Sequential Preventive Maintenance Research Articles

ПРОГНОЗНЫЙ МОНИТОРИНГ ТЕХНИЧЕСКОГО СОСТОЯНИЯ ПУТЕМ АНАЛИЗА СОСТАВЛЯЮЩИХ СИГНАЛА ВИБРАЦИИ ПОДШИПНИКОВ ВРАЩАЮЩИХСЯ ЭЛЕКТРИЧЕСКИХ МАШИН

Relevance In industry, any failures and unplanned downtime of machines or systems can degrade or interrupt a company's core business, potentially leading to significant penalties and irreparable loss of reputation. Existing traditional maintenance approaches (failure or routine maintenance) suffer from some assumptions and limitations, such as high prevention or repair costs and inadequate or inaccurate mathematical degradation processes. Due to the trend of intelligent manufacturing, data mining, and artificial intelligence, predictive maintenance control is proposed as a new type of maintenance paradigm only after analytical models predict certain failures or degradations. Equipment condition monitoring can reduce the likelihood of failures occurring. Successful technical diagnostics and monitoring of complex electromechanical subsystems and assemblies provides an increase in service life and reliability of the systems as a whole. Aim of research A general overview of maintenance aims and objectives, which mainly include detection and prevention of failures and malfunctions, maintaining performance within established limits, condition prediction in order to improve machine reliability and full use of its resource, cost minimization, maximization of availability/reliability and multi-criteria optimization. Object of research The object of the study is the technical condition of rolling bearings of rotating electric machines. The main defects that are characteristic of this object are analyzed. In addition, a practical and effective sequential preventive maintenance method for rotating electrical machines is proposed, consisting of four steps: feature generation and fault diagnosis selection, data dimensionality reduction and feature fusion, fault detection and diagnosis, and estimation of the remaining useful life of rotating electrical machine bearings. Vibration signal analysis (from the time domain waveform and spectrum of the original or derivative signal, characteristics can be calculated and their effectiveness in diagnosing a fault can be assessed). Research methods To verify the performance of the proposed method, bearing failure is simulated in the Simulink MATLAB module. The bearing failure is created by feeding the vibration perturbation into the shaft system. Results Based on the results of the simulation, a conclusion on the operability of the proposed method for investigating the technical condition of rotating electric machines is drawn.

Predictive Maintenance and Sensitivity Analysis for Equipment with Multiple Quality States

This paper discusses the predictive maintenance (PM) problem of a single equipment system. It is assumed that the equipment has deteriorating quality states as it operates, resulting in multiple yield levels represented as system observation states. We cast the equipment deterioration as discrete-state and continuous-time semi-Markov decision process (SMDP) model and solve the SMDP problem in reinforcement learning (RL) framework using the strategy-based method. In doing so, the goal is to maximize the system average reward rate (SARR) and generate the optimal maintenance strategy for given observation states. Further, the PM time is capable of being produced by a simulation method. In order to prove the advantage of our proposed method, we introduce the standard sequential preventive maintenance algorithm with unequal time interval. Our proposed method is compared with the sequential preventive maintenance algorithm in a test objective of SARR, and the results tell us that our proposed method can outperform the sequential preventive maintenance algorithm. In the end, the sensitivity analysis of some parameters on the PM time is given.

Application of Stochastic Optimization to Optimal Preventive Maintenance Problem

The contribution deals with the optimization of a sequential preventive maintenance schedule of a technical device. We are given an initial time-to-failure probability distribution, model of changes of this distribution after maintenance actions, as well as the costs of maintenance, of a device acquisition, and of the impact of failure. The maintenance timing and, eventually, its extent, are the matter of optimization. The objective of the contribution is two-fold: first, to formulate a proper (random) objective function evaluating the lifetime of the maintained device relatively to maintenance costs; second, to propose a numerical method searching for a maintenance policy optimizing selected characteristics of this objective function. The method is based on the MCMC random search combined with simulated annealing. It is also shown that such a method is rather universal for different problem specifications. The approach will be illustrated on an artificial example dealing with accelerated lifetime after each maintenance action.

