Condition-based Maintenance Model Research Articles

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.

Condition-based maintenance policy for systems under dynamic environment

This paper addresses the challenge of optimizing maintenance for systems with varying degradation characteristics in dynamic environment. The focus is on repairable systems, where degradation is characterized by a Wiener process that incorporates the dynamic influence of the environment. We investigate a condition-based maintenance model aiming at minimizing the expected cost over a finite and infinite horizon. Firstly, the model considers the evolution of environment as a Markov process, which influences the drift parameter in the Wiener process. The choice between a corrective and preventive replacement rests on the periodic inspections on the system and environment. Subsequently, the maintenance model is formulated in the framework of Markov decision process. Meanwhile, the backward dynamic programming algorithm and value iteration algorithm are employed to gain the optimal maintenance policies over the finite and the infinite planning horizons, respectively. Finally, the proposed model is further explained with concrete examples and a comprehensive sensitivity analysis. Numerical results clearly demonstrate notable variations in the optimal maintenance strategies in different periods and underscore the importance of taking proactive maintenance measures in harsh environmental conditions, even if the degradation level of the system is relatively low.

Condition-based maintenance considering imperfect inspection for a multi-state system subject to competing and hidden failures

Unexpected failures may cause huge economic losses and bring about uncontrolled risks and damage to humans and the environment. Condition-based maintenance is considered one of the most effective maintenance methods, which can reduce and eliminate possible failures and consequently ensure the safety and operating process effectiveness of systems. Inspection quality is an important factor affecting the effect of condition-based maintenance since imperfect inspection may result in inappropriate maintenance decisions. However, the factor of imperfect inspection is rarely considered when maintaining multi-state systems subjected to competing and hidden failures. In this paper, a periodical inspection policy is utilized to identify all states of a system subject to a three-state degradation process and random shocks. Inspections for all system states are considered imperfect. Upon the inspection outcome, the system may be replaced by a failure-based corrective replacement once it is detected as failed, or is renewed by a defect-based preventive replacement if a defect is identified. Moreover, an age-based preventive replacement is also required if the system is still normal up to the preset number of inspections. Subsequently, a condition-based maintenance model is constructed based on the probabilities of different renewal scenarios. Eventually, the applicability and effectiveness of the proposed model are verified by a numerical example. The results indicate that minimizing the constructed maintenance model can achieve the optimal inspection interval, which is greatly influenced by the factor of imperfect inspection. This research can provide references for decision-makers to optimize inspection and maintenance strategies.

Research on imperfect condition-based maintenance strategy based on accelerated degradation process

To address the situation that the degraded system cannot be repaired as new and the degradation is accelerated after repair in engineering practice, an imperfect condition-based maintenance model is proposed based on the Gamma process by introducing the maintenance improvement factor and the accelerated degradation factor. The maintenance improvement factor describes the recovery degree of the degradation amount after imperfect maintenance, then gives a quantitative representation method of the system maintenance improvement effect and the determination method of the maximum number of imperfect maintenance. The accelerated degradation factor is combined with the geometric process to describe the accelerated degradation process of the system after imperfect maintenance. The Monte Carlo method is used to determine the optimal number of imperfect preventive maintenance decisions. Through the demonstration of calculation examples, the influence of accelerated degradation factor and maintenance improvement factor on the long-term operation maintenance rate and the optimal number of imperfect maintenance is analyzed. The results show that the model can more comprehensively describe the degradation and maintenance process of degraded systems, and it can effectively reduce maintenance cost ratio under long-term operation. It provides a certain theoretical support for formulating an economical and reasonable maintenance strategy that is more in line with the actual degradation and maintenance process in engineering practice.

Piecewise deterministic Markov process for condition-based imperfect maintenance models

Piecewise deterministic Markov process for condition-based imperfect maintenance models

Condition-based Maintenance Optimization for Gamma Deteriorating Systems under Performance-based Contracting

With the further development of service-oriented, performance-based contracting (PBC) has been widely adopted in industry and manufacturing. However, maintenance optimization problems under PBC have not received enough attention. To further extend the scope of PBC’s application in the field of maintenance optimization, we investigate the condition-based maintenance (CBM) optimization for gamma deteriorating systems under PBC. Considering the repairable single-component system subject to the gamma degradation process, this paper proposes a CBM optimization model to maximize the profit and improve system performance at a relatively low cost under PBC. In the proposed CBM model, the first inspection interval has been considered in order to reduce the inspection frequency and the cost rate. Then, a particle swarm algorithm (PSO) and related solution procedure are presented to solve the multiple decision variables in our proposed model. In the end, a numerical example is provided so as to demonstrate the superiority of the presented model. By comparing the proposed policy with the conventional ones, the superiority of our proposed policy is proved, which can bring more profits to providers and improve performance. Sensitivity analysis is conducted in order to research the effect of corrective maintenance cost and time required for corrective maintenance on optimization policy. A comparative study is given to illustrate the necessity of distinguishing the first inspection interval or not.

