A general inspection and opportunistic replacement policy for one-component systems of variable quality

We model the influence of opportunities in a hybrid inspection and replacement policy. The base policy has two phases: an initial inspection phase in which the system is replaced if found defective; and a later wear-out phase that terminates with replacement and during which there is no inspection. The efficacy of inspection is modelled using the delay time concept. Onto this base model, we introduce events that arise at random and offer opportunities for cost-efficient replacement, and we investigate the efficacy of additional opportunistic replacements within the policy. Furthermore, replacements are considered to be heterogeneous and of variable quality. This is a natural policy for heterogeneous systems. Our analysis suggests that a policy extension that allows opportunities to be utilised offers benefit, in terms of cost-efficiency. This benefit is significant compared to those offered by age-based inspection or preventive replacement. In addition, opportunistic replacement may simplify maintenance planning

Predictive Group Maintenance Model for Networks of Bridges

Recent progress in the monitoring and prediction of the condition of infrastructure using sensing technologies has motivated researchers and infrastructure owners to explore the benefits of asset predictive maintenance, as an alternative to reactive maintenance. However, the application of predictive group maintenance for multi-system multi-component networks (MSMCN) has not received much attention in the literature or in practice. The paper presents an approach that prioritizes the maintenance of MSMCN of bridges, using a deterioration model of components with uncertainty, a lifecycle cost model, a predictive model for the optimal time for maintenance based on the latest inspection, a group maintenance model to reduce setup cost, and a scheduling model considering budget constraints. This model has been applied to a network of 15 bridges constituted by multiple heterogeneous components, and, compared with the Structures Investment Toolkit, it showed potential for a substantial decrease in maintenance costs, thus highlighting the practical significance of the presented approach. EU H202

Delay-time modelling of a critical system subject to random inspections

We model the inspection-maintenance of a critical system in which the execution of inspections is random. The models we develop are interesting because they mimic realities in which production is prioritised over maintenance, so that inspections might be impeded or they might be opportunistic. Random maintenance has been modelled by others but there is little in the literature that relates to inspection of a critical system. We suppose that the critical system can be good, defective or failed, and that failure impacts on production, so that a failure is immediately revealed, but a defect does not. A defect, if revealed at inspection, is a trigger for replacement. We compare the cost and reliability of random inspections with scheduled periodic inspections and discuss the implications for practice. Our results indicate that inspections that are performed opportunistically rather than scheduled periodically may offer an economic advantage provided opportunities are sufficiently frequent and convenient. A hybrid inspection and replacement policy, with inspections subject to impediments, is robust to departure from its inspection schedule. Keywords: Maintenance; reliability; random inspection; production; qualit

Modified age-based replacement

The maintenance policy of age-based replacement (ABR) is widely specified in OEM instructions. The practical application of ABR raises concerns about ensuring consistent adherence to prescribed replacement schedules for extended periods. ABR lacks periodicity, resulting in scheduling asynchrony with designated time slots, while alternative policies such as block replacement (BR) provide periodicity at the expense of efficiency. Additionally, scepticism about ABR is based on its simplicity and restrictive assumptions, which include ideal replacements and the one-component system assumption. The task of estimating component lifetime distributions and defining critical parameters such as cost of failure, which is an average cost with varying downtime, presents significant challenges. We study “modified age-based replacement” (MABR) in response to the limitation of periodicity, so that preventive replacements exhibit quasi-periodic behaviour. We quantify the cost-inefficiency of MABR compared to ABR, thus informing the practical implications of introducing periodicity into the ABR policy and highlighting the need to incorporate real-world constraints, such as time slots for maintenance actions. The findings indicate that MABR and a special case are reasonably efficient provided the slot-interval is not too large. This is a useful insight for practical application of ABR type policies for scheduling preventive maintenance

Predictive group maintenance for multi-system multi-component networks

Predictive maintenance has become highly popular in recent years due to the emergence of novel condition monitoring and data analysis techniques. However, the application of predictive maintenance at the network-level has not seen much attention in the literature. This paper presents a model for predictive group maintenance for multi-system multi- components networks (MSMCN). These networks are composed of multiple systems that are, in turn, composed of multiple components. In particular, the hierarchical structure of the MSMCN enables different representations of dependences at the network and system levels. The key novelty in the paper is that the designed approach combines analytical and numerical techniques to optimize the predictive group maintenance policy for MSMCNs. Moreover, we introduce a genetic algorithm with agglomerative mutation (GA-A) that enables a more effective evolution of the predictive group maintenance policy. Application of this model on a case study of a two-bridge network made of 23 different components shows a potential 11.27% reduction in maintenance cost, highlighting the model’s practical significance.This research was funded by the Engineering and Physical Sciences Research Council (UK) and Innovate UK through the Innovation and Knowledge Centre for Smart Infrastructure and Construction (Grant EP/N021614/1). This work was partially supported by Talent recruitment Funds of Tsinghua University grant NO.113052

Conditional inspection and maintenance of a system with two interacting components

