Predictive modeling of track degradation rates considering drainage conditions for maintenance scheduling

As railroad infrastructure becomes older and older and rail transportation is developing towards higher speed and heavier axle, the risk to safe rail transport and the expenses for railroad maintenance are increasing. The railroad infrastructure deterioration (prediction) model is vital to reducing the risk and the expenses. A short-range track condition prediction method was developed in our previous research on railroad track deterioration analysis. It is intended to provide track maintenance managers with two or three months of track condition in advance to schedule track maintenance activities more smartly. Recent comparison analyses on track geometrical exceptions calculated from track condition measured with track geometry cars and those predicted by the method showed that the method fails to provide reliable condition for some analysis sections. This paper presented the enhancement to the method. One year of track geometry data for the Jiulong-Beijing railroad from track geometry cars was used to conduct error analyses and comparison analyses. Analysis results imply that the enhanced model is robust to make reliable predictions. Our in-process work on applying those predicted conditions for optimal track maintenance scheduling is discussed in brief as well.

Traditional static maintenance scheduling based on lifetime data and replacement upon failure is adequate for typical power users. However, in the case of high reliability/availability-oriented industries (e.g., power systems for internet data centers have a desired availability of 0.99999 and, for semiconductor fabrication plants, have availability requirement of 0.9999999), this type of preventive maintenance scheduling is inadequate. A suitable approach in these situations is the adoption of condition-based predictive maintenance. Here the system condition is evaluated by processing the information gathered from the monitors placed at different points in the system, and maintenance is performed only when the failure/malfunction prognosis dictates. In the past, for power systems, voltages, currents, power, temperature and electromagnetic quantities had been monitored along with surface inspection and material quality tests at regular intervals. Diagnostic methods are already in place to indicate problems in industrial power systems by examining these monitored quantities. However, they lack the capability of looking into distant future. With the introduction of modern digital electronics-based smart monitors, the capability of logging power quality data at micro-second intervals, advanced signal processing tools for extracting features from collected data, and data mining techniques, a new horizon in maintenance scheduling has been unveiled. Trending techniques and techniques based on neural networks, when applied to the extracted features, enable us to predict the possible failures of individual equipment and subsystems well before they manifest. This paper considers the problem of evaluating the health indices of components of a power system by making use of the monitored power-quality data and classification techniques. Health index analysis distinguishes the healthy and risky components of the system. Results of these evaluations can be fed as inputs into a system-reliability/availability analysis tool. The reliability analysis enables analysts to decide on prioritization of the maintenance options subject to budget constraints.

Buildings account for 40% of US primary energy consumption and 72% of electricity. Of this total, 50% of the energy consumed in buildings is used for Heating Ventilation and Air-Conditioning (HVAC) systems. Current HVAC systems only condition based on static schedules; rooms are conditioned regardless of occupancy. By conditioning rooms only when necessary, greater efficiency can be achieved. This paper describes POEM, a complete closed-loop system for optimally controlling HVAC systems in buildings based on actual occupancy levels. POEM is comprised of multiple parts. A wireless network of cameras called OPTNet is developed that functions as an optical turnstile to measure area/zone occupancies. Another wireless sensor network of passive infrared (PIR) sensors called BONet functions alongside OPTNet. This sensed occupancy data from both systems are then fused with an occupancy prediction model using a particle filter in order to determine the most accurate current occupancy in each zone in the building. Finally, the information from occupancy prediction models and current occupancy is combined in order to find the optimal conditioning strategy required to reach target temperatures and minimize ventilation requirements. Based on live tests of the system, we estimate ~30.0% energy saving can be achieved while still maintaining thermal comfort.

Owing to its advantages such as high reliability for power supply, flexible operating and dispatching mechanism and convenient transfer breaker, the 3/2 mode of connection has been greatly put into use in 500kv power stations and some 200kv load-center substations. However, more equipment may be subjected to power cut caused by some fault modes as long as the mode of connection is linked with primary equipment or the breaker is under the prophylactic repair mode. Based on the analysis of network topology and on-line identification technology to deal with equipment risk for protection and operation logic, it enables itself to automatically identify the latent fault mode which is contained within power-failure equipment with N-2 qualification or more and remind relevant dispatching working staff in accordance of the prophylactic repair of primary equipment or breaker under the mode of connection, meanwhile, through the combination of static and dynamic security analysis and maintenance scheduling, it will achieve the on-line prevention and control mechanism for equipment risk in large power grid.

