Performance Degradation Trend Research Articles

Performance degradation trend prediction of proton exchange membrane fuel cell based on GA-TCN

To improve the prediction accuracy of the performance degradation trend of proton exchange membrane fuel cell (PEMFC), this paper proposes a temporal convolutional network (TCN) model based on genetic algorithm (GA) optimization to predict the performance degradation trend of PEMFC. Firstly, variational mode decomposition and wavelet threshold denoising algorithms are used to denoise the original data. Then the hyperparameters of the TCN model are optimized by GA, and the GA-TCN model for predicting the performance degradation trend of PEMFC is constructed. Finally, this paper uses the PEMFC stack degradation experimental dataset disclosed in the IEEE PHM 2014 Data Challenge to verify, and compares the proposed model with the backpropagation neural networks model, the long short-term memory model and the classical TCN model. The results show that the proposed method has the highest performance degradation trend prediction accuracy. In particular, when the training dataset accounts for 30%, i.e. the training samples are small, the root mean square error, mean absolute error and mean absolute percentage error of the GA-TCN model are 0.004 726, 0.003 119 and 9.62%, respectively, which are 14.48%, 20.05% and 2.42% lower than that of the classical TCN model. Consequently, this methodology can forecast the degradation trend of PEMFC with high accuracy.

A Novel Data-Driven Approach for Predicting the Performance Degradation of a Gas Turbine

Gas turbines operate under harsh conditions of high temperature and pressure for extended periods, inevitably experiencing performance degradation. Predicting the performance degradation trend of gas turbines and optimizing planned maintenance cycles are crucial for the economic and safety aspects of gas turbine operation. In this study, a novel data-driven approach for predicting gas turbine performance degradation is proposed. Initially, gas turbine operating data are augmented using a mechanism model. Subsequently, a data-driven performance model is constructed based on support vector regression (SVR) and gas turbine operational characteristics, enabling real-time calculation of performance degradation indicators. Building on this, an Autoregressive Neural Network (AR-Net) is employed to construct a model for predicting the trend of performance degradation. The proposed method is applied to predict performance degradation caused by fouling in the compressor of a gas turbine. Comparative analysis with three other performance degradation prediction methods indicates that the proposed approach accurately identifies the performance degradation trend of gas turbines, determining the optimal maintenance timing. This holds significant importance for the condition-based maintenance of gas turbines.

A novel fractional system grey prediction model with dynamic delay effect for evaluating the state of health of lithium battery

Accurately predicting the state of health (SOH) of lithium batteries is critical to improving the energy storage technology of batteries. However, most research focuses solely on the performance degradation trends of lithium batteries during cycling currently, while ignoring the dynamic time lag effects of the influencing factors. Therefore, a novel fractional system grey prediction model with dynamic delay effect is proposed in this paper (abbreviated as FHMGM(1, N)). The fractional-order Huasdorff operator is introduced into the grey prediction model to reflect the dynamic delay between variables in the process of charging and discharging. Meanwhile, the selected grey prediction model with system structure can better characterize the relationships among multiple variables in the system and analyze several main variables that have parallel relationships. In addition, using genetic algorithm to determine the optimal nonlinear parameters can effectively improve the accuracy and stability of the model in the prediction process. Finally, the new model is applied to two types of lithium battery datasets for fitting and prediction, which are compared with the existing grey models and artificial intelligence models. The results show that the new model can better adapt to lithium battery data with different structures, showing good robustness and prediction performance.

1D-SCRN: a novel approach for industrial machinery performance degradation trend prediction

1D-SCRN: a novel approach for industrial machinery performance degradation trend prediction

Early performance degradation of ceramic bearings by a twin-driven model

Early subsurface cracks make significant changes to the dynamic responses of full ceramic ball bearings, and complex space environment brings challenges to the status monitoring of related devices. This paper proposed a model to predict the early degradation performance under complex working conditions driven by digital twin. LSTM recursive neural network was used to establish the mapping relationship between the stiffness weakening factor which represents the degree of damage and stable degradation evaluation index by multi-feature fusion extracted from vibration signals. Digital twin technology was used to combine the mechanism of physical degradation process with real-time working condition data and historical data to predict the early performance degradation trend of ceramic bearings. The results show that the degradation indexes obtained from the fusion of KF, RMS, and MMFD are more effective in reflecting the early degradation stage of ceramic bearings. The twin-driven early performance degradation evaluation model for ceramic bearings can replace the real degradation trend to a certain extent, which promotes the performance degradation evaluation models of key components using ceramic materials.

