论参考。