Autoencoder-Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm

This paper introduces an algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD). The analysis of such data has become increasingly important due to growing availability in many industrial fields. Labeling, sorting or filtering highly transient measurement data for training Condition-based Maintenance (CbM) models is cumbersome and error-prone. For some applications it can be sufficient to filter measurements by simple thresholds or finding change-points based on changes in mean value and variation. But a robust diagnosis of a component within a component group for example, which has a complex non-linear correlation between multiple sensor values, a simple approach would not be feasible. No meaningful and coherent measurement data, which could be used for training a CbM model, would emerge. Therefore, we introduce an algorithm that uses a recurrent neural network (RNN) based Autoencoder (AE) which is iteratively trained on incoming data. The scoring function uses the reconstruction error and latent space information. A model of the identified subsequence is saved and used for recognition of repeating subsequences as well as fast offline clustering. For evaluation, we propose a new similarity measure based on the curvature for a more intuitive time-series subsequence clustering metric. A comparison with seven other state-of-the-art algorithms and eight datasets shows the capability and the increased performance of our algorithm to cluster MTSD online and offline in conjunction with mechatronic systems.

Simultaneous optimization of inventory, maintenance, and quality for production systems subject to multiple mean and variance shifts

This article presents a general, integrated production, quality, and condition-based maintenance model for imperfect processes with multiple out-of-control operating states. The production system is subject to multiple quality disruptions, which affect both the process mean and variance, and failures. The quality of the production process output is monitored by a fully adaptive control scheme, and the state of deterioration is estimated through periodic sampling inspection. The proposed control scheme’s operation is modeled through a Markov chain, and the optimal quality control, maintenance, and inventory policy are defined based on an expected total cost minimization criterion. Finally, an illustrative example is employed to demonstrate the operation of the proposed model, sensitivity analysis is conducted to generate insights into how the process parameters misspecification affects the economic performance, and a comparative study is performed to evaluate the model’s superiority against the respective single quality shift model.

Joint Optimization Strategy of Condition-Based Maintenance and Spare Parts Ordering for Nonlinear Degraded Equipment under Imperfect Maintenance

When existing methods consider the impact of the imperfect maintenance activities on random degraded equipment, they usually assume that the degraded equipment is linear, and set the number of imperfect maintenance activities in advance. However, in engineering practice, most of the degraded equipment is nonlinear, and the number of imperfect maintenance activities has an impact on the development of maintenance strategies. Therefore, this paper proposes a new joint maintenance strategy considering the uncertainty of the number of imperfect maintenance activities for nonlinear degraded equipment. First, nonlinear degradation data are linearized based on Box-Cox transformation (BCT), and the degradation model under the influence of imperfect activities is constructed by the random coefficient regression model. Accordingly, the remaining useful life (RUL) probability distribution can be derived. Secondly, the number of maintenances is calculated by imperfect maintenance level and success probability. Then, the detection cycle, preventive maintenance threshold and maintenance times are taken as decision variables. The optimization objective is to minimize the expected average cost. However, this is restricted by availability and probability of success for imperfect maintenance activities. A multi-objective joint optimization model of condition-based maintenance and spare parts ordering is constructed. Finally, the analysis results based on numerical examples verify the feasibility of the proposed joint optimization strategy.

Industry 4.0 for Aerospace Manufacturing: Condition Based Maintenance Methodology, Implementation and Challenges


 Industry 4.0 is the fourth industrial revolution where machine operations are linked through data communications and together, they form a production ecosystem. In Industry 4.0 settings, these operations are monitored, recorded and analyzed. This can be performed at Edge, Fog or Cloud levels.
 
 
 In the industrial big data era, with ever maturing sensor technologies, data capture, communication and storage technologies, utilizing machine data for operational insights provides companies with competitive advantages. Benefits can include reduced operational and maintenance costs, a decrease in unscheduled downtime and greater assurance of on-time delivery of products. In this work, we will cover the milestones of implementing an industry 4.0 condition-based maintenance (CBM) strategy for machine tools and their surrounding systems. In addition, we will discuss the methodology for sensor selection, data collection, transmission and storage, return on investment for CBM and building CBM models for detection. Finally, will delve into challenges of implementing this methodology in industrial settings from both technology and logistics aspects.