Prediction of Contact Fatigue Performance Degradation Trends Based on Multi-Domain Features and Temporal Convolutional Networks.

Contact fatigue is one of the most common failure forms of typical basic components such as bearings and gears. Accurate prediction of contact fatigue performance degradation trends in components is conducive to the scientific formulation of maintenance strategies and health management of equipment, which is of great significance for industrial production. In this paper, to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional networks (TCNs) was proposed. Firstly, a multi-domain and high-dimensional feature set of vibration signals was constructed, and performance degradation indexes with good sensitivity and strong trends were initially screened using comprehensive evaluation indexes. Secondly, the kernel principal component analysis (KPCA) method was used to eliminate redundant information among multi-domain features and construct health indexes (HIs) based on a convolutional autoencoder (CAE) network. Then, the performance degradation trend prediction model based on TCN was constructed, and the degradation trend prediction for the monitored object was realized using direct multi-step prediction. On this basis, the effectiveness of the proposed method was verified using a bearing common-use data set, and it was successfully applied to performance degradation trend prediction for rolling contact fatigue specimens. The results show that using KPCA can reduce the feature set from 14 dimensions to 4 dimensions and retain 98.33% of the information in the original preferred feature set. The method of constructing the HI based on CAE is effective, and change processes versus time of the constructed HI can truly reflect the degradation process of rolling contact fatigue specimen performance; this method has obvious advantages over the two commonly used methods for constructing HIs including auto-encoding (AE) networks and gaussian mixture models (GMMs). The model based on TCN can accurately predict the performance degradation of rolling contact fatigue specimens. Compared with prediction models based on long short-term memory (LSTM) networks and gating recurrent units (GRUs), the model based on TCN has better performance and higher prediction accuracy. The RMS error and average absolute error for a prediction step of 3 are 0.0146 and 0.0105, respectively. Overall, the proposed method has universal significance and can be applied to predict the performance degradation trend of other mechanical equipment/parts.

A Comparative Analysis of Software Aging in Image Classifiers on Cloud and Edge

Image classifiers for recognizing real-world objects are widely used in the Internet of Things (IoT) and Cyber-Physical Systems(CPSs). A classifier is trained offline by machine learning algorithms with training data sets, and then it is deployed on a cloud or an edge computing system for online label predictions. As the classifier's performance depends on the underlying software infrastructure, it may degrade over time due to software faults causing software aging. In this paper, we address this issue and experimentally investigate software aging observed in an image classification system that continuously runs on cloud and edge computing environments. We apply several statistical techniques to analyze degradation trends in the systems under stress tests. Our statistical trend analysis confirms the degradation trends in the throughput as well as the available memory resources both in the cloud and the edge environments. Contrary to our expectation, the edge computing environment under test had much less impact on the performance degradation than our cloud environment when the workload is high, although the latter one has four times larger allocated memory resources. We also show that the observed performance degradation trends are associated with the memory usage of specific processes by performing correlation analysis.

Multilayer-graph-embedded extreme learning machine for performance degradation prognosis of bearing

As a key component in electromechanical systems, the health condition monitoring of rolling bearings is crucial for the safe operation of the whole system. For this purpose, the prediction of rolling bearing performance degradation is indispensable. To improve the accuracy of Extreme Learning Machine (ELM) based algorithms for the bearing performance degradation prediction, a novel Graph embedded ELM autoencoder (GEELM-AE) is first constructed via combining the graph embedding framework and then a performance degradation prediction method of Multilayer-Graph-embedded ELM (MGEELM) with a deep framework is developed by stacking multiple GEELM-AEs. The MGEELM algorithm not only extracts the abstract features in the data by virtue of its deep structure, but also maintains the local structural information and non-local structural information of the data during feature extraction. Accordingly, the performance degradation trend of rolling bearings can be accurately predicted with the proposed MGEELM. Moreover, the MGEELM algorithm does not require reverse fine-tuning, which greatly reduces the training time and improves the prediction efficiency. The advantages of the proposed method are validated by rolling bearing life-cycle vibration data. In the experimental analysis, the four performance indicators of the proposed method obviously outperform the comparative methods, which indicates the superior ability of the proposed method in tracking the bearing operating condition evolution.

