Search Results (45)

This paper presents the design, implementation, and experimental validation of a Condition Monitoring (CM) circuit for SiC-based Power Electronics Converters (PECs). The paper leverages in situ drain–source resistance (R_ds,on) measurements, interfaced with cloud connectivity for data processing and lifetime assessment, addressing key limitations in current state-of-the-art (SOTA) methods. Traditional approaches rely on expensive data acquisition systems under controlled laboratory conditions, making them unsuitable for real-world applications due to component variability, time delay, and noise sensitivity. Furthermore, these methods lack cloud interfacing for real-time data analysis and fail to provide comprehensive reliability metrics such as Remaining Useful Life (RUL). Additionally, the proposed CM method benefits from noise mitigation during switching transitions by utilizing delay circuits to ensure stable and accurate data capture. Moreover, collected data are transmitted to the cloud for long-term health assessment and damage evaluation. In this paper, experimental validation follows a structured design involving signal acquisition, filtering, cloud transmission, and temperature and thermal degradation tracking. Experimental testing has been conducted at different temperatures and operating conditions, considering coolant temperature variations (40 °C to 80 °C), and an output power of 7 kW. Results have demonstrated a clear correlation between temperature rise and R_ds,on variations, validating the ability of the proposed method to predict device degradation. Finally, by leveraging cloud computing, this work provides a practical solution for real-world Wide Band Gap (WBG)-based PEC reliability and lifetime assessment. Full article

Proton exchange membrane fuel cells (PEMFCs) are ideal for fuel cell vehicles due to their high specific power, rapid start-up, and low operating temperatures. However, their limited lifespan presents a challenge for large-scale deployment. Accurate assessment of remaining useful life (RUL) is essential for enhancing longevity. Automotive PEMFC systems are complex and nonlinear, making lifespan prediction difficult. Recent studies suggest deep learning approaches hold promise for this task. This study proposes a novel EMD-TCN-GN algorithm, which, for the first time, integrates empirical mode decomposition (EMD), temporal convolutional network (TCN), and group normalization (GN) by using EMD to adaptively decompose non-stationary signals (such as voltage fluctuations), the dilated convolution of TCN to capture long-term dependencies, and combining GN to group-calibrate intrinsic mode function (IMF) features to solve the problems of modal aliasing and training instability. Parametric analysis shows optimal accuracy with the grouping parameter set to 4. Experimental validation, with a voltage lifetime threshold at 96% (3.228 V), shows the predicted degradation closely aligns with actual results. The model predicts voltage threshold times at 809 h and 876 h, compared to actual values of 807 h and 872 h, with a temporal prediction error margin of 0.250–0.460%. These results demonstrate the model’s high prediction fidelity and support proactive health management of PEMFC systems. Full article

To solve the control problem of the performance degradation of proton exchange membrane fuel cells (PEMFCs), a novel control framework based on the performance degradation is proposed. This control framework introduces the results of the state of health (SoH) estimation and remaining useful lifetime (RUL) prediction, which were used for the controller design because they determine the PEMFC output power. Furthermore, the information of SoH and RUL could be reflected the PEMFC health state and provided maintenance recommendations. The desired power of the stack was obtained, which was used as the real-time desired power of the PEMFC system by synthesizing the RUL, SoH, and ECU information of the stack. The results showed that when the PEMFC system used the designed control framework, the RUL and SoH information could be provided. The stack temperature showed an increasing and then decreasing trend, which indicates that the stack temperature was still controllable by controlling the speeds of the pump and fan. Full article

Predictive maintenance, recognized as an effective health management strategy for extending the lifetime of devices, has emerged as a hot research topic in recent years. A general method is to execute two separate steps: data-driven remaining useful life (RUL) prediction and a maintenance strategy. However, among the numerous studies that conducted maintenance and replacement activities based on the results of RUL prediction, little attention has been paid to the impact of preventive maintenance on sensor-based monitoring data, which further affects the RUL for repairable degrading devices. In this paper, an adaptive RUL prediction method is proposed for repairable degrading devices in order to improve the accuracy of prediction results and achieve adaptability to future degradation processes. Firstly, a phased degradation model based on an adaptive Wiener process is established, taking into account the impact of imperfect maintenance. Meanwhile, integrating the impact of maintenance activities on the degradation rate and state, the probability distribution of RUL can be derived based on the concept of first hitting time (FHT). Secondly, a method is proposed for model parameter identification and updating that incorporates the individual variation among devices, integrating maximum likelihood estimation and Bayesian inference. Finally, the effectiveness of the RUL prediction method is ultimately validated through numerical simulation and its application to repairable gyroscope degradation data. Full article