Semi-Markov models of inspection-based maintenance with empirical data from case studies on hydrant pumps

Considerable benefits have been gained from condition-based maintenance (CBM) utilizing continuous monitoring integrated with information technology. However, periodic inspection for CBM is still used widely as a practically helpful method to know the condition of the equipment. This paper starts from a case study where a maintenance log recorded by periodic inspection from five hydrant pumps is used to estimate the required parameter for maintenance modeling. To process the data for CBM, two schemes are taken into consideration: Inference of condition indicator through repair activities and reflection of non-observable events with virtual nodes. A CBM model of inspection-based preventive maintenance with discrete data is developed using the Markov model. The semi-Markov process is adopted then with more flexibility allowing the Weibull distributed sojourn times and the Multiphase Markov process is suggested to reflect the periodic inspection. Thus, the model for pumps takes into account both SMP and multiphase Markov process. Monte-Carlo simulations are generated to calculate state probability and the number of maintenances. An analytical solution is proposed by the transition probability of embedded Markov chain (EMC) and sojourn time of SMP. The developed CBM models are verified and compared based on analysis results and empirical data.

Preventive condition-based maintenance model of EMU components based on stochastic differential equation

Preventive condition-based maintenance model of EMU components based on stochastic differential equation

A condition-based maintenance model considering multiple maintenance effects on the dependent failure processes

Maintenance is generally assumed to play a positive role, however, in reality, it is also common that maintenance actions have negative impacts. In this paper, a condition-based maintenance model for repairable systems subject to dependent failure processes (soft failure due to system degradation and hard failure due to random shocks) is proposed. It is a unified model with strong compatibility. The effects of maintenance actions with negative or positive effects incurred by inspection, preventive maintenance and corrective maintenance are all considered. In order to show the effects of maintenance actions intuitively, a vector space spanned by the degradation level, deteriorate rate and hard failure threshold is constructed. It can easily show the complex and multiple effects between maintenance impacts. Finally, the model is used to deal with high-voltage transmission lines’ maintenance optimization problem. The results show that the model is feasible and can be applied in the field of maintenance optimization.

Assessing Wear Evolutions in Railway Wheelsets Using a Survival Modeling Approach

Abstract This paper provides a method to predict maintenance needs for the railway wheelsets by modeling the wear out affecting the wheelsets during its life cycle using survival analysis. Wear variations of wheel profiles are discretized and modeled through a censored survival approach, which is appropriate for modeling wheel profile degradation using real operation data from the condition monitoring systems that currently exist in railway companies. Several parametric distributions for the wear variations are modeled, and the behavior of the selected ones is analyzed and compared with wear trajectories computed by a Monte Carlo simulation procedure. This procedure aims to test the independence of events by adding small fractions of wear to reach larger wear values. The results show that the independence of wear events is not true for all the established events, but it is confirmed for small wear values. Overall, the proposed framework is developed in such a way that the outputs can be used to support predictions in condition-based maintenance models and to optimize the maintenance of wheelsets.

Condition-Based Maintenance Modeling and Reliability Assessment for Multi-Component Systems with Structural Dependence under Extended Warranty

For engineering and production systems, due to the structural dependence between components, the disassembly operation caused by the replacement of components will affect the failure and degradation processes of other components in the system. In order to optimize the extended warranty (EW) cost of the multi‐component system with structural dependence, this paper described the structural dependence and modeled the disassembly operation impact, and then the failure rate model of the component considering the impact of disassembly operation under EW was developed. Combined with the actual situation, a condition-based maintenance (CBM) strategy was employed to construct the EW cost model of the multi‐component system with structural dependence. Monte Carlo simulation was proposed to determine the optimal EW cost of the system and the optimal periodic inspection interval of the CBM strategy. Finally, a numerical example of the planetary gear train of an automobile generator is introduced to demonstrate the feasibility and advantages of the proposed model in EW cost optimization and the analysis of disassembly operation impact on the optimal maintenance strategy.

An imperfect age-based and condition-based opportunistic maintenance model for a two-unit series system

Maintenance strategies have been studied extensively and applied successfully to reduce the operation costs of industrial assets. However, the study for heterogeneous two-unit systems is rarely based on the usage and condition information of units, simultaneously. This paper originally proposes a novel imperfect opportunistic maintenance model for a two-unit series system considering random repair time and two types of failures, where unit 1 and unit 2 are respectively subject to soft failure and hard failure. The system maintenance actions are performed not only based on the deterioration level of unit 1 but also on the usage of unit 2. To utilize the economic dependence between the two units, the opportunistic maintenance policy is adopted in the series system. When a unit fails or is being repaired, the other unit has an opportunity to be simultaneously maintained once it meets the requirement of opportunistic maintenance. Herein, opportunistic maintenance action is imperfect maintenance, which means that the deterioration level of unit 1 and the usage of unit 2 restore to a lower level than the as-good-as new status after repair. Nonetheless, once both units meet the requirement of replacement actions, they will be perfectly replaced by new ones. The objective is to minimize the system average maintenance cost by optimizing the thresholds of opportunistic maintenance and perfect replacement of two units. Finally, an illustrative example is presented to demonstrate the superiority of the proposed maintenance model.

