Residual Life Prediction Model Research Articles

A Remaining Useful Life Prediction Method of Mechanical Equipment Based on Particle Swarm Optimization-Convolutional Neural Network-Bidirectional Long Short-Term Memory

In industry, forecast prediction and health management (PHM) is used to improve system reliability and efficiency. In PHM, remaining useful life (RUL) prediction plays a key role in preventing machine failures and reducing operating costs, especially for reliability requirements such as critical components in aviation as well as for costly equipment. With the development of deep learning techniques, many RUL prediction methods employ convolutional neural network (CNN) and long short-term memory (LSTM) networks and demonstrate superior performance. In this paper, a novel two-stream network based on a bidirectional long short-term memory neural network (BiLSTM) is proposed to establish a two-stage residual life prediction model for mechanical devices using CNN as the feature extractor and BiLSTM as the timing processor, and finally, a particle swarm optimization (PSO) algorithm is used to adjust and optimize the network structural parameters for the initial data. Under the condition of lack of professional knowledge, the adaptive extraction of the features of the data accumulated by the enterprise and the effective processing of a large amount of timing data are achieved. Comparing the prediction results with other models through examples, it shows that the model established in this paper significantly improves the accuracy and efficiency of equipment remaining life prediction.

The residual life prediction of power grid transformers based on GA-ELM computational model and digital twin data

As one of the core equipment of the power grid, the operation status of transformers directly affects the stability and reliability of the power system. To accurately evaluate the remaining life of power grid transformers, a genetic algorithm is applied to optimize the Extreme Learning Machine based on digital twin technology. Then, considering changes in load rate, a residual life prediction model for power grid transformers is constructed. From the results, the error of the research method was within 2℃, with a maximum error of only 1.76℃. The research model converged with a fitness value of 0.04 at 150 iterations. It showed good predictive performance for hot spot temperatures under different load rates, with an average accuracy of 99.97%. Compared with backpropagation models and extreme learning machine models, the research method improved accuracy by 2.85% and 1.01%, respectively, with small and stable prediction errors. It verified the superiority of the research model, indicating that the research method can improve the accuracy of predicting the remaining life for power grid transformers. By monitoring the operation status of transformers in real-time, potential faults can be detected in a timely manner. The maintenance and replacement can be carried out in advance to avoid power outages caused by equipment damage. In addition, the research can provide reference for the planning and design of power systems, and support the stability and reliability of power systems.

A method for establishing a bearing residual life prediction model for process enhancement equipment based on rotor imbalance response analysis

A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer. During the operation of the rotating packed bed, the nonreactants and products irregularly adhere to the wire mesh packing in the rotor, thus resulting in an imbalance in the vibration of the rotor, which may cause serious damage to the bearing and material leakage. This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis. This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage. The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius, as revealed by the results of orthogonal experiments. Through implementing finite element analysis, the imbalance response curve for the rotating packed bed rotor was obtained, and a correlation among rotor imbalance mass, distribution radius of imbalance mass, and bearing residue life was established via data fitting. The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.

Remaining Useful Life Prediction for Turbofan Engine Using SAE-TCN Model

Turbofan engines are known as the heart of the aircraft. The turbofan’s health state determines the aircraft’s operational status. Therefore, the equipment monitoring and maintenance of the engine is an important part of ensuring the healthy and stable operation of the aircraft, and it is vital to monitor the remaining useful life (RUL) of the engine. The monitored data of turbofan engines have high dimensions and a long time span, which cause difficulties in predicting the remaining useful life of the engine. This paper proposes a residual life prediction model based on Autoencoder and a Temporal Convolutional Network (TCN). Among them, Autoencoder is used to reduce the dimension of the data and extract features from the engine monitoring data. The TCN network is trained on the obtained low-dimensional data to predict the remaining useful life. The model mentioned in this article is verified on the NASA public data set (C-MAPSS) and compared with common machine learning methods and other deep neural networks. The SAE-TCN model achieved better scores on the FD001 independent testing data set with an RMSE of 18.01 and a score of 161. The average relative error of the model relative to other common learning models is 0.9499 in RMSE and 0.2656 in Scoring Function. The experimental results show that the model proposed in this paper performs the best in the evaluation, and this conclusion has important implications for engine health.

