Estimation Of Residual Life Research Articles

State-of-the-art of crack propagation modelling in tubular joints

Offshore structures are conventionally designed for a service life between 20-30 years and mostly used beyond their service life. They are operated up to the point where costs of operations, maintenance and repair exceed the revenue generated by the offshore structure. The cyclic nature of environmental loading induces multi-axial stresses with high gradients in welded tubular joints that lead to fatigue cracks emanating from imperfections in the welded materials. Tubular joints may exhibit significant residual life after fatigue cracks formation due to crack propagating around the weld circumference. It is important to understand tubular joints behaviour and estimate the residual life beyond crack initiation as it promotes the safe operation of the offshore installation, inspection planning and life extension of the asset. This paper presents an overview of the residual life estimation of cracked tubular joints using numerical methods. The recent developments and possible enhancements to the modelling methods of cracked tubular joints are presented.

Assessment of the residual life of the high-temperature elements of the turbine rotors through modeling the reduction of the metal continuity

Calculation and experimental estimates of the residual life of steels P2M and EI415 of two strength levels (H and B) are carried out using two methods, the new one and the traditional. The loading was carried out for 300 thousand hours under conditions similar to those in which the metal operates in the zone of blade attachment in the rim of the disk of the first stage of medium-pressure rotors of K-300-240 LMZ and KhTZ turbines. When evaluating the residual life, a gradual decrease in stresses due to creep was taken into account by distinguishing several stages with conditionally constant stresses. According to the new methodology, the residual life was estimated by a change in the metal super-continuity at each stage 47 = ц/п + 1, where u/ is the continuity, n is the crack formation index; whereas in the traditional method the relative damage Р is estimated according to the principle of linear summation of damage (LSP). It is shown that the time dependences P and 47 obey the logarithmic law, while the metal of the fbrgings В (high strength) in all cases had a greater resource compared to the fbrgings H (low strength). For the first time, the process of resource depletion was considered by analyzing the sum of two functions: 47 + P = 1. It is shown that for R2M steel this amount is less than unity; for steel EI415 it is equal to or greater than unity. Proceeding from the literature data on the differences that follows from using LSP rule for a relatively viscous and brittle metal, we suggested that deviations from unity are attributed to the error in estimating the resource according to the LSP principle. Therefore, the result obtained by the new method should be recognized as more accurate. Taking into account the use of samples with a particularly deep sharp notch in which the brittle crack development is guaranteed by the proposed methodology, the new principle of calculating the residual life can be used in determination of the resource for any type of the parts made of the metal subjected to brittle fracture during operation under creep conditions.

A novel non-linear cumulative fatigue damage model based on the degradation of material memory

Many fatigue damage models have been investigated based on the S– N curve or modified S– N curve; however, a number of them require additional efforts to determine the material parameters or do not consider the loading history (loading interactions, loading sequences, loading levels, etc.). These limitations can result in extreme deviations for estimating the fatigue life in real-world scenarios. To address these limitations, a new fatigue damage model is developed based on the material memory, which can be described as the degradation of mechanical properties under cyclic loadings. Comparisons with three models are used to demonstrate the validity of the proposed model. Furthermore, four sets of experimental data under two-stress and four-stress levels are carried out to verify the validation of the proposed model, which improves the residual life estimation over the three existing models used for comparison.

DSSCC: Decision Support System for Ceramic Capacitor using Fuzzy Logic

As the technology advances, the reliability becomes the main constraint for the successful operation of the electronic product. To fully automate the system, the electronic devices become more and more complex. Reliability becomes a challenge with the regular demand of low cost and high-speed devices. Residual life estimation of passive devices such as resistor, capacitor etc. is of a great concern. Failure of one small component can lead to fully damage of whole system. In this paper, a practical approach i.e. accelerated life testing is deployed to calculate remaining useful life of the ceramic capacitor. An intelligent model is formulated using various artificial intelligence techniques. Artificial Neural Network (ANN), Fuzzy Inference System (FIS) as well as Adaptive Neuro Fuzzy Inference System (ANFIS) are deployed to predict the remaining useful lifetime of an electrolytic capacitor. An error analysis is conducted to estimate the most accurate intelligent technique. A fuzzy based decision support system is modelled, which provides an interactive GUI to users. The user can access the live health status of electrolytic capacitor at various input parameters. It will warn the user to replace or repair the upcoming fault in the component or device, before it actually degrades or shut downs the complete system. The comparative analysis of all the artificial intelligence techniques shows that Adaptive Neuro Fuzzy Inference System (ANFIS) has the highest accuracy i.e. 99.5%, as compare to Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), where accuracy rate is 98.06% and 97.84% respectively. This prediction system is helpful to reduce the problem of electronic e-waste by enabling the user to reuse the component.

