Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery

Online Prediction of Remaining Useful Life for Li-Ion Batteries Based on Discharge Voltage Data

The state of health and remaining useful life of lithium-ion batteries are key indicators for the normal operation of electrical devices. To address the problem of the capacity of lithium-ion batteries being difficult to measure online, in this paper, we propose an online method based on particle swarm optimization and support vector regression to estimation the state of health and remaining useful life. First, a novel health indicator is extracted from the discharge voltage to characterize the capacity of lithium-ion batteries. Then, based on the capacity degradation characteristics, support vector regression is used to predict the remaining useful life of these batteries, and particle swarm optimization is selected to optimize the parameters of the support vector regression, which effectively enhances the predictive performance of the model. Validated for the NASA battery aging dataset, when training with the first 40% of the dataset, the maximum error of the predicted remaining useful life was four cycles, and when training with the first 50% of the dataset, the maximum error of the predicted remaining useful life was only one cycle. When comparing to a deep neural network, support vector regression, long short-term memory algorithms and existing similar methods in the literature, the particle swarm optimization and support vector regression method can obtain more accurate prediction results.

Nondestructive multiplex detection of foodborne pathogens with background microflora and symbiosis using a paper chromogenic array and advanced neural network

We have developed an inexpensive, standardized paper chromogenic array (PCA) integrated with a machine learning approach to accurately identify single pathogens (Listeria monocytogenes, Salmonella Enteritidis, or Escherichia coli O157:H7) or multiple pathogens (either in multiple monocultures, or in a single cocktail culture), in the presence of background microflora on food. Cantaloupe, a commodity with significant volatile organic compound (VOC) emission and large diverse populations of background microflora, was used as the model food. The PCA was fabricated from a paper microarray via photolithography and paper microfluidics, into which 22 chromogenic dye spots were infused and to which three red/green/blue color-standard dots were taped. When exposed to VOCs emitted by pathogens of interest, dye spots exhibited distinguishable color changes and pattern shifts, which were automatically segmented and digitized into a ΔR/ΔG/ΔB database. We developed an advanced deep feedforward neural network with a learning rate scheduler, L₂ regularization, and shortcut connections. After training on the ΔR/ΔG/ΔB database, the network demonstrated excellent performance in identifying pathogens in single monocultures, multiple monocultures, and in cocktail culture, and in distinguishing them from the background signal on cantaloupe, providing accuracy of up to 93% and 91% under ambient and refrigerated conditions, respectively. With its combination of speed, reliability, portability, and low cost, this nondestructive approach holds great potential to significantly advance culture-free pathogen detection and identification on food, and is readily extendable to other food commodities with complex microflora.

State of Health Prediction of Lithium-Ion Batteries Based on the Discharge Voltage and Temperature

Accurate state of health (SOH) prediction of lithium-ion batteries is essential for battery health management. In this paper, a novel method of predicting the SOH of lithium-ion batteries based on the voltage and temperature in the discharging process is proposed to achieve the accurate prediction. Both the equal voltage discharge time and the temperature change during the discharge process are regarded as health indicators (HIs), and then, the Pearson and Spearman relational analysis methods are applied to evaluate the relevance between HIs and SOH. On this basis, we modify the relevance vector machine (RVM) to a multiple kernel relevance vector machine (MKRVM) by combining Gaussian with sigmoid function to improve the accuracy of SOH prediction. The particle swarm optimization (PSO) is used to find the optimal weight and kernel function parameters of MKRVM. The aging data from NASA Ames Prognostics Center of Excellence are used to verify the effectiveness and accuracy of the proposed method in numerical simulations, whose results show that the MKRVM method has higher SOH prediction accuracy of lithium-ion batteries than the relevant methods.

Remaining Useful Life Prediction of MOSFETs via the Takagi–Sugeno Framework

The paper presents a new method of predicting the remaining useful life of technical devices. The proposed soft computing approach bridges the gap between analytical and data-driven health prognostic approaches. Whilst the former ones are based on the classical exponential shape of degradation, the latter ones learn the degradation behavior from the observed historical data. As a result of the proposed fusion, a practical method for calculating components’ remaining useful life is proposed. Contrarily to the approaches presented in the literature, the proposed ensemble of analytical and data-driven approaches forms the uncertainty interval containing an expected remaining useful life. In particular, a Takagi–Sugeno multiple models-based framework is used as a data-driven approach while an exponential curve fitting on-line approach serves as an analytical one. Unlike conventional data-driven methods, the proposed approach is designed on the basis of the historical data that apart from learning is also applied to support the diagnostic decisions. Finally, the entire scheme is used to predict power Metal Oxide Field Effect Transistors’ (MOSFETs) health status. The status of the currently operating MOSFET is determined taking into consideration the knowledge obtained from the preceding MOSFETs, which went through the run-to-failure process. Finally, the proposed approach is validated with the application of real data obtained from the NASA Ames Prognostics Data Repository.