Residual life prediction of thermal insulation material for cold chain logistics transportation vehicle

Aiming at the problems of low prediction accuracy and long prediction time cost in traditional methods, a residual life prediction method of thermal insulation material for cold chain logistics transportation vehicle is proposed. The distribution position of thermal insulation material in the car body is analysed. According to the distribution position of thermal insulation materials, the life prediction parameters of thermal insulation material are calculated. According to the determined parameters, the key degree of thermal insulation material is calculated by gradient descent method. So as to construct the residual life prediction model of cold chain logistics transport vehicle compartment thermal insulation material, the key parameters of thermal insulation material is taken as the input, and the residual life prediction results of compartment thermal insulation material as the output. Experimental results show that the prediction accuracy of the proposed method is about 97% when 100 times, and the shortest time cost is about 0.5 s.

A mechanical system reliability degradation analysis and remaining life estimation method——With the example of an aircraft hatch lock mechanism

The failure mechanism and degradation analysis of an aircraft cabin door lock mechanism are studied. A data-driven degradation analysis is conducted based on the characteristics of two types of degradation data for the lock mechanism. The degradation process of the fluctuating elevation is described by Wiener process, and the lifetime is described using the Inverse Gauss (IG) process. The correlation between the two degradation processes is studied, and the correlation analysis is carried out with four copula functions. Moreover, the optimal model is obtained for reliability analysis using three model selection methods and Bayesian inference based on the Markov chain Monte Carlo (MCMC) method for parameter estimation. Finally, a residual life prediction model is proposed, and the accuracy of the model is tested with the error. The results show that the RUL prediction accuracy can be accepted, which helps to improve the reliability and service life of a type of aircraft lock mechanism.

A New Model for Remaining Useful Life Prediction Based on NICE and TCN-BiLSTM under Missing Data

The Remaining Useful Life (RUL) prediction of engineering equipment is bound to face the situation of missing data. The existing methods of RUL prediction for such cases mainly take “data generation—RUL prediction” as the basic idea but are often limited to the generation of one-dimensional test data, resulting in the extraction of the prediction network. Therefore, this paper proposes a multivariate degradation device based on Nonlinear Independent Components Estimation (NICE) and the Temporal Convolutional Network–Bidirectional Long Short-term Memory (TCN-BiLSTM) network for the RUL prediction requirements in the case of missing data. First, based on the NICE network, realistic data are generated through reversible sampling; then, the filling of multivariate missing data is completed. Next, the filled multivariate degradation data are processed to generate multivariate degradation data and predicted labels for constructing the training set and test set. Based on this, a residual life prediction model integrating TCN and the BiLSTM network is proposed. To evaluate the proposed method, this paper takes an example of the RUL prediction of aeroengines to perform multivariate degradation data-filling and prediction tasks. The results demonstrate the superiority and potential application value of the method.

MSCNN-BLSTM based Prediction of the Remaining Useful Life of Aeroengine

Accurate prediction of the aeroengine remaining useful life (RUL) is essential to improve engine availability and reliability. Aiming at the reliable prediction of residual life of aeroengine system, an engine residual life prediction model based on the fusion of multiscale fusion two-dimensional convolutional neural network and bidirectional long and short term memory (MSCNN-BLSTM) is proposed. Based on the fusion of two-dimensional convolutional neural network and bidirectional long and short time memory (BLSTM) network, the engine medium and advanced features extracted by the convolutional neural network are integrated to make residual life prediction. Finally, C-MAPSS dataset provided by NASA was used for validation. It is shown that the proposed multiscale hybrid model, compared with other model predictions, reduces the performance index score and root mean square error by 32.2% and 14.7% respectively. It can be seen that the data-driven model can effectively extract the information from the degradation data, which improves the prediction performance of aeroengine remaining life.

