Condition-Based Maintenance Decision-Making for Multiple Machine Systems

This paper is concerned with a particular flexibility parameter, referred to as mix response flexibility. This is a measure of the difficulty of processing different products on the same equipment, where a different set-up is required for each product type. A mathematical model for measuring mix response flexibility is described, and its results are compared with a simulation. The model is found to give meaningful results for both single machines and multiple machine systems.

With the popularization of service-oriented manufacturing, the current operation & maintenance (O&M) has shifted from traditional in-house maintenance to proactive outsourcing maintenance. It is paramount for an original equipment manufacturer (OEM) to provide timely and cost-effective maintenance schemes to geographically distributed customer enterprises. Interestingly, transportation theories could be combined to facilitate multi-location O&M management. In this paper, a transportation-oriented cross-regional opportunistic maintenance (TCOM) policy is developed for solving O&M optimizations and planning real-time schemes for the multi-center service network. The optimization model of this TCOM policy addresses several inter-related decisions: (1) most suitable maintenance times for each leased machine, (2) cost-effective arrangements of technician teams to perform maintenance tasks, and (3) optimal service routes for required teams. We not only integrate maintenance grouping and technician routing, but also investigate the new issues arising from the collaborative sharing of technician teams belonging to different maintenance centers. Numerical examples show that this TCOM policy can achieve significant cost-saving in cross-regional maintenance grouping and multi-location routing optimization for OEMs. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the critical problem of computing an optimal maintenance policy for the multi-center service network of geographically distributed production systems to achieve a timely and cost-effective O&M management. We consider individual machine degradations, complex maintenance opportunities and network logistics optimization to establish a global maintenance scheme for the multi-center service network. The existing approaches are to schedule the preventive maintenance (PM) for a single machine or multi-unit leased system, which is unilateral and cannot be applied to multi-location production systems. Although the network opportunistic maintenance policy has been proposed in the literature for maintenance grouping and technician routing problem, it still does not consider the limited workforce resource and the collaborative sharing of technician teams from different maintenance centers. To fill this literature gap, in this paper, we develop the TCOM policy to determine the optimal maintenance timetable for leased machines, as well as the cost-effective service route of technician teams belonging to multiple maintenance centers, so as to conduct timely and cost-effective PM on multi-location production systems. Compared with the conventional maintenance policy, our adaptive policy greatly reduces the total outsourcing maintenance cost in long-term O&M services.

This study proposes a sequential Condition-Based Maintenance (CBM) policy for intelligent monitored system based on cost and reliability prioritisation. It is assumed that system's reliability could be continuously monitored, whenever it reaches the threshold R, and scheduled maintenance activity is performed to restore the system. This maintenance policy differs from other policies in taking into consideration of influences from the frequency of maintenance activities and operating time on system's failure rate function subject to a deterioration process. Meanwhile, this CBM policy firstly considers the variant operational cost and variant minimal repair cost by the effects of system's corresponding condition, which helps to construct more practical maintenance model. Finally, a numerical example is presented to test this proposed sequential predictive maintenance policy and the simulation results show its efficiency.

Joint optimization of maintenance and spares inventory policy for a series-parallel system considering dependent failure processes

This paper proposes a condition-based maintenance policy for gearbox with online monitoring information. The bispectrum entropy is chosen as the extracted degradation feature. Effective maintenance decisions based on proportional hazards model is developed, jointly considering the degradation data and the failure time data. The optimal maintenance policy set two control limits, including both hazard rate control limit and age control limit. The optimal maintenance decisions is calculated by minimize the average per unit time cost rate. The method is illustrated by a simulation case. A comparison with other policies proves the effectiveness of the research method.

Condition-based maintenance (CBM) has been a research topic since 1975. It has been introduced as an alternative approach to enhance the effectiveness of preventive maintenance strategy. Currently, CBM research is growing rapidly. Compared with the traditional time-based maintenance approach, CBM application is more beneficial and realistic. With CBM, better maintenance decisions can be made to avoid or minimise unnecessary maintenance costs. This paper attempts to explore how exactly CBM decision-making is conducted, with the methods of decision-making classified into current-condition-evaluation-based and future-condition-prediction-based. This paper systematically reviews the applications of these methods by focusing on the techniques used, as well as on case studies. It concludes with findings based on the academic and industrial perspectives.

Reduction of energy consumption has attracted much attention of the researchers and practitioners in the field of sustainable manufacturing. However, the issue of integrated optimal production and energy control for sustainable manufacturing systems has not yet been investigated sufficiently. In this paper, we study such a problem for a single machine and single product-type manufacturing system, with the objective of minimizing the sum of production cost and energy consumption cost. To solve this problem, a Markov decision process model is developed, based on which, we obtain the integrated optimal production and energy control policy by the policy iteration method. Furthermore, we reveal the structural characteristics of optimal control policy and analyze the influence of system parameters on it by comparing different cases.

