Maintenance Threshold Research Articles

Condition‐based maintenance policy for a multi‐component system considering stochastic dependence and quality loss

AbstractThe reliability evaluation of multi‐component systems has attracted increasing concern in academic and industrial circles. However, the research maintenance optimization for multi‐component systems is still underexplored due to the curse of dimensionality. This paper makes a novel contribution to the existing literature by proposing a condition‐based maintenance (CBM) policy for multi‐component systems with joint consideration of stochastic dependence and quality loss. The system is composed of critical and auxiliary components. The deterioration process of the critical component can be accelerated when the auxiliary one degrades to a certain state. Based on the states of the components, the operation cost of the system owing to quality loss is analyzed, and the operation cost is used as the threshold for the preventive maintenance (PM) decision. Semi‐regenerative technology is used to model the evolution process of the system, and the long‐run average cost is calculated. The optimal inspection period and PM threshold are derived by minimizing the long‐run average cost, and the stationary distribution of the system state under the optimal long‐run average cost is derived with a proposed algorithm. Sensitivity analysis is conducted to reveal the most sensitive parameter. Finally, an illustrative example is presented, and a comparison experiment with other maintenance policies is conducted to demonstrate the effectiveness of the proposed policy.

Severe hyponatremia in obstetrics: Presentations and outcome. Retrospective cohort over 10 years.

Hyponatremia in labor and the postpartum period is generally underreported. In this study we aimed to identify attributable causes of severe hyponatremia in an obstetric population and define maternal outcomes. This was a retrospective cohort study, in a single center over a period of 10 years. The study setting was an NHS maternity trust serving a multi-ethnic population in southwest London. The hospital cared for 53 649 pregnant women in the study period. All patients with severe hyponatremia in labor and immediate postpartum period from January 2011 until December 2020 were identified from the laboratory database. Individual case notes were analyzed for fluid use, use and duration of oxytocin, medical conditions, and delivery details. Severe peripartum hyponatremia was identified in 77 patients. A total of 28% of those analyzed required ITU admission for electrolyte correction, of whom 6% presented with severe neurologic symptoms, making symptoms a poor marker of severity (P = 0.051). The main association was to oxytocin and intravenous fluid infusions (80% and 86%, respectively), only one-third of patients had pre-eclampsia. Other associated medical conditions did not appear to have a significant influence on disease course nor prognosis (P = 0.359). Hyponatremia during labor can have detrimental effect on maternal outcomes. Lower threshold for testing and diligent maintenance of fluid balance charts during labor can help with primary prevention.

A novel TD3 for solving multi-level imperfect maintenance optimization problem

Abstract As we all know, multi-level imperfect maintenance strategy is usually more effective than single-level maintenance strategy for the actual production machines. At the same time, in the previous multi-level maintenance strategies, the majority of maintenance models only consider the constant maintenance thresholds, while variable maintenance thresholds are usually ignored. Under these contexts, a novel multi-level imperfect maintenance model with variable preventive maintenance (PM) thresholds, variable overhaul maintenance (OM) thresholds and variable number of PMs in each OM cycle is established. In order to deal with the concerned problem, a novel twin delayed deep deterministic policy gradient (TD3) algorithm that is a kind of reinforcement learning is designed, renamed as NTD3. Finally, through numerical simulation, we can find that (1) the average improvements between the proposed maintenance strategy and other three traditional strategies in the average cost rate (ACR) are 11.50%, 595.91% and 5.16%, respectively; and (2) the average improvement between the proposed NTD3 and other random search method is 5.53%. Thus, the effectiveness of the proposed maintenance strategy and the superiority of proposed NTD3 are all demonstrated.

Joint optimization of job scheduling, condition-based maintenance planning, and spare parts ordering for degrading production systems

The system degrades gradually due to intrinsic properties and the external environment while processing scheduling jobs. Maintenance plays an important role in restoring system performance during the job scheduling process. In contrast to traditional integration studies of scheduling and maintenance, this study further considers spare parts inventory in integrated decision-making. By integrating spare parts ordering information, engineers can meticulously organize maintenance and spare parts replenishment, thereby bolstering system reliability and ensuring smooth production scheduling. This paper presents a mathematical model that jointly optimizes job scheduling, condition-based maintenance planning, and spare parts ordering for deteriorating production systems. The system's degradation state and spare parts inventory state are identified and revealed at the end of the scheduling job. A condition-based maintenance and spare parts ordering policy is triggered based on the captured joint state. Subsequently, we developed a joint optimization model to determine the optimal maintenance thresholds, spare parts ordering thresholds, and job sequences, aimed at minimizing the total weighted expected cost within the scheduling horizon. Finally, an effective genetic algorithm is proposed, and a practical case study involving a rolling mill system is utilized to validate the proposed policy.