Multiobjective Multistate System Preventive Maintenance Model with Human Reliability

Modern equipment is designed to operate under deteriorating performance conditions resulting from internal ageing and/or external environmental impacts influencing downstream maintenance. This study focuses on the development of a multistate system (MSS) that considers a human reliability factor associated with maintenance personnel—a condition-based multiobjective MSS preventive maintenance model (MSSPMM). The study assumes that no more than one maintenance activity is performed to achieve the most appropriate preventive maintenance (PM) strategy and easy implementation and to reduce maintenance error due to human reliability. The MSS performance based on mean system unavailability and total maintenance cost is evaluated using a stochastic model approach, and then, the MSSPMM is used for optimisation. A customised version of the nondominated sorting genetic algorithm III is employed to ensure efficient solution of the PM model with human reliability—which is considered a constrained multiobjective combinatorial optimisation problem. The optimised solutions are determined from the nondominated Pareto frontier comprising the diversified PM alternatives. A helicopter power transmission system is used as an example to illustrate the efficacy and applicability of the proposed approach through sensitivity analyses with relevant parameters.

A numerical study of Markov decision process algorithms for multi-component replacement problems

We present a unified modeling framework for Time-Based Maintenance (TBM) and Condition-Based Maintenance (CBM) for optimization of replacements in multi-component systems. The considered system has a K-out-of-N reliability structure, and components deteriorate according to a multivariate gamma process with Lévy copula dependence. The TBM and CBM models are formulated as Markov Decision Processes (MDPs), and optimal policies are found using dynamic programming. Solving the CBM model requires that the continuous deterioration process is discretized. We therefore investigate the discretization level required for obtaining a near-optimal policy. Our results indicate that a coarser discretization level than previously suggested in the literature is adequate, indicating that dynamic programming is a feasible approach for optimization in multi-component systems. We further demonstrate this through empirical results for the size limit of the MDP models when solved with an optimized implementation of modified policy iteration. The TBM model can generally be solved with more components than the CBM model, since the former has a sparser state transition structure. In the special case of independent component deterioration, transition probabilities can be calculated efficiently at runtime. This reduces the memory requirements substantially. For this case, we also achieved a tenfold speedup when using ten processors in a parallel implementation of algorithm. Altogether, our results show that the computational requirements for systems with independent component deterioration increase at a slower rate than for systems with stochastic dependence.

Condition-Based Maintenance Strategies to Enhance the Durability of ETICS

The increase of awareness with sustainability and the desire of reducing the energy consumption in the construction sector haved increased the application of External Thermal Insulation Composite Systems (ETICS) across Europe in the last decades. Nevertheless, the implementation of appropriate maintenance strategies is still neglected. The aim of this study is to analyse the impact of different maintenance strategies. For that purpose, a condition-based maintenance model, based on Petri nets, is used to evaluate three maintenance strategies: MS1—total replacement only; MS2—combination of minor intervention and total replacement; and MS3—combination of cleaning operations, minor intervention, and total replacement. In the end, a multi-criteria analysis is used to discuss the impact of the three maintenance strategies proposed, evaluating the remaining service life, the global costs over time, the ETICS’ degradation condition, and the number of replacements (end of service life) over the time horizon. For this purpose, a sample of 378 ETICS was analysed, based on in situ visual inspections, carried out in Portugal. The results from this study reveal that maintenance plays an important role to increase the durability of ETICS, and therefore their sustainability. Regular maintenance can promote the extension of the ETICS’s service life between 88% and 159% (between 15 to 27 years), improve the global degradation condition of the ETICS, and reduce the impact on users by reducing the number of deeper interventions. Further research is essential to optimise the maintenance strategies (time interval between inspections, stakeholders’ performance criteria, and environmental exposure).

A Metric and Visualization of Completeness in Multi-Dimensional Data Sets of Sensor and Actuator Data Applied to a Condition Monitoring Use Case

The so-called ‘Industrie 4.0’ provides high potential for data-driven methods in automated production systems. However, sensor and actuator data gathered during normal operation of the system is often limited to a narrow range of single, specific operating points. This limitation also restricts the significance of condition-based maintenance models, which are trained to the narrow data. In order to reveal the structure of such multi-dimensional data sets and detect deficiencies, this paper derives a data quality metric and visualization. The metric observes the feature space and evaluates the completeness of data. In the best case, the observations utilize the whole feature space, meaning all different combinations of the variables are present in the data. Low metric values indicate missing combinations, reducing the representativeness of the data. In this way, appropriate countermeasures can be taken if relevant data is missing. For evaluation, a data set of an industrial test bed for condition monitoring of control valves is used. It is shown that the state-of-the-art metrics and visualizations cannot detect deficiencies of completeness in multi-dimensional data sets. In contrast, the proposed heat map enables the expert to locate limitations in multi-dimensional data sets.