Performance Degradation Prognosis Based on Relative Characteristic and Long Short-Term Memory Network for Components of Brake Systems of in-Service Trains

During the service life of brake systems, performance degradation of the components is inevitable. In order to grasp the health status of components of brake systems, and aiming at the problem that the performance degradation trend of the components of the brake system is not completely clear due to signal coupling between components, the influence of variable working conditions, and the long performance degradation cycle, a performance degradation prognosis method of the components of the brake system based on relative characteristic (RC) and the long short-term memory (LSTM) network was proposed. The input and output signals of the components were isolated and fused, the working condition-independent RC was extracted to construct the health indicator (HI), and the validity of the HI was tested by using the monotonicity, correlation, and robustness metrics. Moreover, considering the time memory characteristics, the trend prediction of the HI curve of the components of the brake system was carried out based on the LSTM network. Furthermore, data augmentation for the training and testing sets was performed. Taking the typical component of brake systems as an example, a performance degradation test was carried out. The analysis results of the test data show that the accuracy of the performance degradation prognosis of the intake filter was over 99%, which validates the effectiveness and accuracy of the proposed method. The research results could provide a reference for health management and to improve the active safety protection capability of brake systems of in-service trains.

Performance degradation prediction model of rolling bearing based on self-checking long short-term memory network

The prediction of performance degradation is of great significance for the health monitoring of rolling bearings. When predicting the performance degradation trend for the whole-life data of bearings, there are problems such as long prediction time, high cost and single evaluation criteria for prediction effect, which hinder the accuracy of degradation prediction. To this end, a self-checking long and short-term memory (Sc-LSTM) prediction model is proposed in this paper for predicting the performance degradation trend of bearings. First, Relation was used as a performance degradation indicator and improved using the mean and normalization methods. This approach can effectively alleviate the problems of long forecasting time and high cost. Secondly, the long and short-term memory (LSTM) sets up many different prediction schemes and proposes a test layer by segmental prediction and comparison to select the appropriate test index σ. According to the robust stability of the self-checking method and the high accuracy of LSTM nonlinear prediction, an Sc-LSTM performance degradation prediction model is established. The model introduces the prediction effect assessment of prediction pass rate η to make up for the problem of inadequate assessment of prediction effect by a single error. Finally, using the Sc-LSTM model, relation is used to perform segmental predictive analysis and experimental validation of the overall prediction. Experiments show that the model can effectively improve the prediction accuracy and is feasible in the prediction of bearing performance degradation.

K-Means Clustering and Bidirectional Long- and Short-Term Neural Networks for Predicting Performance Degradation Trends of Built-In Relays in Meters.

The built-in relay in a meter is a key control component of a smart meter, and its reliability determines whether the user can use electricity safely and smoothly. In this paper, the degradation characteristics of the arc-burning energy are enhanced by the method of K-means clustering to replace degradation data, such as the overtravel time, release time, and other data. In existing methods, the meter needs to be disassembled to describe the degradation trend of the meter relay. The proposed method is combined with a bidirectional long short-term memory (Bi-LSTM) neural network to predict the degradation trend of the relay's performance. In this paper, K-means clustering is used to enhance the extraction of arc energy data features, and then the arc energy data obtained from the reliability lifetime test is assessed to predict the degradation trend of the meter relay by means of a bidirectional LSTM.

Experimental research on performance degradation of TSV microstructure under thermal cycling, vibration and electrical stress

Through‑silicon-via (TSV) microstructure is the key structure of 3D integrated packaging, and its reliability during service plays a very important role in the long-term reliable operation of the chip. However, the failure mechanism and reliability evaluation of the TSV structure in the service process of the chip have not been fully elucidated. Chips inevitably bear various stresses during the service process, such as thermal stress, vibration stress, electrical stress, etc. In this paper, a TSV reliability test chip that can be easily loaded with various stresses was designed first, and the resistivity of the TSV microstructure was tested. It is found that the resistivity of TSV has a significant scale effect. Then, thermal cycling, vibration and electromigration tests were carried out, and the failure mode, performance degradation trend and failure distribution of TSV microstructure under thermal cycling, vibration and electrical stress were analyzed. Finally, the failure analysis of the tested chip was carried out by scanning electron microscope (SEM) and high-resolution industrial micro-CT, and the potential failure mechanism were analyzed and discussed.