Predicting the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and availability of mechanical systems. Constructing health indicators (HIs) is a fundamental step in the methodology for predicting the RUL of rolling bearings. Traditional HI construction often involves determining the degradation stage of the bearing by extracting time–frequency domain features from raw data using a priori knowledge and setting artificial thresholds; this approach does not fully utilize the vibration information in the bearing data. In order to address the above problems, this paper proposes an Asymmetric Residual Shrinkage Convolutional Autoencoder (ARSCAE) model. The asymmetric structure of the ARSCAE model is characterized by the soft thresholding of signal features in the encoder part to achieve noise reduction. The decoder part consists of convolutional and pooling layers for data reconstruction. This model can directly construct HIs from the original vibration signals collected, and comparisons with other models show that it constructs better HIs from the original vibration signals. Finally, experiments on the FEMTO dataset show that the results indicate that the HIS constructed by the ARSCAE model has better lifetime prediction capability compared to other methods. Full article

In the context of predicting the remaining useful life (RUL) of rolling bearings, many models often encounter challenges in identifying the starting point of the degradation stage, and the accuracy of predictions is not high. Accordingly, this paper proposes a technique that utilizes particle swarm optimization (PSO) in combination with the fusing of a one-dimensional convolutional neural network (CNN) and a multihead self-attention (MHSA) bidirectional long short-term memory (BiLSTM) network called PSO-CNN-BiLSTM-MHSA. Initially, the original signals undergo correlation signal processing to calculate the features, such as standard deviation, variance, and kurtosis, to help identify the beginning location of the rolling bearing degradation stage. A new dataset is constructed with similar degradation trend features. Subsequently, the particle swarm optimization (PSO) algorithm is employed to find the optimal values of important hyperparameters in the model. Then, a convolutional neural network (CNN) is utilized to extract the deterioration features of rolling bearings in order to predict their remaining lifespan. The degradation features are inputted into the BiLSTM-MHSA network to facilitate the learning process and estimate the remaining lifespan of rolling bearings. Finally, the degradation features are converted to the remaining usable life (RUL) via the fully connected layer. The XJTU-SY rolling bearing accelerated life experimental dataset was used to verify the effectiveness of the proposed method by k-fold cross-validation. After comparing our model to the CNN-LSTM network model and other models, we found that our model can achieve reductions in mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of 9.27%, 6.76%, and 2.35%, respectively. Therefore, the experimental results demonstrate the model’s accuracy in forecasting remaining lifetime and support its ability to forecast breakdowns. Full article

The low-voltage electric machine (EM) is a core technology for transportation electrification, and features like high power density and compact volume are essential prerequisites. However, these requirements are usually in conflict with the reliability property of EM, especially in the safety-critical industry such as aviation. Therefore, an appropriate balance between high-performance and reliability needs to be found. Often, the over-engineering method is applied to ensure safety, although it might have a detrimental effect on the EM volume. To address this issue, the EM reliability assessment is included at the EM design stage through the physics of failure (PoF) theory. In EMs, the windings play a key role in electromechanical energy conversion, but their insulation system is subject to frequent failure and represents a reliability bottleneck. Therefore, in-depth research on the root causes of insulation breakdown is beneficial for EM reliability improvement purposes. Indeed, increasing awareness and knowledge on the mechanism of the insulation degradation process and the related lifetime modeling enables the growth of appropriate tools for achieving reliability targets since the first EM design steps. In this work, the main aspects of the insulation system, in terms of materials and manufacturing, are first reviewed. Then, the principal stresses experienced by the winding insulation system are deeply discussed with the purpose of building a profound understanding of the PoF. Finally, an overview of the most common insulation lifetime prediction models is presented, and their use for accomplishing the reliability-oriented design (RoD) and the remaining useful life (RUL) estimation are examined. Full article

Predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is crucial to preventing system failures and enhancing operational performance. Knowing the RUL of a battery enables one to perform preventative maintenance or replace the battery before its useful life expires, which is vital in safety-critical applications. The prediction of the RUL of Li-ion batteries plays a critical role in their optimal utilization throughout their lifetime and supporting sustainable practices. This paper conducts a comparative analysis to assess the effectiveness of multiple machine learning (ML) models in predicting the capacity fade and RUL of Li-ion batteries. Three case studies are analyzed to assess the performances of the state-of-the-art ML models, considering two distinct datasets. These case studies are conducted under various operating conditions such as temperature, C-rate, state of charge (SOC), and depth of discharge (DOD) of the batteries in Cases 1 and 2, and a different set of features and charging policies for the second dataset in Case 3. Meanwhile, diverse extracted features from the initial cycles of the second dataset are considered in Case 3 to predict the RUL of Li-ion batteries in all cycles. In addition, a multi-feature multi-target (MFMT) feature mapping is introduced to investigate the performance of the developed ML models in predicting the battery capacity fade and RUL in the entire life cycle. Multiple ML models that are developed for the comparison analysis in the proposed methodology include Random Forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), multi-layer perceptron (MLP), long short-term memory (LSTM), and attention-LSTM. Furthermore, hyperparameter tuning is applied to improve the performance of the XGBoost and LightGBM models. The results demonstrate that the extreme gradient boosting with hyperparameter tuning (XGBoost-HT) model outperforms the other ML models in terms of the root-mean-squared error (RMSE) and mean absolute percentage error (MAPE) of the battery capacity fade and RUL for all cycles. The obtained RMSE and MAPE values for XGBoost-HT in terms of cycle life are 69 cycles and 6.5%, respectively, for the third case. In addition, the XGBoost-HT model handles the MFMT feature mapping within an acceptable range of RMSE and MAPE, compared to the rest of the developed ML models and similar benchmarks. Full article

