Residual Life Research Articles

Condition-based maintenance optimisation for multi-component systems using mean residual life

This paper aims to propose a Novel Condition-based maintenance (CBM) decision aid model for optimising the maintenance of complex multi-component systems. As the degradation level of each component is assumed to be independent and stochastic, it follows a specific probability distribution determined from historical data of experimental observations and inspection. The main objective is to optimise the total cost for providing maintenance actions and reducing the excess of spare parts usage. The decision support model consists of determining measurements on components with the aim of estimating the instant of time of removing predictively one or a group of components before they fail. The measurement model includes the mean residual lifetime (MRL) and some extensions developed for this purpose. For demonstrating the pertinency of the proposed model, we use a preventive maintenance strategy for one-component systems and a grouping/opportunistic maintenance for multi-component systems. Besides, a numerical comparative study performing these measurements is carried out using several examples and a case study from Electric energy distribution systems. The solution is illustrated as a decision-making optimal model for optimising the maintenance operations’ costs and the total number of spare parts. The numerical results and the comparison show the efficiency of the proposed approach.

Prediction of Remaining Fatigue Life of In-Service Bridge Cranes

At present, bridge crane accidents occur frequently, resulting in significant losses and casualties; to ensure the safe use of in-service bridge cranes, it is necessary to predict the residual fatigue life of in-service bridge cranes. Firstly, a static analysis of the most dangerous working conditions of in-service bridge cranes is carried out to find the fatigue failure point. Subsequently, a three-parameter Weibull distribution model is established for the characteristic parameters affecting the acquisition of the stress spectrum. Latin hypercubic sampling is applied to randomly sample the characteristic parameters to produce a random sample set of characteristic parameters for use in obtaining the stress–time history. The amplitude and mean values of the stress spectra are obtained by cycle counting using the rainflow counting method. Finally, Forman’s formula and Miner’s continuous damage accumulation theory were used to derive the remaining life prediction equations for constant and variable amplitude loads. Comparing the remaining life obtained from the simulation and test, the error is about 9.145%, which proves that the remaining life obtained from the simulation is more accurate. The results show that the combined method of simulation and testing is feasible and can predict the remaining life more accurately. In the past, the prediction of residual life was performed with either testing or simulation, which is long and costly. Simulation is low-cost and takes a short time, but the accuracy is not high. In this paper, the combination method of testing and simulation improves the efficiency of production and reduces the cost of use.

The Type II Topp-Leone Inverse Power Lomax distribution with Simulation and Applications

In this work, we present a four-parameter lifetime model that can be used to model reliability issues, fatigue life studies, and survival data called the Type II Topp-Leone Inverse Power Lomax distribution. It has the Type II inverse Lomax, Inverse Power Lomax, and Inverse Lomax distributions as sub-models. Some of its statistical properties, including complete and incomplete moments, generating functions, characteristics functions, mean residual life, mean inactivity time, Renyi entropy, Tsallis entropy, order statistics, stress-strength reliability, and weighted probability moment, have formal formulas that we have developed. The model's parameters are estimated using the maximum likelihood estimation technique. The effectiveness of maximum likelihood estimators is evaluated in terms of absolute bias and simulation study standard error. Two lifetime data sets are used to demonstrate how the new model can be applied. Using the same comparative criteria, the proposed distribution offers a better fit than a few well-known distributions.

Estimation of the residual useful life of EV batteries using advanced hybrid learning tools

Estimation of the residual useful life of EV batteries using advanced hybrid learning tools

Creep damaged microstructure and mechanical properties of Cr–Mo–V steel subjected to long-term service exposures

Evaluating the degree of creep damage is of high importance for the residual life assessment of high temperature and pressure steam piping systems of power plants. This study aims at assessing creep damaged microstructure and mechanical properties of low alloy Cr–Mo–V steel after long-term service exposures. Variations in mechanical properties have been assessed by means of tensile tests, hardness measurement and instrumented indentation testing (IIT). Optical and scanning electron microscopies were employed for the identification of creep damaged microstructure. The effect of heat treatment on the recovery of microstructure and mechanical properties was also studied. Correlations of IIT and hardness results between surface and bulk values have been obtained. The results of IIT agree well to the results of tensile tests. Moreover, relationships between hardness and tensile properties have been assessed. Eventually, the evaluation of the degree of creep damage by employing IIT and hardness measurement was attempted.

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.

Kernel estimators for mean residual lifetime in length-biased sampling

In this article, we propose three non parametric kernel estimators for the mean residual life function when the data are selected proportionally to their length. We evaluate the mean squared error of the three estimators and investigate the consistency for all three of them.