The nuclear industry has recently been shifting to value-based maintenance in order to keep nuclear power competitive in the power generation market. A key challenge in value-based maintenance is the optimization of a maintenance schedule. With most components having ten to fifteen available maintenances, the complexity of the optimization grows quite quickly. This paper presents a methodology for combining maintenance effectiveness, cost estimates, failure impacts, and overall reliability data to estimate an expected life cycle cost (LCC) for a component. The maintenance types are categorized into three types: monitoring, wear-rate reducing (e.g. oil change), or life-restoring (e.g. refurbishment). Each maintenance type has a different effect on problem detection, degradation rate, and future life expectancy. A Markov model uses the maintenance effects to estimate the distribution of what state of degradation a component is in at a specific time and concurrently, the component failures, maintenance costs, and failure impacts are tallied up in order to provide an expected life cycle cost for a given maintenance schedule. Optimization of the maintenance schedule is performed using the genetic algorithm where multiple maintenance schedules are simultaneously calculated, compared, and evolved in order to find the lowest expected life cycle costs. The genetic algorithm was selected as a suitable optimization algorithm for its ability to find relatively close approximations to the global optimum with relative ease while concurrently being able to handle non-smooth objective functions.

Many of the components on a gas turbine are subject to fouling and degradation over time due to debris buildup. For example, axial compressors are susceptible to degradation as a result of debris buildup on compressor blades. Similarly, air cooled lube oil heat exchangers incur degradation as a result of debris buildup in the cooling air passageways. In this paper, we develop a method for estimating the degradation rate of a given gas turbine component that experiences recoverable degradation due to normal operation over time. We then establish an economic maintenance scheduling model which utilizes the derived rate and user input economic factors to provide a locally optimal maintenance schedule with minimized operator costs. The rate estimation method makes use of statistical methods combined with historical data to give an algorithm with which a performance loss rate can be extracted from noisy data measurements. The economic maintenance schedule is then derived by minimizing the cost model in user specified intervals and the final schedule results as a combination of the locally optimized schedules. The goal of the combination of algorithms is to maximize component output and efficiency, while minimizing maintenance costs. The rate estimation method is validated by simulation where the underlying noisy data measurements come from a known probability distribution. Then, an example schedule optimization is provided to validate the economic optimization model and show the efficacy of the combined methods.

In the past few decades, the railway infrastructure has been widely expanded in urban and rural areas, making it the most complex matrix of rail transport networks. Safe and comfortable travel on railways has always been a common goal for transportation engineers and researchers, and requires railways in excellent condition and well-organized maintenance practices. Degradation of rail tracks is a main concern for railway organizations as it affects the railway’s behaviour and its parameters, such as track geometry, speed, traffic and loads. Therefore, the prediction of the degradation of rail tracks is very important in order to optimise maintenance needs, reduce maintenance and operational costs of railways, and improve rail track conditions.This paper provides a comprehensive review of rail degradation prediction models, their parameters, and the strengths and weaknesses of each model. A comprehensive discussion of existing research and a comparison of different models of degradation of rail tracks is also provided. Finally, this review presents concluding remarks on the limitations of existing studies and provides recommendations for further research and appraisal practices.

Background Systems operating in industrial environments are often exposed to two concurrent failure mechanisms: gradual degradation and sudden traumatic events. Maintenance decisions must account for these competing risks while controlling inspection, replacement, and failure costs. This study develops a quantitative framework to determine an economically efficient maintenance strategy under such conditions. Methods A discrete-state model is formulated with three operational conditions: Good, Degraded, and Failed. Transitions between states are driven by the system’s degradation trajectory and the occurrence of traumatic failures. A long-term expected cost model is established, incorporating inspection costs, preventive replacement costs, and failure-related losses. Dynamic programming is used to identify the policy that minimizes the expected cost per unit time. The optimisation evaluates how inspection intervals, degradation rates, and traumatic event probabilities influence replacement decisions. Results The optimisation results indicate that the cost-effective policy depends strongly on the interaction between degradation progression and the frequency of traumatic events. Higher rates of traumatic events lead to earlier preventive replacement, while intermediate degradation rates make the inspection interval the primary driver of cost reduction. The model delineates the parameter regions in which periodic inspection is justified and quantifies the cost effects of different maintenance schedules. Conclusions The proposed dynamic programming approach provides a structured method for selecting inspection and replacement strategies in systems subject to multiple failure mechanisms. The results offer decision-support guidance for maintenance planning, particularly in environments where degradation and traumatic events jointly affect system reliability and operating costs.

The traffic loading is a typical cyclic loading with variable confining pressure and always lasts long, and is believed to have a significant effect on the subgrade soil, especially for the subgrade filled with soft clay. However, the mechanics have yet to be fully understood. Given that the duration of traffic loading lasts long enough, the partially drained conditions should be considered for the soft clay under the long-term cyclic loading, rather than the undrained conditions adopted commonly by most previous researches. In this study, 28 cyclic tests were conducted on the remolded saturated soft clay, utilizing both constant confining pressure and variable confining pressure under partially drained and undrained conditions. The effect of cyclic confining pressure and different drainage conditions is analyzed in relation to the evolution of pore pressure and deformation behaviors. Incorporating both the cyclic confining pressure and cyclic stress ratio, a concise pre-diction model of permanent strain is proposed and validated by the experimental results.

