Residual Life Prediction Model Research Articles

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.

Multiple physical signals based residual life prediction model of slewing bearing

Critical failure of a slewing bearing used in large machines would entail high costs to an enterprise. Designing the condition monitoring system to diagnose the failure or predict the residual life of the slewing bearing is a practical and effective method to reduce unexpected stoppage or optimize the maintenances. Many literatures mentioned the life prediction of small typical rolling bearings based on the vibration signal. However slewing bearing is a large low-speed heavy-load bearing completely different from small bearing. Some researchers focused on the fault diagnosis of slewing bearing using non-traditional methods with vibration signals. And no published literatures mention the life prediction researches of slewing bearings based on the condition monitoring. Therefore, this paper presents a residual life model for slewing bearing based on multiple physical signals (torque, temperature and vibration). The correlation analysis and principal component analysis (PCA) based multiple sensitive features in time-domain were used to establish the performance recession indicators of temperature, torque and vibration, and these three indicators are input to the support vector regression (SVR) to construct the residual life model. The test results show that the PCA fusion combined correlation based features selection is an effective method of choosing the performance regression indicators, which is able to make full use of various features. The residual life prediction model based on temperature, torque and vibration signals can well reflect the performance recession trend and is suitable to predict the residual life of slewing bearing effectively.

Reliability-based residual life prediction of large-size low-speed slewing bearings

Critical failure of a slewing bearing used in large machines would bring disastrous loss to an enterprise. Accurate residual life prediction of a slewing bearing can reduce unexpected accidents and unnecessary maintenances. In this paper, a residual life reliability prediction approach for slewing bearings was firstly presented based on the Weibull distribution. Afterwards, a novel approach for parameter estimation was proposed based on a small-sample test, which built the relationship between the characteristic life of a slewing bearing and the maximal load over the raceway. To verify the proposed approach, a full-load accelerated life test was implemented on a QNA-730-22 slewing bearing. It was observed that the damage of the outer (fixed) ring caused the failure of the slewing bearing, while the inner (rotatable) ring was still in good condition. After which, the residual life prediction model was then established by the proposed approach. Compared to ISO 281 and NREL design guide 03, the proposed approach is closer to engineering practice, and thus has the potential for slewing bearing prognostics.

Study of Durability Analysis and Evaluation Model for Existing Concrete Structure in Coastal Areas

This article put forward the durability state analysis method for existing concrete structure based on the step analysis theory and environmental load feature of east coast in China. Considering the process of both concrete degradation and steel corrosion, this article also established the residual life prediction model of existing concrete structure, based on a whole life prospective period. The model was successfully applied to evaluate the durability state and residual life of certain existing concrete structures, including dock structure in Hangzhou Bay and matching construction of Expo, 2010.

An asset residual life prediction model based on expert judgments

An appropriate and accurate residual life prediction for an asset is essential for cost effective and timely maintenance planning and scheduling. The paper reports the use of expert judgments as the additional information to predict a regularly monitored asset’s residual life. The expert judgment is made on the basis of measured condition monitoring parameters, and is treated as a random variable, which may be described by a probability distribution due to the uncertainty involved. Since most expert judgments are in the form of a set of integer numbers, we can either directly use a discrete distribution or use a continuous distribution after some transformation. A key concept used in this paper is condition residual life where the residual life at the point of checking is conditional on, among others, the past expert judgments made on the same asset to date. Stochastic filtering theory is used to predict the residual life given available expert judgments. Artificial, simulated and real data are used for validating and testing the model developed.

Fatigue Behavior of Impacted Carbon/Epoxy Laminates under 2-Stage Block Loading

The paper presents the fatigue behavior of carbon/epoxy laminates with impact-induced damage under 2-stage block loading. The impact damage parameter is proposed to evaluate the effect of impact damage on fatigue life. Based on this parameter, the model is developed to predict the residual life at second block. Also, the model for equivalent stress is proposed to estimate the fatigue life under 2-stage block loading based on the S-N curves under constant amplitude loading. For these models, the 2-stage block loading fatigue tests were performed on the impacted composite laminates. The effect of impact damage on fatigue life under 2-stage block loading can be characterized by the impact damage parameter. Additionally, the results by the present residual life prediction model agree with experimental results regardless of applied impact energy. Also, the equivalent stress and corresponding fatigue life by the present model agrees well with the experimental results.