Cluster Migration Distance for Performance Degradation Assessment of Water Pump Bearings.

Because the signal of water pump bearing is seriously disturbed by noise and the fault evolution is complex, it is difficult to describe the performance degradation trend of water pump bearing in a timely and accurate manner using the traditional performance degradation index (PDI). In this paper, a new Cluster Migration Distance (CMD) algorithm is proposed. The extraction of the indicator includes the following four steps: First, the relevant blind separation is used to extract the useful signal of the monitored bearing from the mixed signal; secondly, the impact component is further enhanced by wavelet packet analysis. Then, the redundancy of the original feature vectors is eliminated using our previously proposed KJADE (Kernel Joint Approximate Diagonalization of Eigen-matrices) method. Finally, the newly proposed CMD index is computed as PDI. By calculating the offset trajectory of the feature cluster centroid in the continuous running process of the bearing, CMD can aptly deal with the complex and variable features in the fault evolution process of the water pump bearing. The whole-life monitoring data of a 220 KW water pump system are processed. The results show that the proposed CMD index has better early-warning ability and monotonicity than the traditional kurtosis index.

Remaining useful life prediction of rolling bearings based on Pearson correlation-KPCA multi-feature fusion

In the feature selection process, a subset of features is selected from the original set of features based on the feature redundancy and importance. However, most of the existing methods for fusing multiple features ignore the connection between original features which leads to increased redundancy and affects the final classification results. To address this issue, in this study, an effective feature fusion method is proposed for predicting the remaining useful life (RUL) of rolling bearings based on Pearson correlation coefficient and kernel principal component analysis (KPCA). Firstly, the multi-domain features reflecting the bearing degradation state are extracted, and the correlation between the multi-domain features is obtained by Pearson correlation analysis before the feature fusion by KPCA. The features are grouped in such a way that that the correlation between the fused features is higher and the redundancy is lower. Secondly, a RUL prediction model of bearings is established based on the long short-term memory (LSTM) neural network and the Cox proportional-hazards model. In this model, the fused feature set is input into the LSTM neural network to predict the bearing performance degradation trend, which is then used as a multi-dimensional covariate in the Cox proportional-hazards model to assess the reliability of the bearing over time, thereby predicting the bearing RUL. Finally, based on experimental verification, the bearing failure prediction error for two different bearings is obtained to be 1.6 % and 3.4 %, which indicates the feasibility and efficacy of the proposed method for RUL prediction of mechanical equipment.

Prognostics for remaining useful life estimation in proton exchange membrane fuel cell by dynamic recurrent neural networks

This paper proposes a nonlinear dynamic recurrent neural network (DRNN) prognostics method for predicting the performance degradation trend and estimating the remaining useful life (RUL) of a proton exchange membrane fuel cell (PEMFC). The conducted DRNN prognostic methods are based on a nonlinear autoregressive neural network (NARNN) model and nonlinear autoregressive with exogenous inputs neural network (NARXNN) model, with the goal of taking actions to extend the life of the PEMFC. To effectively remove data noise or spikes and provide smooth reconstruction, a locally weighted regression (LWR) method is used for pre-processing. The DRNN model parameters for the time lags are processed by an autocorrelation function (ACF). For comparing the predictions within the different DRNN models, the data sets are divided into three different prognostic configurations for predicting and estimating the remaining life of the PEMFC. The results show that the NARXNN model has the best predictions for the degradation trend and accurate remaining useful life, i.e. better than the NARNN model. Finally, the prognostic capability of the proposed DRNN method is compared with those in the literature.

A convolutional neural network model for battery capacity fade curve prediction using early life data

Early prediction of battery performance degradation trends can facilitate research of new materials and cell designs, rapid deployment of batteries in real-world applications, timely replacement of batteries in critical applications, and even the secondary use market. In this study, we design a convolutional neural network model to predict the entire battery capacity fade curve – a critical indicator of battery performance degradation – using first 100 cycles of data (∼ three weeks of testing). We use the discharge voltage-capacity curves as input to the model and automate the feature extraction process through the convolutional layers of the network. Our approach can predict the per cycle capacity fade rate and rollover cycle (knee point) in the capacity fade curve, which indicate the onset of rapid capacity decay. On the publicly available graphite/LiFePO4 battery dataset, optimized networks predict the capacity fade curves, rollover cycle, and end of life with 3.7% (worst-case), 19%, and 17% mean absolute percentage errors, respectively.