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

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

Method of dependability assessment of spacecraft in design and engineering studies

The paper examines the matters of operational dependability of space systems (SS), efficiency of complex systems, use of redundancy in spacecraft (SC) design. It presents methods of predicting the dependability of designed devices, design of devices with desired dependability and comparison of dependability of various SS. For that purpose, the authors set forth the fundamentals of the dependability theory for SS design, methods of collection and processing of data of equipment dependability based on the results of operation and special dependability tests. Methods, mathematical models are developed, the equipment architecture at the stage of design and manufacture is analyzed. The paper also cites the design ratios for various tested types of redundancy, lifetime extension of SC units based on the residual operating life estimation method. The existing methods of dependability analysis are classified and examined. The authors outline the problems of ambiguity of information of the input data in case of classical computing. The effect of nominal deviations of the external effects, irregularity of the failure rate, non-linear nature of the effect of external factors on the dependability are examined. The paper also takes a look at the way the external factors affect the dependability and the degree to which such factors are taken into consideration in the existing methods. It is noted that the qualitative, technical and organizational (design and software) requirements for dependability in the technical specifications for each stage of elements and SS development, shall be observed and confirmed at the respective stage of activities. The paper presents the methods of estimation of technical item operating life with the focus on those based on the physical premises of operating life depletion. Attention is drawn to the importance of the economic aspect in the research dedicated to SS lifetime extension.

Potential for using the ANN-FIS meta-model approach to assess levels of particulate contamination in oil used in mechanical systems

Abstract Mechanical systems need to ensure high levels of quality. Today, greater generic reliability in systems makes it difficult to base any failure prognosis on previous system failures. Predicting the condition of a mechanical system needs to be based, instead, on monitoring the degradation of a system's components. Diagnostic signals can be identified and used as data to estimate the rate of degradation. A key driver for this work is the need to understand the performance of lubricants in systems involving mechanical contact. This article presents methods for studying field data collected with regard to oil. It focuses, in particular, on contaminated oil as this is an excellent source of diagnostic signals and information. However, data on oil present a degree of uncertainty in terms of both their collection and their use in the laboratory. Analysis of oil contaminants was, therefore, performed by applying a fuzzy inference system (FIS) and neural networks. The multilayer perception network was found to be an effective tool. The concentrations of iron and soot particles in used oil were selected as being both illustrative and the most significant model variables. The aim of this study is to acquire information about the condition of both lubricants and the mechanical systems, along with the development of degradation in mechanical equipment and the estimation of residual useful life (RUL). The results obtained will be useful in organizing effective operation of the mechanical systems being studied and modifying their maintenance.

Residual life estimation of fabricated humidity sensors using different artificial intelligence techniques

Residual life estimation of fabricated humidity sensors using different artificial intelligence techniques

Estimation of mean residual life based on ranked set sampling

The mean residual life (MRL) of a nonnegative random variable X plays an important role in various disciplines such as reliability, survival analysis, and extreme value theory. This paper deals with the problem of estimating the MRL in ranked set sampling (RSS) design. An RSS-based estimator for MRL is proposed and its properties are investigated. For finite sample sizes, a Monte Carlo simulation study is carried out to show that the resulting estimator is more efficient than its counterpart in simple random sampling (SRS) design. It is proved that the proposed estimator asymptotically follows a Gaussian process and its asymptotic variance is no larger than its counterpart in the SRS design, regardless of the quality of ranking. Different methods of constructing a confidence interval for MRL in the RSS and SRS designs are then discussed. It is observed that while both the RSS and SRS-based confidence intervals do not control the nominal confidence level equally well, the RSS-based confidence intervals have generally shorter lengths than those in the SRS scheme. Finally, a potential application in the context of medical studies is presented for illustration purpose.

On the Choice of Forming Modes and Estimation of Residual Service Life by Kinetic Equations with the Scalar Damage Parameter

On the Choice of Forming Modes and Estimation of Residual Service Life by Kinetic Equations with the Scalar Damage Parameter

Application of the Multi-microprobe DC Potential Difference Method to the Non-destructive Estimation of Residual Life of SUS316L Hollow Cylinder Subjected to High Temperature Fatigue in Air

Application of the Multi-microprobe DC Potential Difference Method to the Non-destructive Estimation of Residual Life of SUS316L Hollow Cylinder Subjected to High Temperature Fatigue in Air

Development of electromagnetic NDT methods for structural integrity assessment

Nondestructive testing (NDT) methods are widely used for inspection of long term exploited structures as important part of maintenance strategy. NDT methods can create the background for material optimization under the engineering procedures (e.g., welding) influence also. Principal attention taken into consideration is the investigation of the correlations between measured nondestructively material properties (e.g., specific electrical conductivity, coercive force) and parameters of fatigue crack growth resistance (FCGR) needed for residual life estimation. In this study the evaluation of aluminum alloys (AA) degradation in aging aircraft and AA welded joints characterization based on the specific electrical conductivity (SEC) measurements are presented. Eddy current (EC) device for local noncontact SEC with lift off suppression was developed. For ferrous steel components (steam pipelines, ship bearing elements) the NDT method based on coercive force (CF) measurements by attachable type probes are applied. The EC method based on the magnetic anisotropy (MA) changes under mechanical stress was proposed.

Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes

AbstractMost maintenance optimization models of gear systems have considered single failure mode. There have been very few papers dealing with multiple failure modes, considering mostly independent failure modes. In this paper, we present an optimal Bayesian control scheme for early fault detection of the gear system with dependent competing risks. The system failures include degradation failure and catastrophic failure. A three‐state continuous‐time–homogeneous hidden Markov model (HMM), namely the model with unobservable healthy and unhealthy states, and an observable failure state, describes the deterioration process of the gear system. The condition monitoring information as well as the age of the system are considered in the proposed optimal Bayesian maintenance policy. The objective is to maximize the long‐run expected average system availability per unit time. The maintenance optimization model is formulated and solved in a semi‐Markov decision process (SMDP) framework. The posterior probability that the system is in the warning state is used for the residual life estimation and Bayesian control chart development. The prediction results show that the mean residual lives obtained in this paper are much closer to the actual values than previously published results. A comparison with the Bayesian control chart based on the previously published HMM and the age‐based replacement policy is given to illustrate the superiority of the proposed approach. The results demonstrate that the Bayesian control scheme with two dependent failure modes can detect the gear fault earlier and improve the availability of the system.

Dynamiczna strategia utrzymania ruchu na podstawie stanu technicznego dla ulegających degradacji systemów opisanych modelem autoregresyjnym z parametrami losowymi – studium porównawcze

In this paper, we optimize a dynamic condition-based maintenance policy for a slowly degrading system subject to soft failure and condition monitoring at equidistant, discrete time epochs. A random-coefficient autoregressive model with time effect is developed to describe the system degradation. The system age, previous state observations, and the item-to-item variability of the degradation are jointly combined in the proposed degradation model. Stochastic behavior for both the age-dependent and the statedependent term are considered, and a Bayesian approach for periodically updating the estimates of the stochastic coefficients is developed to combine information from a degradation database with real-time condition-monitoring information. Based on this degradation model, the dynamic maintenance policy is formulated and solved in a semi-Markov decision process framework. Incorporated with the same semi-Markov decision process framework is a novel approach for mean residual life estimation, which enables simultaneous residual life estimation with the optimization procedure. The effectiveness of using the proposed randomcoefficient autoregressive model with time effect rather than the existing fixed-coefficient ones to describe system degradation is demonstrated through a comparative study based on a real degradation dataset. The advantages of using a dynamic maintenance policy are also revealed

Neural Estimation of Bond Strength Degradation in Concrete Affected by Reinforcement Corrosion

Bond between the reinforcement and its surrounding concrete is essential for their composite action. Due to corrosion of reinforcement and associated spalling/cracking of concrete, the bond strength reduces. Estimation of the reduced bond strength of the corroded reinforcing bar in concrete becomes important in assessment of the performance of the corrosion-affected concrete structure, particularly for residual life estimation. There are analytical as well as empirical models available in the literature for estimation of the reduced bond strength from the degree of corrosion of the reinforcing bar. In this paper, we develop ANN models for the aforementioned estimation for both pull-out tests and flexural tests. From the performance measures and the scatter plots, it is concluded that the ANN models perform comparably with the best empirical model (Bhargava 2008) though developed with 70–80% of the data.

Control valve stiction compensation by dynamic inversion: a comparative study

PurposeThe purpose of this paper is to improve the performance of control loop suffering from control valve stiction. Control valve stiction is considered as of one of the main causes of oscillation in process variables, which require performing costly unplanned maintenance and process shutdown. An adaptive solution to handle valve stiction while maintaining safety and quality until next planned maintenance is highly desirable to save considerable cost and effort.Design/methodology/approachThis paper implements a new stiction compensation method built using adaptive inverse model techniques and intelligent control theories. Finite impulse response (FIR) model, which is known to be robust, as a compensator for stiction. The parameters of FIR model are tuned in an adaptive way using differential evolution (DE) technique. The performance of proposed method is compared with other two compensation techniques.FindingsThe new method showed excellent performance of the DE–FIR compensator compared to other dynamic inversion methods in terms of minimizing process variability, energy saving and valve stem aggressiveness.Research limitations/implicationsThe compensation ability for all compensators reduces with the increase of stiction severity, thus the over shoot case always shows the worst result. In future works, other optimization techniques will be explored to find the appropriate technique that can extend the FIR model size with smallest computation time that can improve the performance of the compensator in over shoot case. In addition, the estimation of the valve residual life based on the level of stiction and effort required by the controller should be considered.Originality/valueThe presented approach represents an original contribution to the literature. It performs stiction compensation without a need for a prior knowledge on the process or the valve models and guarantees a smooth control of the stem movement with a low control effort. The proposed approach differs from previous adaptive methods as it uses stable FIR models and DE to find the appropriate parameters of the inverse model and handle nonlinear behavior of stiction.