Statistical analysis of Saudi Arabia and UK Covid-19 data using a new generalized distribution

In this article, we extended the Topp–Leone distribution to create the Odd-Topp–Leone–Gompertz-G distribution, a new family of distributions. We were motivated by the limitations of classical models in modeling real life datasets hence, the creation of a new flexible distribution. Expansion of density, distribution of order statistics, Rényi entropy, moments, quantile, generating functions, residual life function and reverse residual life function, and reliability model are some of its structural aspects that are obtained. We looked at some special cases of the new distribution and concluded that our new model can handle both highly skewed data and non-monotonic hazard rate functions. The maximum likelihood estimation technique was used to estimate the model parameters. A simulation exercise was carried out to evaluate the performance of the proposed family of distribution. The article used the Saudi Arabia and United Kingdom dataset to demonstrate the utility of the new model and it was found that the new model performs better in modeling these datasets as compared to non-nested models presented in this article.

Residual Life Prediction of Railway Axle: A Review

Meskipun dirancang untuk beroperasi selama lebih dari 30 tahun, poros roda kereta api sering mengalami kerusakan selama masa penggunaannya. Kerusakan dapat terjadi akibat pengaruh lingkungan, benturan benda asing, atau ketidaksempurnaan material. Makalah ini memberikan gambaran tentang kerusakan umum yang sering terjadi pada poros roda kereta api dan membahas model perhitungan pertumbuhan retakan kelelahan yang digunakan untuk menganalisis umur sisa, serta parameter yang mempengaruhi kegagalan kelelahan pada poros roda kereta api. Kemudian, studi terkait prediksi umur kelelahan yang telah dilakukan oleh beberapa peneliti telah ditinjau. Studi-studi tersebut menunjukkan bahwa penting mempertimbangkan berbagai variasi data saat melakukan perhitungan untuk menghasilkan hasil yang akurat dan dapat diandalkan.
 Kata Kunci: fatigue poros roda kereta api, Prediksi umur sisa, Perambatan retak

Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic

Many power transformers throughout the world are nearing or have gone beyond their theoretical design life. Since these important assets represent approximately 60% of the cost of the substation, monitoring their condition is necessary. Condition monitoring helps in the decision to perform timely maintenance, to replace equipment or extend its life after evaluating if it is degraded. The challenge is to prolong its residual life as much as possible. Dissolved Gas Analysis (DGA) is a well-established strategy to warn of fault onset and to monitor the transformer’s status. This paper proposes a new intelligent system based on DGA; the aim being, on the one hand, to overcome the conventional method weaknesses; and, on the other hand, to improve the transformer diagnosis efficiency by using a four-step powerful artificial intelligence method. (1) Six descriptor sets were built and then improved by the proposed feature reduction approach. Indeed, these six sets are combined and presented to a Kohonen map (KSOM), to cluster the similar descriptors. An averaging process was then applied to the grouped data, to reduce feature dimensionality and to preserve the complete information. (2) For the first time, four direct Multiclass Support Vector Machines (M-SVM) were introduced on the Generic Model basis; each one received the KSOM outputs. (3) Dempster–Shafer fusion was applied to the nine membership probabilities returned by the four M-SVM, to improve the accuracy and to support decision making. (4) An output post-processing approach was suggested to overcome the contradictory evidence problem. The achieved AUROC and sensitivity average percentages of 98.78–95.19% (p-value < 0.001), respectively, highlight the remarkable proposed system performance, bringing a new insight to DGA analysis.

Sensitivity enhanced method for fault detection and prediction of elevator doors using a margin maximized hyperspace

This paper proposes novel fault classification and prediction method by addressing a margin maximized hyperspace (MMH) to solve the problems absent of any label at highly imbalanced dataset, which is a frequent but challenging problem in real-world industries. The proposed method features three characteristics. First, knowledge-based feature manipulation is executed using reference and feedback physical properties and the manipulated features are used for training the proposed neural network because the features contain rich information for classifying and predicting faults of the system of interest. Second, VAE transforms high-dimensional input features to a low-dimensional feature space. This nonlinear space transformation reduces the complexity of the classification securing high accuracy and robustness of fault classification in the MMH. Third, the acquired MMH through VAE with Bayesian optimization statistically allocates two extremes of major (normal) and minor (faulty) clusters at origin and unity at the feature space, indicating that sensitivity of fault prediction is maximized. The method would be highly effective in that the model only focuses on separating major and minor clusters deciding each health condition but ignores minor differences within the clusters which confuse users. The effect of the method is demonstrated with field measurements of an elevator door stroke dataset comprising normal, degradation, and faulty states in open and close strokes. The systematic analysis shows that these characteristics contribute to improve accuracy and robustness for fault classification. Specifically, knowledge-based feature manipulation improves the accuracy, and VAE enhances sensitivity on separating each cluster and locational constancy. Moreover, the MMH is effective to predict potential fault without any label for a highly imbalanced dataset. The proposed method provides remaining useful lifetime (RUL) using distances from normal and faulty clusters at the MMH, which enables to quantitatively provide RUL of the system without any definition of RUL. Considering that many systems deployed on fields lack information for fault life or residual useful life, the proposed method would be practical and effective for real world applications.

