Contribution to prediction of soft and hard failure occurrence in combustion engine using oil tribo data

When assessing technical systems, we apply different condition indicators. If we take into account indirect diagnostic indicators, lubricant seems to be a very good source of different information. Where the oil is part of a system, it is possible to get the information about the oil condition as well as the system itself. In this article we focus on the system of piston combustion engine and tribodiagnostic data. The indirect diagnostic information is the concentration level of contaminating particles in the oil. We observe specific particles of oil contamination - soot which is formed in the operation as a by-product during fuel combustion. The soot contaminates the oil significantly and the soot concentration shows under what conditions the system was operated. It also indicates that the system condition gradually deteriorates. We have a statistically very interesting set of data from the operation of heavy off-road vehicles. The recorded soot operation data depend on a few operation values: kilometres [km], days [day] and moto-hours [Mh]. Modelling a soft and a hard failure which might occur the moment the concentration reaches its critical value is performed with selected stochastic diffusion processes, namely the Wiener process with drift and the Ornstein-Uhlnbeck process. The main aim and contribution of our article is to estimate and study the distribution of the first hitting time of a critical threshold and to determine the moment when the soft and hard failure occurs in a vehicle. We also try to estimate the engine useful residual life. We are aware, however, that there are numerous possibilities of using these results.     

Condition‐based Maintenance Optimization Using Neural Network‐based Health Condition Prediction

Artificial neural network (ANN)‐based methods have been extensively investigated for equipment health condition prediction. However, effective condition‐based maintenance (CBM) optimization methods utilizing ANN prediction information are currently not available due to two key challenges: (i) ANN prediction models typically only give a single remaining life prediction value, and it is hard to quantify the uncertainty associated with the predicted value; (ii) simulation methods are generally used for evaluating the cost of the CBM policies, while more accurate and efficient numerical methods are not available, which is critical for performing CBM optimization. In this paper, we propose a CBM optimization approach based on ANN remaining life prediction information, in which the above‐mentioned key challenges are addressed. The CBM policy is defined by a failure probability threshold value. The remaining life prediction uncertainty is estimated based on ANN lifetime prediction errors on the test set during the ANN training and testing processes. A numerical method is developed to evaluate the cost of the proposed CBM policy more accurately and efficiently. Optimization can be performed to find the optimal failure probability threshold value corresponding to the lowest maintenance cost. The effectiveness of the proposed CBM approach is demonstrated using two simulated degradation data sets and a real‐world condition monitoring data set collected from pump bearings. The proposed approach is also compared with benchmark maintenance policies and is found to outperform the benchmark policies. The proposed CBM approach can also be adapted to utilize information obtained using other prognostics methods. Copyright © 2012 John Wiley & Sons, Ltd.     

Modelling of degradation and a soft failure moment during the operation of a supercapacitor applying selected diffusion processes

An important requirement imposed on storage batteries nowadays is to have sufficient capacity. At the same time a high level of availability, reliability and safety is required as well. Our intention is to determine a capacity degradation threshold and the moment the soft failure of a graphite supercapacitor (SC) occurs. If we do not take into account the idle state, the functioning of the supercapacitor might be expressed by charging and discharging processes under different operating conditions given by the allowed extent of SC design. When looking for the degradation threshold and the moment of soft failure occurrence we performed the experimental part of measuring in the climatic chamber. We performed and recorded the processes of SC charges and discharges at different temperatures: 40 °C, 25 °C and  −42 °C and at different charging and discharging currents: 2A, 4A, 6A and 8A. The experimental results were used to model mathematically the SC discharge process. Appropriate tools used for SC discharge are diffusion processes. In this case we apply a Wiener process with drift and an Itô process.     

Reliability modeling of multi‐state degraded repairable systems and its applications to automotive systems

In this paper, we presented a continuous‐time Markov process‐based model for evaluating time‐dependent reliability indices of multi‐state degraded systems, particularly for some automotive subsystems and components subject to minimal repairs and negative repair effects. The minimal repair policy, which restores the system back to an “as bad as old” functioning state just before failure, is widely used for automotive systems repair because of its low cost of maintenance. The current study distinguishes with others that the negative repair effects, such as unpredictable human error during repair work and negative effects caused by propagated failures, are considered in the model. The negative repair effects may transfer the system to a degraded operational state that is worse than before due to an imperfect repair. Additionally, a special condition that a system under repair may be directly transferred to a complete failure state is also considered. Using the continuous‐time Markov process approach, we obtained the general solutions to the time‐dependent probabilities of each system state. Moreover, we also provided the expressions for several reliability measures include availability, unavailability, reliability, mean life time, and mean time to first failure. An illustrative numerical example of reliability assessment of an electric car battery system is provided. Finally, we use the proposed multi‐state system model to model a vehicle sub‐frame fatigue degradation process. The proposed model can be applied for many practical systems, especially for the systems that are designed with finite service life.     

