Ramin Moghaddass

Selective maintenance for binary systems under imperfect repair

In many industrial environments like manufacturing systems, military equipments, power generation systems, etc., system maintenance is performed between successive missions. Different maintenance options (do nothing, minimal repair, preventive maintenance options or system overhaul, etc.) are possible for components in the system. However, it may not be feasible to do all possible maintenance actions during the maintenance break. Hence, optimal maintenance decision is required such that available resources are optimally used to maximize the next mission reliability. In this paper, a mathematical model is used to help in decision making for selective maintenance under imperfect repair. The level of maintenance actions determines the improvement in the component health. A model is formulated to relate the amount of resources used for maintenance to the level of imperfect repair. Further, a characteristic constant is used which determines the component response to resource consumed by a maintenance task. Selective maintenance model is formulated and illustrative examples are used to demonstrate the applicability and advantages of the proposed method. The results show that introduction of imperfect repair facilitates better allocation of maintenance resources.

An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process

Efficient asset management is of paramount importance, particularly for systems with costly downtime and failure. As in energy and capital-intensive industries, the economic loss of downtime and failure is huge, the need for a low-cost and integrated health monitoring system has increased significantly over the years. Timely detection of faults and failures through an efficient prognostics and health management (PHM) framework can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize the overall maintenance cost of the systems. This paper aims at practical challenges of online diagnostics and prognostics of mechanical systems under unobservable degradation. First, the elements of a multistate degradation structure are reviewed and then a model selection framework is introduced. Important dynamic performance measures are introduced, which can be used for online diagnostics and prognostics. The effectiveness of the result of this paper is demonstrated with a case study on the health monitoring of turbofan engines.

Selective maintenance modeling for a multistate system with multistate components under imperfect maintenance

In many industrial environments, maintenance is performed during successive mission breaks. In these conditions, it may not be feasible to perform all possible maintenance actions due to limited maintenance resources such as time, budget, repairman availability, etc. A subset of maintenance actions is then performed on selected components such that the system is able to meet the next mission requirement. Such a maintenance policy is called selective maintenance. In this article, a selective maintenance strategy is developed for a MultiState System (MSS). The system can have several finite levels of performance in an MSS. Previous studies on selective maintenance have solely focused on MSSs with binary components. However, components in an MSS may be in more than two possible states. Hence, a series-parallel MSS that consists of multistate components is considered in this article. Imperfect maintenance of a component is considered to be a maintenance option, along with the replacement and the do-nothing options. Maintenance resources need to be allocated such that maximum system reliability during the next mission is ensured. A universal generating function is used to determine system reliability. An illustrative example is presented that depicts the advantages of utilizing imperfect maintenance/repair options.

Availability of a general k-out-of-n:G system with non-identical components considering shut-off rules using quasi-birth–death process

Abstract A general repairable k-out-of-n:G system with non-identical components, which is a common form of redundancy, is considered in this paper. The number of repairmen is assumed to be r (1≤r≤n−k+1) while components can have similar or different repair priorities. The objective of this work is to address the problem of efficient evaluation of the systems availability in a way that steady state solutions can be obtained systematically in a reasonable computational time. This problem is modeled as a finite state-dependent non-homogeneous quasi-birth–death (QBD) process. An algorithm is introduced to systematically generate the system state vectors and transition rate matrix and then an iterative method based on the Block Gauss–Seidel method is employed to determine the steady state probabilities. These are the novel contributions made in this paper. An analog Monte Carlo simulation is presented to demonstrate the correctness and the efficiency of the proposed method.