Optimum sequential preventive maintenance first or last policies with imperfect maintenance for a system subject to shocks

This paper proposes and analyses the generalized sequential preventive maintenance policies for an operating system that works at random processing times and subject to shocks. The shocks arrive according to a non homogeneous Poisson process (NHPP) with varied intensity function in each maintenance interval. As a shock occurs, the system suffers two types of failures with number-dependent probabilities: type-I (minor) failure, which is rectified by a minimal repair, and type-II (catastrophic) failure, which is removed by a corrective maintenance. The imperfect maintenance is carried out to improve the system failure characteristic due to the altered shock process. The preventive maintenance-first and preventive maintenance-last policies are defined as that the system is maintained before any type-II failure occurs at a planned time Ti or at the completion of a working time in the i-th maintenance interval, whichever occurs first and last, respectively. At the N-th maintenance, the system is replaced rather than maintained. This paper aim is to minimize the mean cost rate as a measure of policy by determining optimal sequential maintenance parameters for each preventive maintenance policy. All discussions are presented analytically and determined numerically in terms of its existence and uniqueness.

Optimal sequential preventive maintenance policy for a repairable system with maintenance windows

Optimization of sequential preventive maintenance has been widely studied. However, some issues need further discussion when considering certain real factors. Hence, an sequential preventive maintenance policy for a mono-unit repairable system with maintenance windows is proposed. The load strength and catastrophic failures of a system are considered in the reliability model, and an optimization of the SPM policy is rigorously proven by an analytical method. The existence and uniqueness of the optimal solution with a monotone increasing failure intensity function are presented in detail. The proof of theory indicates that the length of the maintenance window has an upper bound. Finally, a numerical example and some sensitivity analyses are given to illustrate the proposed sequential preventive maintenance policy. Results show that waste may occur when performing the general sequential preventive maintenance policy and may increase along with the length of the maintenance windows.

Optimal System Design and Sequential Preventive Maintenance Under Uncertain Aperiodic-Changing Stresses

This paper presents a two-stage stochastic programming model with recourse for integrating the design and sequential preventive maintenance schedule of a system, which is subject to uncertain aperiodic-changing future usage stresses. Specifically, the usage stresses change as the system operates and preventive maintenance is conducted. The system undergoes imperfect repair according to the sequential preventive maintenance policy and minimal repair in response to emergency failures. The system is replaced when the maintenance is uneconomical due to the deterioration of the components. Under such future usage stresses and maintenance, this paper formulates the failure rates and lifetime distributions of components and the system, the failure rates increase with the usage stresses, and both the failure rates and usage stresses have an instantaneous incremental decrease at each preventive maintenance action. In the two-stage stochastic optimization model, the first-stage decision variables are the numbers of components to be used in the subsystems, and these variables affect the second-stage variables, which define the number of imperfect preventive maintenance actions before the replacement of the system and the aperiodic preventive maintenance time intervals for various future usage scenarios. Analytical properties about the failure rates of components and subsystems and the solution for minimizing expected system maintenance cost rate are derived. A decomposition method for solving the proposed two-stage stochastic model is designed based on the analytical results. Numerical examples and sensitivity analysis are provided for deep understanding of the proposed method.

Cumulative Hazard Intensity Based on Sequential Preventive Maintenance Strategy for Ensuring Minimum Reliability

Cumulative Hazard Intensity Based on Sequential Preventive Maintenance Strategy for Ensuring Minimum Reliability

Preventive Maintenance Models Based on the Generalized Geometric Process

For a generalized geometric process (GGP), the geometric ratio changes with the number of repairs rather than a constant. The GGP is a more flexible maintenance model that can reflect the varying of the repair effect to the system. On the basis of the GGP, two imperfect preventive maintenance (PM) models are studied. First, we assume that the system is preventively maintained periodically after every $T$ time unit, and is correctively repaired at failure. The corrective repair is a minimal repair that just restores the system to work, while the PM results in a GGP, i.e., the lifetime sequence of a system after PM constitute a decreasing GGP. The long-run average cost rate $C(N,T)$ of the system is derived, and the optimal bivariate policy $(N^*,T^*)$ is determined by minimizing $C(N,T)$ , where $N$ is the number of PMs before replacement. Next, a sequential PM model is investigated for a system in which a sequential time interval to be determined for PM. An algorithm is given to seek the optimal replacement policy $N^*$ and the optimal time intervals $T_1^*,T_2^*,\ldots, T_N^*$ between PMs. By assuming that the lifetime of the system is Weibull distributed, the optimal policy is obtained explicitly. In both models, numerical examples are provided to verify the effectiveness of the approaches developed.