Application of Automated Pavement Inspection Technology in Provincial Highway Pavement Maintenance Decision-Making

Taiwan’s provincial highways span approximately 5000 km and are crucial for connecting cities and towns. As pavement deteriorates over time and maintenance funds are limited, efficient pavement inspection and maintenance decision-making are challenging. Traditional inspections rely on manual visual assessments, consuming significant human resources and time without providing quantitative results. This study addresses current maintenance practices by introducing automated pavement damage detection technology to replace manual surveys. This technology significantly improves inspection efficiency and reduces costs. For example, traditional methods inspect 1 km per day, while automated survey vehicles cover 4 km per day, increasing efficiency fourfold. Additionally, automated surveys reduce inspection costs per kilometer by about 1.7 times, lowering long-term operational costs. Inspection results include the crack rate, rut depth, and roughness (IRI). Using K-means clustering analysis, maintenance thresholds for these indicators are established for decision-making. This method is applied to real cases and validated against actual maintenance decisions, showing that the introduced detection technology efficiently and objectively guides maintenance decisions and meets the needs of maintenance units. Finally, the inspection results are integrated into a pavement management platform, allowing direct maintenance decision-making and significantly enhancing management efficiency.

Novel condition-based opportunistic maintenance policy for series systems with dependent competing failure processes

This paper investigates a novel condition-based opportunistic maintenance model for a two-component series system. The system comprises two non-identical components, that is, Component 1 and Component 2, suffering from shocks with different intensities. The two components are subject to dependent competing failure processes, that is, soft and hard failures. The component fails whichever type of failure process occurs. At each inspection, the replacement decision for components is made according to the condition of both components. In addition, when Component 1 is preventively replaced and Component 2 reaches the opportunistic maintenance threshold, opportunistic maintenance (OM) is implemented on Component 2. The optimal inspection interval, preventive replacement (PR) threshold, and OM threshold are derived by minimizing the long-run expected maintenance cost rate. Finally, the superiority of the proposed maintenance model is verified by an illustrative example.

A novel equipment remaining useful life prediction approach considering dynamic maintenance threshold

AbstractIn conventional remaining useful life (RUL) prediction approaches grounded on maintenance, the maintenance threshold is typically established as a stationary value. However, the actual maintenance threshold may exceed its preset value due to the uncertainty of degradation and other factors. Therefore, it is necessary to consider the dynamic maintenance threshold to improve the precision of remaining useful life prediction. By considering the Wiener process, the maintenance threshold error is introduced to reflect the dynamic nature of the maintenance threshold. The influence of maintenance on degradation amount, degradation rate, and degradation path are comprehensively considered to establish a multi‐stage maintenance‐affected degradation process model. The RUL formula of the equipment is derived using the first hitting time (FHT). The maximum likelihood estimation (MLE) approach and Bayesian theory are employed to estimate the model's parameters. The proposed approach is validated using simulation data and gyroscope degradation data. The outcomes reveal that the proposed approach can significantly enhance the precision of life prediction for the equipment.

Optimal condition-based opportunistic maintenance policy for two-component systems considering common cause failure

This paper introduces a maintenance optimization model designed for a system comprising two distinct components that are arranged in series and susceptible to common cause failure. Both the components undergo dependent degradation and shock processes whose performance levels are periodically inspected. A condition-based opportunistic maintenance policy is considered, indicating that either preventive or corrective maintenance may be conducted depending on the deterioration state observed during inspections. Meanwhile, when maintenance is performed on one component, the other can be opportunistically maintained. An optimization problem is then formulated under the framework of semi-Markov decision processes, in order to find the optimal preventive and opportunistic maintenance thresholds for the two components. Three algorithms are introduced to address this problem, namely the improved genetic algorithm, the cyclic coordinate search algorithm, and the enumeration algorithm. Their effectiveness is demonstrated through a case study focusing on offshore wind turbines. The result shows that the efficiency of the improved genetic algorithm is significantly superior to the other two algorithms.