Condition based maintenance in the context of opportunistic maintenance

Condition based maintenance (CBM) uses the operating condition of a component to predict a failure event. Compared to age based replacement (ABR), CBM usually results in higher availability and lower maintenance costs, since it tries to prevent unplanned downtime and avoid unnecessary preventive maintenance activities for a component. However, the superiority of CBM remains unclear in multi‐component systems, in which opportunistic maintenance strategies can be applied. Opportunistic maintenance aims to group maintenance activities of two or more components in order to reduce maintenance costs. In a serial system, this may also result in less downtime of the production line. The aim of this paper is to examine the impact of opportunistic maintenance on the effectiveness of CBM. We simulate a small system consisting of three components in series and vary the number of components under a CBM policy, the length of the opportunistic maintenance zone, the cost benefits of grouping maintenance activities, and the chance of a failure occurrence within a preventive maintenance (PM) interval. The results show that within the current experimental settings, CBM remains cost effective in the multi‐component serial system, but is less effective than ABR in grouping maintenance activities. When the chance of failure is small and the length of the opportunistic maintenance zone is large, ABR may even be a better option if line productivity is important.     

Engineering System Safety Analysis and Synthesis Using the Fuzzy Rule‐based Evidential Reasoning Approach

The main objective of this paper is to propose a framework for modelling, analysing and synthesizing system safety of engineering systems or projects on the basis of a generic rule‐based inference methodology using the evidential reasoning (RIMER) approach. The framework is divided into two parts. The first one is for fuzzy rule‐based safety estimation, referred to as a fuzzy rule‐based evidential reasoning (FURBER) approach. The second one is for safety synthesis using the evidential reasoning approach. In the FURBER framework, parameters used to define the safety level, including failure rate, failure consequence severity and failure consequence probability are described using fuzzy linguistic variables; a fuzzy rule base designed on the basis of a belief structure is used to capture uncertainty and nonlinear relationships between these three parameters and the safety level; and the inference of the rule‐based system is implemented using the evidential reasoning algorithm. Then the following steps involve synthesizing safety at higher levels of an engineering system to integrate all possible causes to a specific technical failure, or estimates made by a panel of experts. The synthesis is also based on the evidential reasoning approach. The final step describes the analysis and interpretation of the final synthesized safety of a system. The above framework has been applied to modelling system safety of an offshore and marine engineering system: the floating production storage offloading (FPSO) system. A series of case studies of collision risk between a FPSO and a shuttle tanker due to technical failure during a tandem offloading operation is used to illustrate the application of the proposed model. Copyright © 2005 John Wiley & Sons, Ltd.     

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

Most 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.     

Residual Useful Life Estimation by a Data‐Driven Similarity‐Based Approach

This paper is to provide a general data‐driven, similarity‐based approach for residual useful life estimation for industrial components or systems. Degradation signal data from failure dynamic systems are used to create a database of reference trajectory patterns. Variable weights are assigned to associated points before pointwise similarity computation. Distance score and weight are computed by calculating the fuzzy similarity between test trajectory pattern and reference trajectory patterns. Remaining useful life then could be predicted through aggregating those failure references' residual lifetimes in a weighted sum and updated as time goes by. The potentiality of this approach is illustrated on a problem of contact resistance degradation data. The results indicate that there is no significant difference between Gaussian function and General bell shaped function. Copyright © 2016 John Wiley & Sons, Ltd.     

Mechanism modelling of shot peening effect on fatigue life prediction

A new mechanism modelling is proposed in this paper to explain the shot peening effect on fatigue life predictions of mechanical components. The proposed methodology is based on the crack growth analysis of shot peened specimens, which are affected by the interaction of surface roughness and residual stress produced during the shot peening process. An asymptotic stress intensity factor solution is used to include the surface roughness effect and a time‐varying residual stress function is used to change the crack tip stress ratio during the crack propagation. Parametric studies are performed to investigate the effects of surface roughness and the residual stress relaxation rate. Following this, a simplified effective residual stress model is proposed based on the developed mechanism modelling. A wide range of experimental data is used to validate the proposed mechanism modelling. Very good agreement is observed between experimental data and model predictions.     