Multi-phase sequential preventive maintenance scheduling for deteriorating repairable systems

In this paper, we present a novel multi-phase sequential preventive maintenance (PM) model with multiple maintenance objectives for repairable mechanical systems under deterioration. Although sequential PM scheduling has appeared in literature, conventionally the length of time between maintenance events in the later of replacement process changes very frequently, which has led to poor operability in practice, and has consequently brought a great challenge for maintenance managers. To solve this problem, the concept of multiple phases sequential PM, in which the replacement cycle is divided into several phases, each with equal PM intervals, is proposed to make the policy more suitable in practical applications. A new relationship between cost and maintenance quality, which considers the age reduction factor as a function of maintenance cost and system age, is established to describe the effect of maintenance. The maintenance modeling and optimizing problems are formulated using a multi-attribute value model (MAVM) and a computation algorithm is presented to find the optimum solution. A case study of examining equipment is presented to illustrate the performance of the proposed policy. A comparison with other methods is given to illustrate the effectiveness of our approach. Finally, a sensitivity analysis is performed on the optimality of the maintenance schedule based on related parameters.

Generalized geometric process and its application in maintenance problems

Since the repair effect may be varying with the number of repairs, we propose a generalized geometric process (GGP) to model the deteriorating process of repairable systems. For a GGP, the geometric ratio changes with the number of repairs rather than being a constant. Based on the GGP, two repair-replacement models are studied. Existing preventive maintenance (PM) models based on geometric process (GP) commonly assume that the PM is ‘as good as new’ in each working circle, which is not realistic in many situations. In this study, that the system is assumed to be geometrically deteriorating after PM or corrective maintenance (CM). Firstly, an age-dependent PM model is considered, in which the optimal policies N* and T* are obtained theoretically, and the optimal bivariate policy (N*, T*) which minimizes the average cost rate (ACR) can be determined by a searching algorithm. Next, because of the fact that the system deteriorates after maintenance, the schedule time to PM should decrease with the maintenance number increasing. Therefore, a sequential PM policy is investigated, and the optimal policy N* and the optimal schedule times T1*,T2*,…,TN** are computed. Finally, numerical examples are provided to illustrate the proposed models.

Sequential preventive maintenance policies with consideration of random adjustment-reduction features

Sequential preventive maintenance policies with consideration of random adjustment-reduction features

Multi-phase preventive maintenance policy for leased equipment

This paper proposes a multi-phase preventive maintenance (PM) policy for leased equipment by combining the advantages of both periodic PM and sequential PM. The lease period of the equipment is divided into multiple PM phases. The PM activities within each phase are performed periodically with the convenience of implementation, while the frequency of PM for each phase is different and it gives a gradual increase because of the imperfect effect of PM. A multi-phase PM model is built up based on the age reduction method for imperfect PM with the penalty for equipment failures and overtime of repair involved. The optimal PM intervals for every PM phases are achieved by minimising the cumulative maintenance cost throughout the lease period from the perspective of the lessor. Numerical example shows that the cumulative maintenance cost under the proposed multi-phase PM policy is lower than that under periodic PM policy.

Reliability for multiple units adopting sequential imperfect maintenance policies

Preventive maintenance literature provides so many models that address single unit systems. However, many real life applications deal with multiple-unit systems. This work deals with a sequential imperfect preventive maintenance model and extends it to consider multiple units. The model targets specific system reliability by determining the maintenance timing for each unit in the system. The model helps to optimize the maintenance management process for multiple-unit systems.

A Failure-rate-reduction Periodic Preventive Maintenance Model with Delayed Initial Time in a Finite Time Period

HeA new machine will not fail easily in the early stage of its useful life. This phenomenon is consistent with the fact that the optimal sequential preventive maintenance (PM) policy which has longer PM intervals in its earlier stage of lifetime. In this paper, we propose a failure-rate-reduction periodic PM model with delayed initial time in a finite time span. Then, the optimal periodic PM policy is developed by minimizing the expected total maintenance cost, which can have smaller expected total maintenance cost than the optimal policy of the original failure-rate-reduction periodic PM model. The algorithm of finding the optimal PM policy for the proposed PM model is developed. Finally, examples are illustrated to verify the optimal policies of the proposed new PM model.

An Extended Sequential Imperfect Preventive Maintenance Model with Improvement Factors

This article presents a generalization of the imperfect sequential preventive maintenance (PM) policy with minimal repair. As failures occur, the system experiences one of two types of failures: a Type-I failure (minor), rectified by a minimal repair; or a Type-II failure (catastrophic) that calls for an unplanned maintenance. In each maintenance period, the system is maintained following the occurrence of a Type-II failure or at age, whichever takes place first. At the Nth maintenance, the system is replaced rather than maintained. The imperfect PM model adopted in this study incorporates with improvement factors in the hazard-rate function. Taking age-dependent minimal repair costs into consideration, the objective consists of finding the optimal PM and replacement schedule that minimize the expected cost per unit time over an infinite time-horizon.