A Data-Driven Aero-Engine Degradation Prognostic Strategy

Degradation prognostics of aero-engine are a well-recognized challenging issue. Data-driven prognostic techniques have been receiving attention because they rely on neither expert knowledge nor mathematic model of the system. But they are highly dependent on the quantity and quality of degradation data. To solve the problems caused by unlabeled, unbalanced condition monitoring (CM) data and uncertainties of the prognostics process, a novel data-driven aero-engine degradation prognostic strategy is proposed in this article. First, two indicators are defined to remove redundant degradation features. Then, the number of discrete states of health is determined by a fuzzy c -means algorithm, and the health state labels can be automatically assigned for health state estimation, where the uncertain initial condition and the uncertainty of health state's transition are fully considered. Finally, a multivariate health estimation model and a multivariate multistep-ahead long-term degradation prediction model are proposed for remaining useful life estimation for aero-engines. Verification results using the aero-engine data from NASA can show that the proposed data-driven degradation prognostic strategy is effective and feasible.

Ensemble Model Based on Stacked Long Short-Term Memory Model for Cycle Life Prediction of Lithium–Ion Batteries

To meet the target value of cycle life, it is necessary to accurately assess the lithium–ion capacity degradation in the battery management system. We present an ensemble model based on the stacked long short-term memory (SLSTM), which is used to predict the capacity cycle life of lithium–ion batteries. The ensemble model combines LSTM with attention and gradient boosted regression (GBR) models to improve prediction accuracy, where these individual prediction values are used as input to the SLSTM model. Among 13 cells, single and multiple cells were used as the training set to verify the performance of the proposed model. In seven single-cell experiments, 70% of the data were used for model training, and the rest of the data were used for model validation. In the second experiment, one cell or two cells were used for model training, and other cells were used as test data. The results show that the proposed method is superior to individual and traditional integrated learning models. We used Monte Carlo dropout techniques to estimate variance and obtain prediction intervals. In the second experiment, the average absolute percentage errors for GBR, LSTM with attention, and the proposed model are 28.6580, 1.7813, and 1.5789, respectively.

Long Term Memory Assistance for Evolutionary Algorithms

Short term memory that records the current population has been an inherent component of Evolutionary Algorithms (EAs). As hardware technologies advance currently, inexpensive memory with massive capacities could become a performance boost to EAs. This paper introduces a Long Term Memory Assistance (LTMA) that records the entire search history of an evolutionary process. With LTMA, individuals already visited (i.e., duplicate solutions) do not need to be re-evaluated, and thus, resources originally designated to fitness evaluations could be reallocated to continue search space exploration or exploitation. Three sets of experiments were conducted to prove the superiority of LTMA. In the first experiment, it was shown that LTMA recorded at least 50 % more duplicate individuals than a short term memory. In the second experiment, ABC and jDElscop were applied to the CEC-2015 benchmark functions. By avoiding fitness re-evaluation, LTMA improved execution time of the most time consuming problems F 03 and F 05 between 7% and 28% and 7% and 16%, respectively. In the third experiment, a hard real-world problem for determining soil models’ parameters, LTMA improved execution time between 26% and 69%. Finally, LTMA was implemented under a generalized and extendable open source system, called EARS. Any EA researcher could apply LTMA to a variety of optimization problems and evolutionary algorithms, either existing or new ones, in a uniform way.

Special Issue on Applications of Artificial Neural Networks for Energy Systems

In the 21st century, newer and cleaner technologies like renewable energy technologies, fuel cells, and smart grids are promising to transform the way we generate and distribute electric energy [...]

Bayesian Estimation of Residual Life for Weibull-Distributed Components of On-Orbit Satellites Based on Multi-Source Information Fusion

Residual life estimation is an important problem in reliability engineering. Traditional methods, which are based on time-to-failure distribution, have limitations for components of on-orbit satellites characterized as high reliability with small sample size. Various types of reliability information can be collected during test and operation, including historical lifetime data, degradation data, similar data, expert information, etc. Therefore, making full use of multi-source information is meaningful for improving estimation precision. However, research on residual life estimation by fusing multi-source information is rare. No study has examined the overall process of fusing all of the different kinds of information. In this paper, a Bayesian method is presented to estimate the residual life of Weibull-distributed components of on-orbit satellites by fusing all the collected information. Prior distributions are determined using different kinds of information. After fusing the field data, posterior distributions can be obtained corresponding to each prior distribution. Then, the joint posterior distribution is the weighted sum of these posterior distributions with weights calculated using the second Maximum Likelihood Estimation (ML-II) method. Consistency is tested to guarantee the safety of the information fusion. Furthermore, residual life is estimated by the proposed sample-based method including both the Bayesian estimate and credible interval (CI). A Monte Carlo simulation study is conducted to demonstrate the proposed methods and shows that the Bayesian method is satisfactory and robust. Finally, a published dataset of the momentum wheel in a satellite is analyzed to illustrate the application of the method.

Dynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network

In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)–LSTM network, the proposed model has higher forecast accuracy.