Copula‐based reliability and safety analysis of safety‐critical systems with dependent failures

A safety‐critical system (SCS) is a system whose failure could result in a certain serious consequence, such as loss of life and significant damage to property or environment. Examples of SCSs abound in real‐world applications, such as medical instruments, emergency shutdown systems, and fire/gas detection systems. An SCS can assume 1 of 3 states: working, safe failure, and dangerous (or unsafe) failure. To analyze reliability and safety of SCSs accurately, we build multi‐state models of an SCS and its constituent units. The dependent relationships (nonlinear correlation) of different parts within a safety‐critical unit as well as across the units are modeled using the Copula method. Formulas computing reliability and safety indexes of a safety‐critical unit and of safety‐critical series or parallel systems are derived. Numerical examples are provided to demonstrate the application of the proposed methodology.     

Interior failure mechanism and life prediction of surface‐treated 17Cr‐Ni steel under high and very high cycle fatigue

The understanding of very high cycle fatigue (VHCF) mechanisms is critical to the development of life prediction approach. For this purpose, high cycle fatigue (HCF) and VHCF properties of a surface‐treated 17Cr‐Ni steel were investigated under axial loading with stress ratio of 0. This steel exhibits the constantly decreasing S‐N characteristics associated with the inclusion‐fisheye induced failure under the HCF and the inclusion‐FGA (fine granular area)‐fisheye induced failure under the VHCF. The cyclic pressing plays an important role in the FGA formation process, but the FGA still can be observed for the stress ratio of zero due to the slight crack closure effect. Two life modelling approaches associated with related failure mechanisms in the HCF and VHCF regimes are proposed based on the agreement between experimental and predicted results.     

Finite element modelling of cracked inelastic shells with large deflections: two‐dimensional and three‐dimensional approaches

Higher utilization of structural materials leads to a need for accurate numerical tools for reliable predictions of structural response. In some instances, both material and geometrical non‐linearities are allowed for, typically in assessments of structural collapse or residual strength in damaged conditions. The present study addresses the performance of surface‐cracked inelastic shells with out‐of‐plane displacements not negligible compared to shell thickness. This situation leads to non‐linear membrane force effects in the shell. Hence, a cracked part of the shell will be subjected to a non‐proportional history of bending moment and membrane force. An important point in the discretization of the problem is whether a two‐dimensional model describes the structural performance sufficiently, or a three‐dimensional model is required. Herein, the two‐dimensional modelling is performed by means of a Mindlin shell finite element. The cracked parts are accounted for by means of inelastic line spring elements. The three‐dimensional models employ eight‐noded solid elements. These models also account for ductile crack growth due to void coalescence by means of a modified Gurson–Tvergaard constitutive model, hence providing detailed solutions that the two‐dimensional simulations can be tested against. Using this, the accuracy of the two‐dimensional approach is checked thoroughly. The analyses show that the two‐dimensional modelling is sufficient as long as the cracks do not grow. Hence, using fracture initiation as a capacity criterion, shell elements and line springs provide acceptable predictions. If significant ductile tearing occurs before final failure, the line spring ligaments have to be updated due to crack growth.     

Subspace recycling accelerates the parametric macro‐modeling of MEMS

A fast computational technique that speeds up the process of parametric macro‐model extraction is proposed. An efficient starting point is the technique of parametric model order reduction (PMOR). The key step in PMOR is the computation of a projection matrix V, which requires the computation of multiple moment matrices of the underlying system. In turn, for each moment matrix, a linear system with multiple right‐hand sides has to be solved. Usually, a considerable number of linear systems must be solved when the system includes more than two free parameters. If the original system is of very large size, the linear solution step is computationally expensive. In this paper, the subspace recycling algorithm outer generalized conjugate residual method combined with generalized minimal residual method with deflated restarting (GCRO‐DR), is considered as a basis to solve the sequence of linear systems. In particular, two more efficient recycling algorithms, G‐DRvar1 and G‐DRvar2, are proposed. Theoretical analysis and simulation results show that both the GCRO‐DR method and its variants G‐DRvar1 and G‐DRvar2 are very efficient when compared with the standard solvers. Furthermore, the presented algorithms overcome the bottleneck of a recently proposed subspace recycling method the modified Krylov recycling generalized minimal residual method. From these subspace recycling algorithms, a PMOR process for macro‐model extraction can be significantly accelerated. Copyright © 2013 John Wiley & Sons, Ltd.     