Last-mile distribution planning for fruit-and-vegetable cold chains

PurposeThe purpose of this paper is to formulate and solve a last-mile distribution plan problem with concern for the quality of fruits and vegetables in cold chains.Design/methodology/approachThe vehicle routing problem with time windows (VRPTW) is extended based on the characteristics of fruit-and-vegetable cold chains. The properties of multiple perishable foods, continuing decline in quality, various requirements for quality levels and optimal temperature settings during vehicle transportation are considered in the VRPTW. The product quality level is defined by the estimation of residual shelf life, which changes with temperature, and is characterized by a stepped decrease during the transportation process as time goes on. A genetic algorithm (GA) is adapted to solve the problem because of its convincing ability to solve VRPTW-related problems. For this purpose, solution encoding, a fitness function and evolution operators are designed to deal with the complicated problem herein.FindingsA distribution plan including required fleet size, vehicle routing sequence and what quality level should be shipped out to account for the quality degradation during vehicle transportation is generated. The results indicate that the fulfillment of various requirements of different customers for various fruits and vegetables and quality levels can be ensured with cost considerations.Originality/valueThis study presents a problem for last-mile delivery of fresh fruits and vegetables which considers multiple practical scenarios not studied previously. A solution algorithm based on a GA is developed to address this problem. The proposed model is easily applied to other types of perishable products.

A segmental evaluation model for determining residual rail service life based on a discrete-state conditional probabilistic method

Because steel rail is one of the most fundamental components of railway operations, the accurate estimation of residual rail service life is of great significance in ensuring the safe operation of railways. In addition, maintenance expenses must be minimized in a manner that allows limited railroad resources to be optimally allotted. In this study, the typical types of continuous rail segments on a rail line are classified into non-sharply curved rail segments and sharply curved rail segments. Using these classifications, a model for estimating the residual service lives of rail segments using a discrete-state conditional probability method is proposed based on an analysis of rail deterioration characteristics. The model considers several heterogeneous factors to determine their influence on the deterioration process and is shown to be capable of estimating the residual service lives of rail segments. Finally, the model is validated through a case study of the Beijing Metro, using inspection records of rail defects in conjunction with heterogeneous factor data to predict the service life of the rail, which is then compared with its actual service life. The model is found to show good agreement with the rail inspection and maintenance records of the Beijing Metro, indicating its appropriateness for use by railroad management in allocating future rail maintenance resources.

A hybrid feature selection and health indicator construction scheme for delay-time-based degradation modelling of rolling element bearings

Rolling element bearings are mechanical components used frequently in most rotating machinery and they are also vulnerable links representing the main source of failures in such systems. Thus, health condition monitoring and fault diagnosis of rolling element bearings have long been studied to improve operational reliability and maintenance efficiency of rotatory machines. Over the past decade, prognosis that enables forewarning of failure and estimation of residual life attracted increasing attention. To accurately and efficiently predict failure of the rolling element bearing, the degradation requires to be well represented and modelled. For this purpose, degradation of the rolling element bearing is analysed with the delay-time-based model in this paper. Also, a hybrid feature selection and health indicator construction scheme is proposed for extraction of the bearing health relevant information from condition monitoring sensor data. Effectiveness of the presented approach is validated through case studies on rolling element bearing run-to-failure experiments.

A multi-indicator modeling method for similarity-based residual useful life estimation with two selection processes

The similarity-based residual useful life prediction (SbRLP) approach, a burgeoning technique, plays an increasingly important role in the residual useful life (RUL) prediction. Currently, studies on taking (a) the selection of degradation indicatorsf; and (b) the selection of reference samples into account are rare. While they have positive influence on improving the performance of the SbRLP method with multiple degradation indicator (MSbRLP). Driven by the abovementioned gap, two novel selection processes are advanced by this article and thus an improved MSbRLP method is developed. Then, in case study of the gyroscope RUL estimation, two selection processes and the improved MSbRLP method are illustrated to be reasonable and effective by comparison with the classical MSbRLP method. Furthermore, implementing results reveal that the existence of degradation indicator having bad reflection effect will truly increase the recognition error of the degradation state and hence decrease the accuracy of RUL estimation.