Determining a Reliability Centered Maintenance (RCM) analysis model for large diameter prestressed concrete water pipelines

Which maintenance task to perform on an asset is generally known but the schedule of when to perform the task may not be well defined. If the task is performed early, while it may benefit the asset, it may also be expending resources that could be better used elsewhere. If the task is not performed soon enough, the asset may fail, or the asset may require more extensive maintenance be performed. Reliability Centered Maintenance (RCM) is an analysis process used to determine the most effective maintenance strategies. RCM analysis can help an agency establish the most cost effective maintenance strategies for an asset and help establish the best schedule for implementing those strategies. The ultimate goal of an RCM is to determine the required function of an asset with the required reliability at the lowest operation and maintenance costs by identifying revealing indicators of potential failures to establish a condition based strategy; essentially, predicting failures and identifying procedures to minimize the risk of the failure. This paper reviews potential models for predicting the progression of distress of prestressed concrete pipe segments for use in an RCM analysis and proposes a regression model operators can use to forecast when to perform maintenance activities. The paper further considers the condition of the drinking water infrastructure in the United States, developing the case for evaluating predictive models to forecast when a pipeline may reach a specified limit state. The results of this study suggest that RCM analyses for large diameter water pipelines can improve reliability, reduce maintenance costs, and extend the useful life of a pipeline regardless of age or material. Further, using a regression style model with gathered data can be used to forecast when specific maintenance thresholds may be reached, prompting predictive maintenance actions.

Progesterone in frozen embryo transfer cycles: assays, circulating concentrations, metabolites, and molecular action

Programmed or medicated frozen embryo transfer (FET) cycles rely on exogenous progesterone (P) administration to prepare the endometrium for implantation and to maintain pregnancy. Presently, the optimal route and dose of P replacement for FET is not known. In addition, there is a paucity of data and insufficient understanding regarding the metabolism and actions of P in implantation and pregnancy maintenance. In the present review, we discuss how different P assay methodologies affect the determination of P thresholds for implantation and pregnancy maintenance. In addition, we discuss the importance of free P and its regulation in the endometrium and show the complexity of molecular signaling that is required for P-dependent endometrial receptivity. We conclude that future studies should focus on defining accurate circulating and endometrial P concentrations, both for total and free P, and how these concentrations correlate with endometrial receptivity and clinical outcomes.

A method determining critical operating parameters for landing aircraft based on runway pavement skid resistance

ABSTRACT Poor skid resistance of airport pavement is the critical cause of runway excursion accidents. While the current determination methods of skid resistance are limited to the independent operating parameters and the discontinuously monitoring the skid resistance, the empirically calculating critical operating parameters. In this study, an aircraft tire-fluid-airport pavement skid resistance model was developed using numerical simulation technology. The wide-body aircraft B777 was taken as an example, the available skid number (SN) was used to continuously track the skid resistance of pavement under various landing conditions. The combined effects of groove type, water film thickness and landing weight on the pavement skid resistance performance were evaluated when the pavement reached the maintenance threshold state. Results show that the skid resistance of the ungrooved pavement was subpar according to FAA. The skid resistance was positively connected with the grooved size and landing weight but negatively correlated with the water film thickness. Under certain operating conditions, the FAA maintenance threshold groove still meets the requirements for aircraft landing safety throughout the whole range of landing speeds. A real-time determination method of pavement skid resistance was proposed. It can guide pavement maintenance management departments to avoid runway excursion accidents.

An integrated framework of preventive maintenance and task scheduling for repairable multi-unit systems

Preventive maintenance and task scheduling are both crucial activities in industrial practice. Although they are interdependent and mutually influential, most existing studies typically analyze them separately. In this paper, an integrated framework of preventive maintenance planning and task scheduling is explored. On one hand, while most scholars focus on the system as a whole in scheduling studies, we further take into account the degradation and maintenance of multiple units during the task scheduling process. On the other hand, instead of only using "direct replacement" as the main maintenance method, we further consider the repairability of the unit by incorporating imperfect maintenance effects into the maintenance modeling. Additionally, the lifecycle partitioning method is proposed to analyze the maintenance groups for multiple units at the completion of scheduling tasks and their corresponding probabilities. Based on this foundation, an integrated decision model is established with the preventive maintenance threshold and scheduling task sequence as decision variables to minimize the total weighted expected completion time. Finally, a novel genetic algorithm based on hybrid encoding is proposed, and the effectiveness of integrated decision-making was validated using a practical example.