Crack incubation in shot peened AA7050 and mechanism for fatigue enhancement

Shot peening is a dynamic cold‐working process involving the impingement of peening media onto a substrate surface. Shot peening is commonly used as a surface treatment technique within the aerospace industry during manufacturing to improve fatigue performance of structural components. The compressive residual stress induced during shot peening results in fatigue crack growth retardation, improving the performance of shot‐peened components. However, shot peening is a compromise between the benefit of inducing a compressive residual stress and causing detrimental surface damage. Because of the relatively soft nature of AA7050‐T7451, shot peening can result in cracking of the constituent precipitate particles, creating an initial damage state. The aim of this paper is to understand the balance and fundamentals of these competing phenomena through a comparative study throughout the fatigue lifecycle of baseline versus shot‐peened AA7050‐T7451. Microstructure and surface topology characterization and comparison of the baseline and shot‐peened AA7050‐T7451 has been performed using scanning electron microscopy, electron backscatter diffraction, energy dispersive spectroscopy, and optical profilometry techniques. A residual stress analysis through interrupted fatigue of the baseline and shot‐peened AA7050‐T7451 was completed using a combination of X‐ray diffraction and nanoindentation. The fatigue life performance of the baseline versus shot‐peened material has been evaluated, including crack initiation and propagation. Subsurface particles crack upon shot peening but did not incubate into the matrix during fatigue loading, presumably due to the compressive residual stress field. In the baseline samples, the particles were initially intact, but upon fatigue loading, crack nucleation was observed in the particles, and these cracks incubated into the matrix. In damage tolerant analysis, an initial defect size is needed for lifetime assessment, which is often difficult to determine, leading to overly conservative evaluations. This work provides a critical assessment of the mechanism for shot peening enhancement for fatigue performance and quantifies how incubation of a short crack is inhibited from an initially cracked particle into the matrix within a residual stress field.     

Dynamically Tuning Particle Interactions and Assemblies at Soft Interfaces: Reversible Order–Disorder Transitions in 2D Particle Monolayers

Particles trapped at fluid interfaces experience long‐range interactions that determine their assembly behavior. Because particle interactions at fluid interfaces tend to be unusually strong, once particles organize themselves into a 2D assembly, it is challenging to induce changes in their microstructure. In this report, a new approach is presented to induce reversible order–disorder transitions (ODTs) in the 2D monolayer of colloidal particles trapped at a soft gel–fluid interface. Particles at the soft interface, consisting of a nonpolar superphase and a weakly gelled subphase, initially form a monolayer with a highly ordered structure. The structure of this monolayer can be dynamically varied by the addition or removal of the oil phase. Upon removing the oil via evaporation, the initially ordered particle monolayer undergoes ODT, driven by capillary attractions. The ordered monolayer can be recovered through disorder‐to‐order transition by simply adding oil atop the particle‐laden soft interface. The possibility to dynamically tune the interparticle interactions using soft interfaces can potentially enable control of the transport and mechanical properties of particle‐laden interfaces and provide model systems to study particle‐laden soft interfaces that are relevant to biological tissues or organs.     

A Bayesian framework for accelerated reliability growth testing with multiple sources of uncertainty

Reliability growth tests are often used for achieving a target reliability for complex systems via multiple test‐fix stages with limited testing resources. Such tests can be sped up via accelerated life testing (ALT) where test units are exposed to harsher‐than‐normal conditions. In this paper, a Bayesian framework is proposed to analyze ALT data in reliability growth. In particular, a complex system with components that have multiple competing failure modes is considered, and the time to failure of each failure mode is assumed to follow a Weibull distribution. We also assume that the accelerated condition has a fixed time scaling effect on each of the failure modes. In addition, a corrective action with fixed ineffectiveness can be performed at the end of each stage to reduce the occurrence of each failure mode. Under the Bayesian framework, a general model is developed to handle uncertainty on all model parameters, and several special cases with some parameters being known are also studied. A simulation study is conducted to assess the performance of the proposed models in estimating the final reliability of the system and to study the effects of unbiased and biased prior knowledge on the system‐level reliability estimates.     

Optimal variability sensitive condition-based maintenance with a Cox PH model

Condition based maintenance (CBM) is an important maintenance strategy in practice. In this paper, we propose a CBM method to effectively incorporate system health observations into maintenance decision making to minimise the total maintenance cost and cost variability. In this method, the system degradation process is described by a Cox PH model and the proposed framework includes simulation of failure process and maintenance policy optimisation using adaptive nested partition with sequential selection (ANP-SS) method, which can adaptively select or create the most promising region of candidates to improve the efficiency. Different from existing CBM strategies, the proposed method relaxes some restrictions on the system degradation model and taking the cost variation as one of the optimisation objectives. A real industry case study is used to demonstrate the effectiveness of our framework.     