Adaptive Residual Life Prediction for Small Samples of Mechanical Products Based on Feature Matching Preprocessor-LSTM

In order to solve the problem of predicting the residual life of mechanical products accurately based on small-sample data, this paper proposes a small-sample adaptive residual life prediction model of mechanical products based on feature matching preprocessor-LSTM. First, aiming at the problem of low accuracy of remaining life prediction for small samples of mechanical products caused by multiple time scales and multiple fault states, the failure time data and performance degradation data are fused, and the failure rate and standard deviation are used as the remaining life prediction criteria to intuitively reflect The possibility of failure of a component or system at a certain point in time. Considering the demand of adaptive small-sample residual life prediction data, this paper establishes the adaptive matching pre-processor model of life characteristics. On this basis, the LSTM neural network is used to establish a small-sample adaptive residual life prediction model. Then, the XJTU-SY bearing life data set and the test data of the small-sample life characteristics measured by the RV reducer are used as the research objects, and a small amount of the data set is randomly selected. The remaining life expectancy is predicted from the sample data and compared with its standard remaining life, respectively. The comparison results show that the overall prediction error is small. This study shows that the remaining life prediction model established can better predict the remaining life of mechanical product sub-sample data and provides a feasible method for predicting the remaining life of mechanical product sub-samples.

A New Dynamic Fault Tree Analysis Method of Electromagnetic Brakes Based on Bayesian Network Accompanying Wiener Process

Product fault diagnosis has always been the focus of quality and reliability research. However, a failure–rate curve of some products is a symmetrical function, the fault analysis result is not true because the failure period of the products cannot be judged accurately. In order to solve the problem of fault diagnosis, this paper proposes a new Takagi-Sugeno (T-S) dynamic fault tree analysis method based on a Bayesian network accompanying the Wiener process. Firstly, the top event, middle event, and bottom event of the product failure mode are determined, and the T-S dynamic fault tree is constructed. Secondly, in order to form the Bayesian network diagram of the T-S dynamic fault tree, the events in the fault tree are transformed into nodes, and the T-S dynamic gate is also transformed into directed edges. Then, the Wiener process is used to model the performance degradation process of the stationary independent increment of the symmetric function distribution, and the maximum likelihood estimation method is applied to estimate the unknown parameters of the degradation model. Next, the product residual life prediction model is established based on the concept of first arrival time, and a symmetric function of failure–rate curve is obtained by using the product failure probability density function. According to the fault density function derived from the Wiener process, the reverse reasoning algorithm of the Bayesian network is established. Combined with the prior probability of the bottom event, the posterior probability of the root node is calculated and sorted as well. Finally, taking the insufficient braking force of electromagnetic brakes as an example, the practicability and objectivity of the new method are proved.

Research on electrical life Trace prediction of contact based on SG-BP algorithm

In order to prevent electrical fire in low-voltage distribution system, it is a very effective measure to predict the electrical life of various low-voltage electrical appliances running on the line. At present, it is difficult to predict the residual electrical life of AC contactors. How to establish a reasonable and reliable prediction model for the residual electrical life of AC contactors is the key to the electrical life test and characteristic research of AC contactors. In this paper, Savitzky-Golay convolution smoothing algorithm is combined with BP neural network algorithm, and the remaining electrical life prediction model of contact is established by taking the arcing time, arcing energy and contact opening phase angle as the input parameters of the prediction model. The test results show that the average relative error of BP residual electrical life prediction model is less than 4%, and the maximum relative error is less than 10%. Combined with Savitzky-Golay convolution smoothing algorithm, SG-BP residual electrical life prediction model has average relative error less than 3% and maximum relative error less than 8%, which improves the prediction accuracy of residual electrical life, and lays a good foundation for making safe and reliable judgment by making better use of electrical life trace data..