Multiple-failure mode division and condition-based maintenance decision making for systems with multi-indicator performance degradation

In prognostics and health management research, it is typically assumed that the health status of a system can be reflected by a single performance indicator. However, modern industrial systems are complex and often require multiple indicators to represent different aspects of system health. Furthermore, these indicators may be associated with multiple fault types, leading to multiple failure modes. This study focuses on systems with multi-indicator performance degradation. We construct a division model of multiple failure modes, and investigate the optimal condition-based maintenance decision under multiple failure modes. By constructing division models for systems with two-indicator performance degradation and systems with three-indicator performance degradation, a common multiple-failure mode division model for systems with multi-indicator performance degradation is to distinguish different fault types that may occur in such systems. Taking the systems with two-indicator performance degradation as an example, a condition-based maintenance strategy is developed that considers the differences in maintenance types and effectiveness corresponding to different faults types. A joint probability recursive model under the influence of strategies is derived, and a cost-rate model in a finite time horizon is established to determine the optimal inspection cycle and maintenance threshold for each indicator. Taking the steel rolling system as an application case, the correctness and effectiveness of the proposed strategy and model are verified. The results indicate that the division model can represent the complex relationships among multiple indicators and fault types and that the strategy considering multiple maintenance types and effects can reduce the cost rate of the system.

Repair tolerance assessment for aircraft composite structures using Bayesian updating

Detection and repair of composite damage is crucial to ensure the safety and reliability of aircraft structures. A novel approach to quantitatively evaluate the repair tolerance of composite structures in civil aircraft based on Bayesian updating is presented. The method incorporates historical damage inspection data to determine the prior distribution of damage size, which is then updated with newly collected damage size data using Bayesian theory. Monte Carlo simulation is employed to investigate the probability of failure and estimate maintenance costs, considering various factors such as the frequency and timing of damage events, damage detection, structural strength, gust loads, and maintenance expenses throughout the lifecycle of composite structures. Safety and economic factors are considered to establish a lower threshold for repairs and an upper threshold for maintenance based on the occurrence of accidental impact damage. Verification of the effectiveness and feasibility of a quantitative assessment method for repair tolerance is conducted using damage statistics data from civil aircraft routes utilizing the structural skin panels of composite outer wing. The results demonstrate that the method proposed in conjunction with extensive simulations and full utilization of field damage inspection data can effectively simulate unexpected impact damage situations that may occur during civil aircraft service and evaluate the reliability and economic feasibility of the repair of structure. The research findings hold significant theoretical and practical value for the preparation of documents for continued airworthiness of composite structures, including structural repair manuals and maintenance programs.

Optimizing the maintenance threshold in presence of shocks: A numerical framework for systems with non-monotonic degradation

Shocks have attracted considerable interest in reliability and maintenance engineering because of their impact on vulnerable systems. Most industrial systems suffer from both internal degradation caused by fatigue and wear-out, and external shocks that often occur randomly due to harsh weather conditions, overloading, etc. Developing maintenance optimization models without taking these stochastic shocks into account is often ineffective. This paper develops a model to optimize the maintenance alarm threshold for a single-component continuously monitored system which is exposed to both fatal and non-fatal shocks in the presence of lead time for hard time maintenance. The shocks occur randomly according to a homogeneous Poisson process during the whole degradation process and have a stochastic impact on the degradation level, while the system resistance to shocks decreases as the system approaches failure. We propose a new numerical maintenance optimization model to find the solution without Monte-Carlo simulation and the model is compared to the Wiener process. A numerical example and a real-time experimental case study on roller bearings are used to demonstrate the effectiveness of the model. The results show that the model is capable of improving maintenance decision-making in terms of failure probability and risk perspective.

Condition-based opportunistic maintenance strategy for multi-component wind turbines by using stochastic differential equations

The components of wind turbines are complex in structure and the working environment is harsh, which makes wind turbines face problems such as high failure rates and high maintenance costs. In this paper, the stochastic differential equation model has been established for the harsh operating environment of wind turbines, and used Brownian motion to simulate random disturbances; aiming at the problem of high failure rate of wind turbines, based on Weibull distribution, a new model has been established by combining operating time and equipment state to calculate the failure rate; in the analysis of monitoring data, the Higher-Order Moment method and Bayesian method were used to solve the parameters. The opportunity maintenance threshold curve and preventive maintenance threshold curve were obtained by analyzing Time-Based Maintenance and Condition-Based Maintenance. Therefore, the Condition-Based Opportunistic Maintenance strategy was obtained. The effectiveness of the proposed method was finally verified by arithmetic examples.