Modelling of Protein‐Protein Interactions of Bone Morphogenetic Protein‐2 (BMP‐2) by 3D‐Rapid Prototyping

Recently we were able to apply the technique of 3D‐Rapid Prototyping (3D‐RP) to the construction of highly accurate three‐dimensional plastic models of biomolecules [Laub, M. et al. (2001), Materialwiss. Werkstofftech. 32, 926]. These models are derived from x‐ray crystallographic data and therefore represent exact replicas of the depicted molecules. Due to their accuracy these models should be suitable for the modelling of protein‐protein‐interactions. In a first study using 3D‐Rapid Prototyping models of bone morphogenetic protein‐2 (BMP‐2) we were able to identify a novel structural motive on the concave side of this protein which we termed anthelix since a left‐handed helix (radius ca. 0.8–1 nm, pitch 8–9 nm) can be fitted into this groove. Based on these structural findings we identified a 15mer polypeptide (KNMTPYRSPPPYVPP) from the Brookhaven database as a potential physiological ligand. Molecular docking studies using a geometric recognition approach confirmed the anthelix as a possible binding site for this peptide. However in affinity chromatography experiments no binding between BMP‐2 and the immobilized peptide was observed. As the question arose whether 3D‐Rapid Prototyping is in general suitable for modelling protein‐protein interaction we used dimeric BMP‐2 to study exemplary monomer‐dimer interaction. Molecular docking studies using the monomeric BMP‐2 subunits predicted a structure which is nearly identical to that found in dimeric BMP‐2 (root mean square deviation < 1 Å) proving the suitability of geometric docking. 3D‐RP‐BMP‐2‐monomers (size 140 mm × 75 mm × 65; magnification ca. 22 × 10⁶ fold) constructed from dimeric BMP‐2 could be assembled by hand yielding a structure highly homologous to dimeric BMP‐2. Differences between the 3D‐Rapid Prototyping model of dimeric BMP‐2 and the assembled monomers arose in several gaps at the interface between the two monomers which are not visible in the dimeric structure. These gaps can be explained by the way the solvent‐accessible molecular surface is generated. During this process an exterior probe sphere is rolled over the spherical atoms of the molecule. Distances between the monomers smaller than the diameter of this sphere are bridged thus resulting in a coherent surface. We conclude that 3D‐Rapid Prototyping is in general eligible for the modelling of protein‐protein‐interaction though there are further efforts needed to increase our understanding of this process.     

An engineering approach to fatigue analysis based on elastic‐plastic fracture mechanics

Nowadays cast iron components are widely used in highly stressed structures. Component lifetime is strongly influenced by inhomogeneities caused by the material's microstructure and the manufacturing process (graphite particles, (micro‐)shrinkage pores, inclusions). Inhomogeneities often act as a fatigue crack starter. Lifetime until failure may be divided into stages for crack initiation, short and long crack growth. Initiation of a crack of technical size (a ≈ 1mm) is often dominated by the growth of short cracks. The paper presents an approach to analyse the mechanically short fatigue crack growth based on elastic‐plastic fracture mechanics considering the closure behaviour of short cracks. The effective J‐integral range is used as a crack driving force. Finite element analysis results as well as analytical solutions to approximate the crack driving force are presented. The application of the approach is successfully demonstrated for cast iron material EN‐GJS‐400‐18‐LT using data from fatigue tests, microstructure and fracture surface analyses to assess the fatigue life.     

Residual stress/strain evolution due to low‐cycle fatigue by removing local material volume and optical interferometric data

Three experimental methods, based on optical interferometric measurements of deformation response to local material removing, have been implemented for residual stresses determination. Two first techniques are employed to characterize initial residual stress values and their evolution near welded joints of aluminium plates under low‐cycle fatigue. The hole‐drilling method gives high‐accurate dependencies between residual stress components and number of cycles. The second approach comprises cracks modelling by narrow notches to describe residual stress distributions in more wide spatial range near the weld. The results demonstrate residual stress evolution is of complex character and cannot be uniquely qualified as a gradual relaxation. Besides, the secondary hole drilling method is developed and used as a fast and reliable tool to quantify the redistribution of residual strains near cold‐expanded holes due to low‐cycle fatigue. Dependencies of circumferential residual strains along the secondary hole edge versus number of cycles are constructed.