Performance Prediction of Rolling Bearing Using EEMD and WCDPSO-KELM Methods

Research on bearings performance degradation trend is significant, and can greatly reduce the loss caused by potential faults in the whole life-cycle of rolling bearings. It is also a very important part of Prognostic and Health Management (PHM). This paper proposed a new performance degradation prediction method based on optimized kernel extreme learning machine (KELM), improved particle swarm optimization (PSO) and Ensemble Empirical Mode Decomposition (EEMD). Firstly, the particle swarm optimization algorithm was improved by adjusting the inertia weight and linear learning factor and introducing a disturbance term, namely WCDPSO. Then, the penalty coefficient and kernel parameters of KELM were optimized by the WCDPSO, and the WCDPSO-KELM model was obtained. Subsequently, the EEMD method was used to extract original features from sample data, and a performance degradation index is selected from the EEMD feature space, which was input into the WCDPSO-KELM model in order to build a bearing performance degradation prediction trend model. Finally, the proposed method was verified by datasets of rolling bearings that were provided by the PRONOSTIA platform. Experimental results confirmed that the proposed method can efficiently predict the performance degradation trend of rolling bearings.

Performance Degradation Estimation of High-Speed Train Bogie Based on 1D-ConvLSTM Time-Distributed Convolutional Neural Network.

High-speed train bogies are essential for the safety and comfort of train operation. The performance of the bogie usually degrades before it fails, so it is necessary to detect the performance degradation of a high-speed train bogie in advance. In this paper, with two key dampers on the bogie taken as experimental objects (lateral damper and yaw damper), a novel 1D-ConvLSTM time-distributed convolutional neural network (CLTD-CNN) is proposed to estimate the performance degradation of a high-speed train bogie. The proposed CLTD-CNN is an encoder-decoder structure. Specifically, the encoder part of the proposed structure consists of a time-distributed 1D-CNN module and a 1D-ConvLSTM. The decoder part consists of a 1D-ConvLSTM and a simple time-CNN with residual connections. In addition, an auxiliary training part is introduced into the structure to support CLTD-CNN in learning the performance degradation trend characteristic, and a special input format is designed for this structure. The whole structure is end-to-end and does not require expert knowledge or engineering experience. The effectiveness of the proposed CLTD-CNN is tested by the high-speed train CRH380A under different performance states. The experimental results demonstrate the superiority of CLTD-CNN. Compared to other methods, the estimation error of CLTD-CNN is the smallest.

An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions.

The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.

Life Prediction Method of Remanufactured Machinery Equipment Based on Vibration Signal Feature Extraction

Mechanical equipment is a key component of mechanical equipment, and its working condition is directly related to the overall performance of mechanical equipment. Accurate evaluation and prediction of the performance degradation trend of mechanical equipment is of great significance to ensure the reliability and safety of the mechanical equipment system. Based on the data of typical faulty equipment, this paper analyzes the energy characteristic parameters of mechanical equipment under different types and degrees of failure in the time domain. Using amplitude spectrum analysis, Hilbert envelope demodulation and wavelet packet decomposition method, and other vibration signal processing methods, preliminary extraction of multiple statistical feature parameters are given. Secondly, in view of the irrelevant and redundant components of multiple statistical parameters, a new method for extracting fault features of mechanical equipment based on variance value and principal component analysis is proposed. This method can effectively classify the fault status of mechanical equipment. The effectiveness of the method is verified by actual equipment signals. After that, the value extracted from the vibration signal of the double-row roller equipment is used as the degradation feature. In order to reduce the influence of irregular characteristics in the vibration signal and simplify the complexity of the vibration signal, the wavelet transform and the support vector machine model are combined, according to the degradation after decomposition. The 95% confidence interval of the predicted value is also given. The SVM model is established based on data characteristics, and single-step and multistep prediction of equipment degradation trends are carried out. The prediction result shows that, according to the mapping position formula, the distribution of equipment degradation prediction points is obtained, and a 95% confidence interval based on the distribution of the prediction points is given. Finally, on the basis of completing feature extraction, this paper applies an unsupervised feature selection method. The sensitive characteristics of life prediction and the prediction results of a single SVM model and a neural network model are compared and analyzed at the same time.