Reducing greenhouse emissions can be done via the electrification of the transport industry. However, there are challenges related to the electrification such as the lifetime of vehicle batteries as well as limitations on the charging possibilities. To cope with some of these challenges, a charge scheduling method for fleets of electric vehicles is presented. Such a method assigns the charging moments (i.e., schedules) of fleets that have more vehicles than chargers. While doing the assignation, the method also maximizes the total Remaining Useful Life (RUL) of all the vehicle batteries. The method consists of two optimization algorithms. The first optimization algorithm determines charging profiles (i.e., charging current vs time) for individual vehicles. The second algorithm finds the charging schedule (i.e., the order in which vehicles are connected to a charger) that maximizes the RUL in the batteries of the entire fleet. To reduce the computational effort of predicting the battery RUL, the method uses a Machine Learning (ML) model. Such a model predicts the RUL of an individual battery while taking into account common stress factors and fabrication-related differences per battery. Simulation results show that charging a single vehicle as late as possible maximizes the RUL of that single vehicle, due to the lower battery degradation. Simulations also show that the ML model accurately predicts the RUL, while taking into account fabrication-related variability in the battery. Additionally, it was shown that this method schedules the charging moments of a fleet, leading to an increased total RUL of all the batteries in the vehicle fleet. Full article

In today’s industrial environment, effectively monitoring assets throughout their entire lifetime is essential. Prognostic and Health Management (PHM) is a powerful tool that enables users to achieve this goal. Recently, sensor-equipped actuator electrification has been introduced to capture intrinsic key system variables as time series. This data flow has opened up new possibilities for extracting essential maintenance information. To leverage the full potential of these data, we have developed a novel algorithm for time series registration, which serves as the core of a new similarity-based prognostic method in a PHM context: Partial Time Scaling Invariant Temporal Alignment for Remaining Useful Life Estimation (PARTITA-RULE). Our algorithm transforms acceleration signals into a subset of descriptors for a new actuator, creating a time series. We can extract valuable maintenance information by aligning this time series with the one already labeled from past behaviors of the same actuator’s family of heterogeneous sizes and robust scaling factors. The unique aspect of our method is that we do not need to inject prior knowledge for registration intervals at this stage. Once the unknown series is aligned with all possible candidates, we create a weighting scheme to assign a relevance score with an uncertainty measurement for each aligned pair. Finally, we compute interpolants on the Wasserstein space to obtain the asset’s Remaining Useful Life (RUL). It is important to note that a relevant result in a PHM context requires a database filled with different labeled system behaviors. To test the effectiveness of our method, we use an industrial data set of vibration signals captured on an aeronautical electric actuator. Our method shows promising Remaining Useful Life (RUL) estimation results even with incomplete time segments. Full article

Turbine blades are crucial components exposed to harsh conditions, such as high temperatures, high pressures, and high rotational speeds. It is of great significance to accurately predict the life of blades for reducing maintenance cost and improving the reliability of gas turbine systems. A rapid and accurate blade life assessment method holds significant importance in the maintenance plan of gas turbine engines. In this paper, a novel on-line remaining useful life (RUL) prediction method for high-temperature blades is proposed based on 3D reconstruction technology and data-driven surrogate mode. Firstly, the 3D reconstruction technology was employed to establish the geometric model of real turbine blades, and the fluid–thermal–solid analysis under actual operational conditions was carried out in ANSYS software. Six checkpoints were selected to estimate the RUL according to the stress–strain distribution of the blade surface. The maximum equivalent stress was 1481.51 MPa and the highest temperature was 1393.42 K. Moreover, the fatigue-creep lifetime was calculated according to the parameters of the selected checkpoints. The RUL error between the simulation model and commercial software (Control and Engine Health Management (CEHM)) was less than 0.986%. Secondly, different data-driven surrogate models (BP, DNN, and LSTM algorithms) were developed according to the results from numerical simulation. The maximum relative errors of BP, DNN, and LSTM models were 0.030%, 0.019%, and 0.014%. LSTM demonstrated the best performance in predicting the RUL of turbine blades with time-series characteristics. Finally, the LSTM model was utilized for predicting the RUL within a gas turbine real operational process that involved five start–stop cycles. Full article