Comprehensive investigation and modeling of pitting corrosion effects on mechanical properties and fatigue performance of steel wires

Steel wires are widely employed in structural engineering due to their robust strength. However, the emergence of pitting corrosion significantly impacts their capacity, fatigue performance, and mechanical traits, with no specific codes addressing this issue. To assess the load-bearing capacity of corroded steel wires, predict their remaining fatigue life, and analyze failure modes, a comprehensive physical test was conducted. Mechanical evaluations of corroded wires were followed by 3D scanning to analyze corrosion attributes and fracture surface morphology. Using fuzzy reliability theory, a model estimated wire capacity under varying corrosion degrees. Subsequent fatigue testing explored performance and developed a predictive residual life model. Investigation results revealed a rapid and gradual decrease in capacity's reliability coefficient. Corroded wire fracture morphologies were classified as typical and multi-source cracks, including subtypes like cracks expanding in the same plane and step-like fractures. All wire types exhibited a cracking threshold exceeding 5.6 MPa·m1/2, with fracture intensity declining as corrosion severity increased. Notably, the model better suited slightly corroded wires, showing up to 30.93 % relative error for severely corroded wires. Enhancing accuracy for the latter entails considering additional factors to refine the model.

Development of the Kinetic Equation of the Groove Corrosion Process for Predicting the Residual Life of Oil-Field Pipelines

One of the main reasons for oil-field pipeline failure is groove corrosion. The residual life of such pipelines is estimated based on defectoscopy corrosion rate—a ratio of the formed «groove» depth to the pipeline operation start time. In this case, it is supposed that, in the future, the «groove» will deepen at the same rate for the remaining period of the pipe’s operation. However, sometimes, oil-field pipeline operation experience shows that the remaining time of safe operation is much less than the calculated one. In this article, such a discrepancy is explained via the acceleration of the groove corrosion rate in the process of «groove» deepening due to the increasing level of mechanical stresses in the surrounding metal, which intensifies the corrosion process as a result of the mechanochemical effect. Based on a literature analysis and calculated data, the kinetic equation of the groove corrosion rate for an oil-field pipeline is proposed, which accounts for the acceleration of the process rate as the pipeline is operated and allows the more accurate estimation of its remaining service life.

Non-Parametric Test for Decreasing Uncertainty of Residual Life Distribution (DURL)

In this paper, we propose a new statistic to test the monotonicity of uncertainty based on derivative criteria and the histogram method. We test the null hypothesis that residual entropy is constant against the fact that it decreases over time. Hence, by the fact that the exponential distribution is the distribution with a constant uncertainty, we establish the test exponential distribution against the decreasing uncertainty of residual life distribution. Consistency and asymptotic normality are proved. The critical values of the statistics are given by means of the Monte Carlo simulation method to decide on the test. Then, the power estimates of the new test are compared to those of the test based on the criteria of monotonicity of residual entropy. Finally, we show, with real survival data, that the distributions belong to a decreasing uncertainty residual life class. Moreover, by applying a test of goodness of fit, we confirm that the data follow parametric distributions belonging to a decreasing uncertainty of residual life class.

Preservation of mean inactivity time ordering for coherent systems

AbstractPreservation of stochastic orders through the system signature has captured the attention of researchers in recent years. Signature-based comparisons have been made for the usual stochastic order, hazard rate order, and likelihood ratio orders. However, for the mean residual life (MRL) order, it has recently been proved that the preservation result does not hold true in general, but rather holds for a particular class of distributions. In this paper, we study whether or not a similar preservation result holds for the mean inactivity time (MIT) order. We prove that the MIT order is not preserved from signatures to system lifetimes with independent and identically distributed (i.i.d.) components, but holds for special classes of distributions. The relationship between these classes and the order statistics is also highlighted. Furthermore, the distribution-free comparison of the performance of coherent systems with dependent and identically distributed (d.i.d.) components is studied under the MIT ordering, using diagonal-dependent copulas and distorted distributions.