A DPIM-based probability analysis framework to obtain railway vehicle vibration characteristics considering the randomness of OOR wheel

The OOR (out-of-roundness) wheel is one of the main excitation sources causing vehicle vibration. However, the OOR wheel occurs randomly, indicating that the vibration behavior of a vehicle cannot be comprehensively evaluated using a deterministic approach. Thus, a probability analysis framework is proposed to obtain vehicle vibration characteristics while considering the randomness of the OOR wheel. The probability model of the random OOR wheel is derived by reducing the high-dimensional variables into a few independent variables of the radius, amplitude, and phase. Then, the vertical vehicle-track coupled system with OOR wheels is modelled. A DPIM (direct probability integral method) is further developed to analyze the evolution of excitation to response probabilities. Finally, the statistics of the random vibration of the vehicle are calculated. The proposed framework is verified using a numerical case. Results show that the PDF (probability density function) shape of the vehicle random vibration, induced by the Gaussian-distributed OOR wheel, deviates from the Gaussian distribution due to the nonlinear wheel/rail contact force. Instead, it exhibits a right-skewed shape, significantly impacting the dynamic performance. As the mean or coefficient of variation of the OOR wheel amplitude increases linearly, the reliability of the vehicle Sperling index experiences a quadratic or double-sloping decrease. Consequently, a maintenance threshold for OOR wheel amplitudes is given based on reliability considerations. Compared to Monte Carlo simulation, the proposed framework offers a computational efficiency improvement of at least one order of magnitude.

Review of Multi-dimensional Rut Index Threshold Method Based on Driving Comfort and Safety

Rutting, one of the typical distresses in asphalt pavement, is known to be a potential hazard to driving comfort and safety when considering driving performance. The cost of different treatment plans varies greatly, which directly affects the allocation and use of maintenance funds and the formulation of maintenance investment plans. The safety and comfort evaluation and maintenance threshold of rutting have an important role in determining the timing of rutting maintenance and the allocation of maintenance funds. At present, the research on rutting based on driving safety/comfort only considers the one-dimensional rut depth. It is difficult to carry out in-depth discussion on the influence of traverse and longitudinal features from the three-dimensional perspective, thus cannot establish a vehicle driving safety/comfort theory system based on real multi-dimensional rut morphological features. With the introduction of high-efficiency, high-density and high-precision advanced 3D laser technology, it is possible to break through the bottleneck of effective extraction and construction/reconstruction of high-resolution three-dimensional rutting features. This paper gives a detailed overview of the rut maintenance threshold standard and multi-dimensional rut indexes, rut comfort and safety evaluation, and maintenance threshold methods; and finally discuss the current problems and proposes future research recommendations. Future research will not only construct the multi-dimensional morphological of the rut with high resolution and with the cross and vertical slopes of the road considered, but also establish a multi-dimensional rut evaluation method and maintenance standard considering driving safety/comfort with comprehensive high precision, which can be used for the refining and revising of rut maintenance specification.

Remaining useful life prediction of equipment considering dynamic thresholds under the influence of maintenance

A novel approach for predicting remaining useful life (RUL) is proposed for situations where maintenance threshold and failure threshold exhibit dynamic behavior due to uncertainties in degradation and the influence of detection strategies during maintenance processes. The approach introduces maintenance threshold error to establish a multi-stage maintenance-impact degradation model with dynamic maintenance threshold based on the Wiener process. This model considers the impact of maintenance on degradation rate, amount, and path. Moreover, by using the first hitting time (FHT) and introducing failure threshold error to reflect the dynamic behavior of the failure threshold, the formula for predicting equipment RUL is derived. The model parameters are estimated using both the maximum likelihood estimation (MLE) approach and Bayesian formula. The proposed approach was validated with simulation data and gyroscope degradation data, and the results demonstrate its ability to effectively enhance the precision of equipment RUL prediction.

An analytical solution for friction coefficients of grooved pavements considering tire rubber-groove interaction

To reduce the loss of skid resistance of pavement in wet weather, pavement grooving is used to improve aircraft operation safety on runways. This study presents an analytical solution to analyze mechanical interactions between tire tread rubber and pavement surface grooves for calculating friction coefficients of aircraft tires on runway pavements. The energy dissipation theory is utilized to calculate the contribution of rubber-groove interlock and the friction on plane part of pavement surface due to surface texture is accounted using Brush model. The comparison of model results and field measurements proves the capability of the proposed method to predict friction coefficients of aircraft tire on runway pavements with various groove configurations. The evaluation of trapezoidal grooved pavement shows similar friction coefficients compared to the standard square grooved pavement regulated by the Federal Aviation Administration (FAA). The friction coefficients of grooved pavement vary depending on the inclined angles of groove and edge-to-edge spacing that are two important geometry parameters. The findings demonstrate the pronounced influences of rubber deposits and groove deterioration on skid resistance of runway pavements. The analysis results can be further used to design optimized groove configuration and determine maintenance threshold for safe operation of aircraft on runways.