As a maintenance strategy to reduce unexpected failures and enable safe operation, condition-based maintenance (CBM) has been widely used in recent years. The maintenance decision criteria of CBM in the literature mostly originate from statistical failure data or degradation states, few of which can directly and effectively reflect the current state and analyze condition monitoring data, maintenance measures, and reliability together at the same time. In this paper, we introduce the performance margin as a decision criterion of CBM. We propose a condition-based maintenance optimization method using performance margin. Considering a CBM optimization problem for a degrading and periodically inspected component, a newly developed performance margin degradation model is established when three different maintenance measures become involved. Maintenance measure effect factors, maintenance decision vectors, and maintenance measure threshold vectors are developed to update the degradation model. And to build a maintenance optimization model, both cost and loss related to maintenance decision problems and reliability obtained by performance margin have been taken into consideration. Finally, a numerical example is provided to illustrate the proposed optimization method.

The condition-based maintenance (CBM) is an advanced maintenance program that recommends optimal maintenance policy based on information collected through condition monitoring techniques. At present, CBM was paid attention to widely. The paper theoretically discussed the structure of a complete CBM system based upon the combing and analysis of the current large numbers of literatures of CBM. Firstly, CBM is introduced. Secondly, the three main stages of CBM work process is given. Finally, the basic structural frame of a complete CBM system is designed and constructed according to a series of functions CBM system should implement.

The main idea of condition based maintenance (CBM) is to monitor the health of critical machine components and system almost continuously during operation and maintenance actions based on the assessed condition. If done correctly, CBM has the benefits such as reducing catastrophic failures, minimizing maintenance and logistical cost, maximizing system security and availability and improving platform reliability. A CBM system usually has four functional modules: feature extraction, diagnostics, prognostics and decision support. Among them, fault prognostics is the most important enabling technology. It is the most challenging research area which is so called crystal ball of CBM. But it has the potential to be the most beneficial one. A review of recent progress of fault prognostics is conducted with the emphasis placed on its algorithmic approaches. These approaches generally fall into four main categories, namely experience-based approaches, model-based approaches, knowledge-based approaches and data-driven approaches. Based on the analysis of some typical examples on each prognostic approaches, the advantages and disadvantages of these approaches are further discussed. Finally, the future challenges concerned with fault prognostics are also presented.

In this paper a multi-state deterioration system which experiences several states of performance degradation until it fails is studied extensively and condition-based preventive maintenance policies are examined. The optimal maintenance policy aims at maximizing system's asymptotic availability and at minimizing its total operational cost, with respect to the two different inspection intervals. For the simultaneous optimization of the aforementioned measures, multi-objective optimization methods are employed. In the current work, the inspection times are assumed to be constant, thus the system's evolution in time is modeled by a semi-Markov process. Finally, the proposed system is compared with the corresponding Markov one which is the most commonly used approximation of the original system in practice.

Maintenance is an inevitable reality in industry. Maintenance of a system usually involves maintenance of multiple components with multiple failure modes, each of which may require different maintenance policy (i.e., corrective (CM), preventive (PM), or condition based maintenance (CBM)). A maintenance policy may be best for one component and the worst for the other (CM may be best for a very cheap and non-critical component and the worst for a critical one). This paper presents an economical analysis method that identifies the best maintenance policy for a failure mode and/or component of a system.

Joint optimization of condition-based maintenance and inventory control for a k-out-of-n:F system of multi-state degrading components

In this paper, a novel condition‐based maintenance (CBM) policy for systems with mutually dependent competing failure processes (DCFPs) is studied. The cumulative random shocks contribute to the increased rate of degradation, meanwhile, the hard failure threshold is reduced when the amount of degradation reaches a threshold. The degradation process is simulated by a non‐stationary Gamma process and random shocks are modeled as a homogeneous Poisson process. Preventive replacement (PR) and corrective replacement (CR) are accounted for in the developed maintenance strategy. The optimal periodic inspection interval, PR threshold, and the number of cumulative random shocks can be derived by minimizing the average long‐term maintenance cost rate. In the end, a numerical case is applied to demonstrate the availability of the developed models.

In order to ensure the normal and safe operation of gasifier, it is necessary to ensure that the remaining life of refractory brick, the key component of gasifier, is in a healthy range, so it is necessary to formulate a timely and reasonable maintenance strategy for refractory brick. In order to avoid the problem of excessive Maintenance and insufficient Maintenance, CBM (Condition Based Maintenance) related research on gasifier refractory brick. This article first to identify the current state of the gasifier refractory brick, and then based on this state for different refractory brick their preventive maintenance strategy, can largely avoid excessive problem, lack of maintenance and repair in a timely manner to maintain the gasifier refractory reasonably, to extend its service life, guaranteeing the normal safe operation of the gasifier.