Lithium-ion battery health and remaining useful life (RUL) are essential indicators for reliable operation. Currently, most of the RUL prediction methods proposed for lithium-ion batteries use data-driven methods, but the length of training data limits data-driven strategies. To solve this problem and improve the safety and reliability of lithium-ion batteries, a Li-ion battery RUL prediction method based on iterative transfer learning (ITL) and Mogrifier long and short-term memory network (Mogrifier LSTM) is proposed. Firstly, the capacity degradation data in the source and target domain lithium battery historical lifetime experimental data are extracted, the sparrow search algorithm (SSA) optimizes the variational modal decomposition (VMD) parameters, and several intrinsic mode function (IMF) components are obtained by decomposing the historical capacity degradation data using the optimization-seeking parameters. The highly correlated IMF components are selected using the maximum information factor. Capacity sequence reconstruction is performed as the capacity degradation information of the characterized lithium battery, and the reconstructed capacity degradation information of the source domain battery is iteratively input into the Mogrifier LSTM to obtain the pre-training model; finally, the pre-training model is transferred to the target domain to construct the lithium battery RUL prediction model. The method’s effectiveness is verified using CALCE and NASA Li-ion battery datasets, and the results show that the ITL-Mogrifier LSTM model has higher accuracy and better robustness and stability than other prediction methods. Full article

This research work implements an initial methodology for the assessment of Battery Energy Storage Systems (BESSs) based on Remaining Useful Lifetime (RUL), and its main contribution is the modeling and estimation of Health and Charge indicators through regression algorithms and binary classifiers during the battery’s operation. Linear Regression, Ridge Regression, and Lasso Regression are the main algorithms for modeling the State of Health (SOH), while Decision Tree, Naïve Bayes, and Logistic Regression are implemented as binary classifiers to estimate the charge and discharge during battery operation. Additional data science techniques are executed to provide feature selection, validation, and metrics of performance. The results show that binary classifiers achieve a remarkable accuracy, around 95% for charge and discharge predictions, which is supported by experimental battery measurements. Similarly, regression algorithms achieve accuracy results around 97% and provide a basis for determining the Remaining Useful Lifetime (RUL) according to the End-of-Life (EOL) criteria of a BESS. Full article

The accurate prediction of the remaining useful life (RUL) of aircraft engines is crucial for improving engine safety and reducing maintenance costs. To tackle the complex issues of nonlinearity, high dimensionality, and difficult-to-model degradation processes in aircraft engine monitoring parameters, a new method for predicting the RUL of aircraft engines based on the random forest algorithm and a Bayes-optimized multilayer perceptron (MLP) was proposed here. First, the random forest algorithm was used to evaluate the importance of historical monitoring parameters of the engine, selecting the key features that significantly impact the engine’s lifetime operation cycle. Then, the single exponent smoothing (SES) algorithm was introduced for smoothing the extracted features to reduce the interference of original noise. Next, an MLP-based RUL prediction model was established using a neural network. The Bayes’ online parameter updating formula was used to solve the objective function and return the optimal parameters of the MLP training model and the minimum value of the evaluation index RMSE. Finally, the probability density function of the predicted RUL value of the aircraft engine was calculated to obtain the RUL prediction results.The effectiveness of the proposed method was verified and analyzed using the C-MAPSS dataset for turbofan engines. Experimental results show that, compared with several other methods, the RMSE of the proposed method in the FD001 test set decreases by 6.1%, demonstrating that the method can effectively improve the accuracy of RUL prediction for aircraft engines. Full article

Spindles are key components of machine tools. An efficient estimation of the spindle condition and its prognosis can improve production efficiency and quality due to predictive maintenance planning. This paper proposes a method for predicting the remaining useful life (RUL) of machine tool spindle bearings using a combined calculation and experimental approach. The calculation model based on the ISO 281 standard uses monitored real loading conditions caused by the machining process and the machine tool operation. The model enables the updated calculation of the spindle lifetime L_10h using real load distribution. Since the operation hours of the spindle are also monitored, the remaining useful life (RUL) of the spindle can be calculated. This RUL value is corrected using a bearing condition assessment based on the effective value of the vibration velocity RMS according to the ISO 20816 standard and measured data from the machine tool control system. The proposed method is tested on two different spindle types featuring three pieces of every type. The experimental results of six spindles are compared and validated with a concurrent blind evaluation conducted by a skilled expert. The validation shows a very good match of the proposed method and the expert opinion. The method combining a calculation of the spindle lifetime using monitored real load distribution and subsequent result correction using vibration signal enables the implementation of a full automated estimation of the spindle RUL. Full article