Reliability and Availability Analysis of a Repairable

The k-out-of-n:G system is widely used in reliability and maintenance engineering. We consider a general k-out-of-n:G system which has identical components with identical repair time and lifetime distributions. There are R identical repairmen in the system. The shut-off rules of suspended animation, continuous operation, and a mixture of these two are studied. Repair times and lifetimes are assumed to be statistically independent and exponentially distributed (within, and between). We derive new closed form solutions for important performance measures including steady state availability, mean time to system failure, and mean time to first system failure.

k

Abstract The overall performance of a mechanical device under random shocks, fatigue, and gradual degradation may continuously deteriorate over time, leading to multi-state health conditions. This deterioration can be represented by a continuous-time degradation process – with multiple discrete states – that reflects the relative degree of deterioration. This paper focuses on a condition-monitored device with multi-state deterioration, where its degradation state is not directly observable and only incomplete information is available through condition monitoring. After modeling this multi-state device, an unsupervised parameter estimation method is developed, which employs historical condition monitoring information to estimate the unknown characteristic parameters of the degradation process and the observation process. The results are evaluated through numerical experiments.

-out-of-

Multistate reliability has received significant attention over the past decades, particularly its application to mechanical devices that degrade over time. This degradation can be represented by a multistate continuous-time stochastic process. This article considers a device with discrete multistate degradation, which is monitored by a condition monitoring indicator through an observation process. A general stochastic process called the nonhomogeneous continuous-time hidden semi-Markov process is employed to model the degradation and observation processes associated with this type of device. Then, supervised parametric and nonparametric estimation methods are developed to estimate the maximum likelihood estimators of the main characteristics of the model. Finally, the correctness and empirical consistency of the estimators are evaluated using a simulation-based numerical experiment.

n:G

The configuration of a repairable system directly affects its performance measures such as mean time between failures and steady state availability. Corrective, preventive, and condition-based maintenance strategies can also affect the overall performance of the system. The objective of this work is to investigate the possible trade-off between the configuration of a repairable k-out-of-n:G system and its maintenance strategy. The redundancy (the number of active red undant components) and the number of cold standby components are considered to be the design decision variables, wh ereas the repair capacity and the maintenance activation point are considered to be the maintenance decision variables. The corresponding stochastic process for this model is formulated using the continuous time Markov process; as well an optimization model is introduced for cost-effective design of a repairable k-out-of-n:G system. The optimization model is used to minimize the overall operational and maintenance cost associated with the system, considering constraints on availability, space and/or budget. Finally, genetic algorithm is used to find the optimal values for the decision variables. The result is demonstrated using a numerical example.

System With

In this paper, we report a study of the reliability optimal design of multi-state weighted series-parallel systems. Such a system and its components are capable of assuming a whole range of levels of performance, varying from perfect functioning to complete failure. There is a component utility corresponding to each component state. This system model is more general than the traditional binary series-parallel system model. The so-called component selection reliability optimal design problem which involves selection of components with known reliability characteristics and cost characteristics has been widely studied. However, the problem of determining system cost and system utility based on the relationships between component reliability, cost and utility has not been adequately addressed. We call it optimal component design reliability problem which has been studied in one of our former papers and continued in this paper for the multi-state weighted series-parallel systems. Furthermore, comparing to the traditional single-objective optimization model, the optimization model we proposed in this paper is a multi-objective optimization model which is used to maximize expected system performance utility and system reliability while minimizing investment system cost simultaneously. Genetic algorithm is used to solve the proposed physical programming based optimization model. An example is used to illustrate the flexibility and effectiveness of the proposed approach over the single-objective optimization method.

R

A preventive maintenance scheduling model is proposed in this article. The proposed model includes finite planning horizon and limited available resources to perform maintenance scheduling. A subset of maintenance actions, that is, selective maintenance is needed during maintenance breaks due to limited resources such as time, cost, and repairman availability. Maintenance can not only improve the effective age of a component but also may alter the hazard rate. Therefore, a hybrid imperfect maintenance model is used in this article that considers the combined effect of age reduction and hazard adjustment on a component. For a multi-component system, selective maintenance is performed at periodic intervals. In addition to maintenance and failure costs, we have included the maintenance break duration and the shutdown cost in the proposed scheduling model. A periodic maintenance scheduling problem is solved in this article for a series–parallel system. The optimal number of periodic maintenance breaks in a finite planning horizon is determined. Also, maintenance actions required during each of the maintenance breaks are determined. The number of periodic maintenance breaks and maintenance actions during these breaks is selected in a way that the total maintenance, failure, and shutdown cost are minimum. An evolutionary algorithm is used to solve the problem.