With the increasing penetration of both grid connected and standalone photovoltaic (PV) systems in Pakistan to cater the shortfall of energy, it is necessary to determine the performance and durability of already installed PV systems in different climates of country. This work aims to analyze the performance of first grid connected PV power plant located in Islamabad, Pakistan. The performance degradation of PV plants primarily depends on PV modules type (technology and design), regional climatic factors and field operating conditions. In this paper, performance ratio (PR) and degradation rate have been calculated from meteorological and performance data of plant to investigate the energy efficiency and modules quality over the course of operational years. Temperature losses due to module operating temperature were also estimated that primarily contribute to overall energy losses. This evaluation is of paramount importance for estimating the cost effect of the performance, identification of the underlying performance degradation and energy losses mechanisms, that help investors to choose climate specific modules technology and design for future plants and also to make efficient scheduling for preventive maintenance.

The proper scheduling of track maintenance and renewal operations is dependent on the accuracy of predictive maintenance outputs which, in turn, rely on the appropriate modelling of the track geometry degradation process. This paper explores the incidence and representativeness of the alerts generated due to local defects of track geometry and the modelling of their degradation rates between consecutive tamping operations. A software tool was developed and used to process track inspection data collected over 12 years of operation of the main Portuguese line (Lisbon-Porto) on a total of 727 km of single-track lines where speeds vary between 30 and 220 km/h. Linear regressions are found to be suitable to model the degradation rates of local defects of longitudinal levelling, alignment and twist in the vast majority of cases, although 20–30% of the time degradation patterns prove to be non-linear even in the short term. The local defects of alignment degrade more rapidly and trigger 80% of the unplanned maintenance needs. Modelling the evolution of local defects, and not only the standard deviation on sections 200 m long, allows us to anticipate unplanned maintenance and tackle unavailability costs.

Assessment of uncertainties and variations in PV modules degradation rates and lifetime predictions using physical models

In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.

The authors’ research group has undertaken for about a lustrum a comprehensive research project, focusing on both experimental characterization and numerical predictive modelling of the self-healing capacity of a broad category of cementitious composites, ranging from normal strength concrete to high performance cementitious composites reinforced with different kinds of industrial (steel) and natural fibers. In this paper reference will be made to normal strength concrete: both autogenous healing capacity has been considered and self-healing engineered through the use of crystalline admixtures. A tailored methodology has been employed to characterize the healing capacity of the investigated concrete, based on comparative evaluation of the mechanical performance measured through 3-point bending tests. Tests have been performed to pre-crack the specimens to target values of crack opening, and after scheduled conditioning times to selected exposure conditions, including water immersion and exposure to open air. The healing capacity has been quantified by means of the definition and calculation of suitable “healing indices”, based on the recovery of the mechanical properties, including load bearing capacity, stiffness, ductility, toughness etc. and correlated to the amount of crack closure also “estimated” through suitable indirect methodologies. Chemical characterization of the healing products by means of SEM has been performed to understand the different mechanisms governing the observed phenomena and also discriminate among the different amounts of recovery of the different mechanical properties. As a further step a predictive modelling approach, based on modified microplane model, has been formulated. This incorporates the self-healing effects, in particular, the delayed cement hydration, as well as the effects of cracking on the diffusivity and the opposite repairing effect of the self-healing on the micro-plane model constitutive laws. The whole experimental and numerical investigation represents a comprehensive and solid step towards the reliable and consistent incorporation of self-healing concepts and effects into a durability-based design framework for engineering applications made of or retrofitted with self-healing concrete and cementitious composites. DOI 10.21012/FC9.224 L. Ferrara, G. di Luzio and V. Krelani

<div class="section abstract"><div class="htmlview paragraph">The degradation rate of a Li-ion battery is a complex function of temperature and charge/discharge rates over its lifetime. There is obviously a keen interest in predictive electrochemical ageing models that account for known degradation mechanisms, primarily linked with the Solid Electrolyte Interface (SEI) formation and Li-plating, which are highly dependent on the cell temperature. Typically, such ageing models are formulated and employed at pack or cell level, neglecting intra-cell and cell-to-cell thermal and electrical non-uniformities. On the other hand, thermal management techniques can mitigate ageing by maintaining the battery pack within the desired temperature window either by cooling or heating. Inevitably, the cooling of the battery pack by conventional heat exchangers will introduce temperature non-uniformities that may in turn result in undesired intra-cell and/or cell-to-cell health non-uniformities. In this work, an extended multi-dimensional modeling approach is adopted to address intra-cell and cell-to-cell non-uniformities in terms of localized temperature and current. This extension is a key enabler for the prediction of localized temperature and overpotential that determine the rate of the anode deterioration via the formation of Li-plating and SEI. The electrochemical and ageing model parameters are obtained by a combination of literature data and in-house electro-thermal measurements of a pouch cell and a battery module. The model is developed within a commercial software. The study covers thermal management for both active cooling and active heating during battery fast-charging. The results show that inevitable temperature gradients are likely to result in degradation rates that are strongly non-uniform leading to unexpectedly high-capacity loss and resistance increase. The proposed methodology could eventually support the prediction of the battery lifespan as function of thermal design and control parameters at an early development phase.</div></div>