Accuracy Degradation Model and Residual Accuracy Life Prediction of CNC Machine Tools Based on Wiener Process

In order to predict the accuracy residual life of CNC machine tools, an accuracy degradation model and accuracy residual life prediction method of CNC machine tools based on accuracy degradation data are proposed. Considering the non-linear and uncertainties in the process of accuracy degradation, an accuracy degradation model of CNC machine tools was established based on the nonlinear Wiener process; The accuracy failure is judged when the measured accuracy value exceeds the failure threshold for the first time. An accuracy residual life prediction model based on Inverse Gaussian distribution is established, and the accuracy residual life is calculated as a probability density function. To solve the problem of lack of historical data and prior information for a single CNC machine tool, particle filter is used to recursively update the posterior values of the model parameters based on the current observation data to achieve continuous optimization of the residual life prediction results; Finally, the correctness and effectiveness of the accuracy degradation model and the prediction method of the accuracy residual life are verified through the example analysis of the accuracy degradation experimental data of CNC machine tool.

Residual life prediction of marine EPR cable under discontinuous operation

It is known that marine cable is responsible for the transmission of electric energy and electrical signals of the ship. In order to predict the residual life of cable under discontinuous operation, a non-destructive online life prediction model is established based on retention rate of hardness (RRH). This study takes the marine ethylene propylene rubber (EPR) cable as the research object. Using time temperature superposition principle, the optimal time temperature shift factor is calculated using particle swarm optimisation and steepest descent method. After that the main curve was fitted exponentially to acquire the relationship between RRH and ageing time. Compared with the existing model, the results indicate that the proposed model has higher prediction accuracy. On this basis, the residual life prediction model based on RRH of marine EPR under discontinuous operation is presented by adding time parameters. The effectiveness of the method is verified by simulation. Thus, it provides theoretical guidance for solving the problem of marine cable replacement time.

On Condition Maintenance Model for Complex Electromechanical Equipments Based on Remaining Useful Life and Wiener Process

Aiming at the problem of condition based maintenance modeling for complex electromechanical equipment, a condition based maintenance model based on residual life prediction and improved Wiener process is proposed; first of all, for the degradation process character of complex electromechanical equipment, this paper proposes a residual life prediction model based on random coefficient Wiener process; To estimate the parameters of degradation process, a prior distribution construction method based on state data is proposed; And the posterior distribution and estimation of degradation process parameters are constructed by Bayes theory, and the remaining life prediction of equipment in operation is realized. Secondly, the decision model of condition based maintenance is established by balancing the reliability and economy targets with the information of remaining life; And the immune particle swarm optimization algorithm is used to solve the model. The case study shows that the residual life prediction model proposed in this paper has higher prediction accuracy and lower prediction uncertainty, the condition based maintenance decision-making model is more practical, has higher operational reliability and economy, and the convergence accuracy of the model solving algorithm is higher.

Calibrating a Local Civil Airport Runway Residual Life Prediction Model Using Random Effects and Joint Estimation

Existing runway residual life prediction models need to be modified using the historical data of evaluated airports during evaluation and management for civil airports in China. If the data measured in the field and the historical data used for model calibration do not represent the actual historical performance of the evaluated airport, the predicted performance of the revised model might be poor. This study used measured pavement performance data for local civil airports in Henan Province from 2007 to 2017. The joint-estimation method was used to establish a functional residual life prediction model for local civil airport pavement with another dataset. A functional residual life prediction model for airport pavement was proposed in consideration of the influence of aircraft traffic and the thickness of the pavement surface layer. Taking into account the differences between samples in the two datasets, nonlinear regression with random-effect analysis and joint estimation were used to explain unobserved heterogeneity at the sample level and heteroscedasticity in the dataset. Based on the results of the established residual life prediction model, the marginal effect of the model parameters and the prediction performance of the entire model were analyzed with the measured data from the local airport pavement. Finally, the engineering applicability of the calibrated prediction model for pavement residual life was further evaluated.

Real-time Online Prediction of Data Driven Bearing Residual Life

In order to realize the predictive maintenance of key components under massive vibration data, real-time online prediction of the remaining life of different types of bearings, a data driven real-time online prediction method for bearing residual life is introduced. The method realizes the construction of the data-driven bearing residual life prediction model by selecting the bearing vibration data Spearman characteristic parameter selection, principal component analysis (PCA), health index fusion, and BP neural network fitting. The built model is continuously updated by real-time online acquisition of data to achieve real-time online prediction of bearing residual life. The accuracy and feasibility of the method for predicting the remaining life of different types of bearings are verified by experiments. Using this method to predict the remaining life of the bearing helps to achieve predictive maintenance of critical components, reduce unplanned downtime, increase production efficiency, and reduce production costs.