Comparison of Extreme Wind and Waves Using Different Statistical Methods in 40 Offshore Wind Energy Lease Areas Worldwide

With the ongoing global drive towards renewable energy, several potential offshore wind energy lease areas worldwide have come into focus. This study aims to estimate the extreme wind and wave conditions across several newly designated offshore wind lease sites spanning six continents that are crucial for risk assessment and the design of offshore wind turbines. Firstly, the raw data of wind speeds and wave heights prevailing in these different lease areas were obtained. Following this, an in-depth extreme value analysis was performed over different return periods. Two principal methodologies were applied for this comparative study: the block-maxima and the peaks-over-threshold (POT) approaches. Various statistical techniques, including the Gumbel method of moments, Gumbel maximum likelihood, Gumbel least-squares, and the three-parameter GEV, were employed under the block-maxima approach to obtain the distribution parameters. The threshold for the POT approach was defined using the mean residual life method, and the distribution parameters were obtained using the maximum likelihood method. The Gumbel least-squares method emerged as the most conservative estimator of extreme values in the majority of cases, while the POT approach generally yielded lower extreme values compared to the block-maxima approach. However, the results from the POT approach showed large variations based on the selected threshold. This comprehensive study’s findings will provide valuable input for the efficient planning, design, and construction of future offshore wind farms.

A fatigue crack quantification model for metallic structure based on strain monitoring data

Obtaining the real-time fatigue crack length of a metallic structure is the prerequisite of the fatigue life monitoring and residual life estimation for an aircraft. This paper proposed a metallic structure's fatigue crack prediction model using strain monitoring data based on deep learning method. A cycle consistent adversarial network was developed to map the strain monitoring data from experimental measurement with those from finite element modeling. A crack size classification model and a crack length quantification model were proposed to classify the crack size range and identify the exact crack length, respectively. The proposed model was applied to predict the fatigue crack growth in centeral hole metallic plates subjected to random loading spectrum. The results showed that the prediction is effective and accurate.

An Improved XShanker Distribution with Applications to Rainfall and Vinyl Chloride Data

This article is an improvement on the XShanker distribution having a single parameter which is the scale parameter and also in the class of Lindley distributions. It is named Double XShanker following the approach that generated it. The distributional properties which include the non-central moment with the associated statistics, the moment generating function, characteristic function, mean residual life function, stress-strength reliability function, Bonferroni and Lorenz curve functions, odd function, stochastic ordering, distribution of order statistics, and Reny entropy. The parameter was estimated using the method of maximum likelihood. Some visualizations based on theoretical values were presented. Some statistics were computed from theoretical values with convergence behavior recorded. A simulation study was conducted with 1000 samples of different sizes and the behavior observed is that as sample size increases the estimates decrease indicating precision. Data on rainfall and Vinyl chloride were used to validate the usefulness of the suggested distribution.

INVESTIGATION OF RESIDUAL PROPERTIES OF TREATED CAR ENGINE OIL

The lubricating properties of motor oils are of great importance for the normal operation of the crank and gas distribution mechanisms of the engine. In the cylinder-piston group, where the greatest friction forces and loads occur, as a result, the quality of engine oil directly affects the resource of the internal combustion engine (ICE). Of no less importance is the constant presence of a reliable oil film in the contact zone of the support and connecting rod necks of the crankshaft with sliding bearings. In addition to the operating time, the parameters are influenced by external environmental factors, operating conditions and the residual life of the engine at the time of oil filling. Factors affecting the operation are important for the study of oils in order to provide information about the quality of oil and the condition of the car in conjunction with these indicators. To derive oil indicators, it is required to conduct laboratory tests using special equipment in order to give more accurate information about the quality of the engine oil under study.
 Purpose. Determination of the residual oil life and its effect on the mechanisms of the car engine during operation.
 Method or methodology of work. The article used laboratory studies using existing instruments.
 Results. The most informative parameters showing the residual resource of motor oils and their effect on the internal combustion engine are obtained.
 The scope of the results. The obtained results should be used when working with vehicles that use engine oil and premature identification of cause-and-effect relationships during the repair of internal combustion engines.

A quasi Rama distribution

A two-parameter quasi Rama distribution which contains the Rama distribution as particular case has been proposed. Its statistical properties based on moments have been discussed. The hazard rate function, mean residual life function, mean deviations and stochastic ordering of the distribution have been derived and studied. The estimation of parameters using method of moments and maximum likelihood methods has been discussed. A simulation study has been presented to know the performance of maximum likelihood estimates. The goodness of fit of the proposed distribution on two datasets relating to failure times has been presented.