Modeling and optimizing maintenance schedule for energy systems subject to degradation

In recent years, with the increasing investment on distributed energy system (DES), maintenance management has played an important role in improving the system performance. This paper aims to integrate the conception of the multiple attribute value theory and imperfect maintenance into an improved sequential preventive maintenance (PM) model for the DES subject to degradation. A model-iteration algorithm using a multi-attribute model (MAM) is developed to optimize the maintenance schedule cycle by cycle. The optimal PM interval for each cycle is obtained through MAM, by maximizing the equipment availability and minimizing the maintenance cost simultaneously. A case study is investigated to show that this sequential PM model is much more significant than conventional PM models. Finally, the sensitivity of the proposed method is investigated.

Survey of maintenance policies for the Last 50 Years

In the past several decades, maintenance and replacement problems have been extensively studied in the literature. Thousands of maintenance and replacement models have been created. However, all these models can fall into some categories of maintenance policies: age replacement policy, block replacement policy, periodic preventive maintenance policy, failure limit policy, sequential preventive maintenance policy, repair cost limit policy, repair time limit policy, repair number counting policy, reference time policy, mixed age policy, group maintenance policy, opportunistic maintenance policy, etc. Each kind of policy has different characteristics, advantages and disadvantages with lot of contributions from Research scientist, Technologists... This survey summarizes, classifies, and compares various existing maintenance policies Around 170 Authors and their research works are presented in the Reference section. It will help to look into the different policies which is appropriate to the organization and for further study the reference section will be helpful for the researchers for further knowledge

Sensitivity Analysis of Cost Parameters of Sequential Imperfect Preventive Maintenance Model and Applications

Maintenance intervals and the optimal number of maintenance N*of NC machine tools in infinite time horizon are presented using a hybrid sequential imperfect preventive maintenance (PM) model. The failures of NC machine tools are described using Weibull process. Sensitivity analysis of cost parameters is given to show how the optimal results depend on the different cost parameters. The results show that when the shape parameter β of Weibull process within the range of [1.1,1.3], N* is slightly affected by β and cost ratio cr /cp. However, when the value of β is greater than 1.4, the effect of β and cr /cp to N* is significance. If an equipment is in wear later stage or PM actions are not too effective to it, it is not suitable to add the number of maintenance.

An optimal sequential preventive maintenance policy under stochastic maintenance quality

This paper presents a sequential imperfect preventive maintenance policy for a degradation system. Two kinds of activity, called continuous preventive maintenance (PM) and minimal repair, are simultaneously considered when arranging discrete imperfect preventive maintenance schedules. In order to obtain the maximum benefit in a finite lifetime, an expected benefit model is formulated based on maximal/equal cumulative-hazard rate constraints, and the optimal PM intervals are obtained using a genetic algorithm (GA). It is usually difficult to determine fixed maintenance quality after performing maintenance activities. This problem is addressed in the present paper by assuming that the reduction factor is a stochastic variable following probability distributions at fixed times. It is more rational to describe the fluctuation and trend of quality of discrete preventive maintenance during a lifetime; this makes optimisation results more robust and insensitive to the randomness of the crucial parameters in imperfect PM models. A numerical case is presented to illustrate the proposed model and some discussions are summarised.

Phasic Sequential Preventive Maintenance Policy Based on Imperfect Maintenance for Deteriorating Systems

The state of most systems after maintenance is somewhere between as good as new and as bad as old.Based on the imperfect maintenance theory,a new improvement factor considering the equipment working age and the maintenance cost is introduced.In order to make the policy more suitable in operation,the concept of phasic sequential preventive maintenance is put forward.By minimizing the cost rate of the maintenance in the life cycle of the system,two sequential preventive maintenance models are proposed,the general sequential one and the phasic sequential one.The optimal preventive maintenance intervals and the number of preventive maintenance activities are obtained from the model.The numerical results show that the phasic sequential preventive maintenance policy is more operable in the actual maintenance activities and more appropriate to the properties of the deteriorating systems.Compared with the general model,the phasic one achieves a better economical efficiency and operability at the same maintenance cost.