Creep residual life prediction of a nickel-based single crystal superalloy based on microstructure evolution

Microstructure evolution occurs throughout the high temperature creep process inside nickel-based single crystal superalloys. In this study, creep tests of a nickel-based single crystal superalloy in the [001] orientation at 980 °C and 1100 °C were performed. The results indicated that creep failure occurred due to the formation of micropores and deterioration. Scanning electron microscopy (SEM) observations showed that the material degradation during creep was primarily reflected in the coarsening of the matrix channel and the precipitation of the TCP phase. Meanwhile, transmission electron microscopy (TEM) observations indicated that the dislocation morphology of the [001] orientation is consistent with the characteristics of the octahedral slip system. To obtain a quantifying explanation of such microscopic phenomena, a material constitutive model and creep damage model considering the Orowan effect and the dislocation effect were established based on the crystal plasticity theory. The model parameters were fitted according to the experimental results. Based on the quantitative description of the microstructure evolution during the creep process, the residual life prediction model of a single crystal superalloy was established for guidance in engineering applications.

Residual life prediction method for remanufacturing sucker rods based on magnetic memory testing and a support vector machine model

It is crucial for remanufacturability to be able to determine the residual life of remanufacturing cores. In the light of weaknesses in the available methods for fatigue damage evaluation and residual life prediction for remanufacturing sucker rods, a novel prediction approach using an optimised support vector machine (SVM) model based on metal magnetic memory (MMM) testing is proposed here. Firstly, tension-tension fatigue experiments on pre-cut groove sucker rod specimens are conducted to investigate the variations in the magnetic memory signals after different numbers of cycles and seven characteristic parameters are extracted to characterise the degree of fatigue damage. Then, a residual life prediction model for remanufacturing sucker rods based on a SVM model is established, where the SVM model parameters, including the radial basis function (RBF) kernel parameter and the penalty factor, are optimised in turn by a genetic algorithm (GA), partical swarm optimisation (PSO) and grid search optimisation (GSO). The results show that the proposed PSO-based approach significantly improves the prediction accuracy, compared with a basic SVM approach, and also yields more stable and accurate results when compared with the GA and the GSO method, providing a new and feasible approach for predicting remaining life.

Analysis of performance degradation and residual life prediction of batteries for electric vehicles under driving conditions

The analysis of residual life prediction of batteries for electric vehicles under driving conditions was performed based on the degradation data. This article, characteristics of lithium iron phosphate battery performance degradation as the object, mainly considers two factors about the charge–discharge rate and vibration stress, first, the lithium battery performance degradation test simulating a car in a real driving environment is designed, which provides the lithium battery performance test reference. Then, the model parameters were obtained based on the test data. Finally, the performance degradation model and residual life prediction model of the lithium battery based on the Wiener process are established. This method not only has a high prediction accuracy, but also avoids the construction of a complex battery mechanism degradation model. What is more, this article provides a good perspective for residual life prediction in simplified experiments saving cost. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Research on Residual Life Prediction for Electrical Connectors Based on Intermittent Failure and Hidden Semi-Markov Model

Based on the dynamic properties of electrical connector intermittent failure, the model and methods for residual life prediction for electrical connectors are studied in this paper. Firstly, the mechanism of electrical connector intermittent failure is analyzed, and the area enclosed by the contact resistance curve and the fault threshold is defined as the generalized severity of intermittent failure to describe how severe the electrical connector’s intermittent failure is. Then, the Hidden Semi-Markov Model (HSMM) is introduced to build the residual life prediction model of the electrical connector. Further, the evaluation method of using the state and prediction method for residual life are studied. Finally, by carrying out the residual life prediction test, the effectiveness of the residual life prediction method for electrical connectors based on intermittent failure and HSMM is verified.