In this paper we consider the inspection and maintenance of a two-component system with stochastic dependence. A failure of component 1 may induce the defective state in component 2 which in turn leads to its failure. A failure of component 1 and a defect in component 2 are detected by inspection. Our model considers a conditional inspection policy: when component 1 is found to have failed, inspection of component 2 is triggered. This opportunistic inspection policy is a natural one to use given this stochastic dependence between the components. The long-run cost per unit time (cost-rate) of the conditional inspection policy is determined generally. A real system that cuts rebar mesh motivates the model development. The numerical examples reveal that when the ratio of the cost of corrective system replacement, that is on failure, to the cost of preventive system replacement is large there exists a finite optimum policy in most cases. Moreover, for the studied system wherein inspections of component 2 are expensive relative to those of component 1, having a reliable indicator of the defective state in component 2 is a good strategy to avoid costly failures of component 2, particularly when its time to failure is short

Methodology for the Maintenance Centered on the Reliability on Facilities of Low Accessibility

AbstractThis paper presents the importance of obtaining the application of a maintenance technique that satisfies in a precise way the different needs of the production process, independently of its technical complexity or difficulty of access to the industrial plant facilities. This is the case of the plants with a high automation level or wind farms located in remote places with low accessibility. Besides this, the studied situations have in common the low level of physical operation in its production process

Investigation On The Influence Of Remanufacturing On Production Planning And Control – A Systematic Literature Review

Production planning and control (PPC) is one of the focal operational tasks of a company, and it is used to design logistics services in a target-orientated manner so that individual customer requirements can be fulfilled. However, existing PPC framework models are still based on the prevailing linear economic procedure (take - make - dispose). Due to customers' increasing interest in sustainability and growing regulatory pressure, the Circular Economy (CE) meets these changing conditions by closing material cycles, improving resource efficiency and extending product life cycles. However, for a company to guarantee a high logistics performance, the operational PPC must be adapted to this new economic model. To this end, it needs to be investigated whether and how the adaptation of circular strategies influences existing PPC processes. This paper focuses on the circular strategy of remanufacturing and its influence on different PPC-main tasks. The latter will be examined using a systematic literature review. Finally, the results of this analysis are compared with the Hanoverian Supply Chain Model as a PPC framework model. This comparison shows which PPC tasks are affected and which existing approaches have already been developed. Ultimately, these results provide the basis for developing a framework model for operational PPC regarding the CE

Hybrid Statistical, Machine Learning, and Deep Learning Models for Fault Diagnosis and Prognosis in Condition-based Maintenance

Maintenance has always been an essential and inseparable part of manufacturing and industrial sectors. Generally speaking, maintenance strategies aim to prevent asset failures/downtimes to protect investments and to provide a safe working environment. With the recent growth in sensor and data acquisition technologies, a rich amount of condition monitoring data has become available in manufacturing and industrial sectors. Consequently, there has been a recent surge of interest in using more advanced solutions, especially those based on Machine Learning (ML), Reinforcement Learning (RL), and Deep Learning (DL) models, to utilize such extensive and high-quality data in the maintenance domain. In this context, the thesis proposed different ML and hybrid models for prognostic and health management purposes to further advance the maintenance field. In particular, we conducted the following three studies: In the first work, a hybrid and semi-supervised framework is designed based on the hazard rate of the system. The proposed framework can extract the hidden state of the system without domain knowledge. To evaluate the efficacy of the proposed method, a real dataset is used where optimal maintenance policies are obtained based on the extracted states via RL. In the second study, a DL-based model is proposed to predict the hazard rate of the underlying system. As opposed to its statistical counterparts, the proposed predictive model does not assume any linear relationship between the sensors' measurements, and is capable of learning from censored data. In the last study, we investigated application of the proposed methods on high-dimensional data such as images. The proposed methods achieved promising results illustrating their great potential to be used in real-world applications

Hardware acceleration for power efficient deep packet inspection

The rapid growth of the Internet leads to a massive spread of malicious attacks like viruses and malwares, making the safety of online activity a major concern. The use of Network Intrusion Detection Systems (NIDS) is an effective method to safeguard the Internet. One key procedure in NIDS is Deep Packet Inspection (DPI). DPI can examine the contents of a packet and take actions on the packets based on predefined rules. In this thesis, DPI is mainly discussed in the context of security applications. However, DPI can also be used for bandwidth management and network surveillance. DPI inspects the whole packet payload, and due to this and the complexity of the inspection rules, DPI algorithms consume significant amounts of resources including time, memory and energy. The aim of this thesis is to design hardware accelerated methods for memory and energy efficient high-speed DPI. The patterns in packet payloads, especially complex patterns, can be efficiently represented by regular expressions, which can be translated by the use of Deterministic Finite Automata (DFA). DFA algorithms are fast but consume very large amounts of memory with certain kinds of regular expressions. In this thesis, memory efficient algorithms are proposed based on the transition compressions of the DFAs. In this work, Bloom filters are used to implement DPI on an FPGA for hardware acceleration with the design of a parallel architecture. Furthermore, devoted at a balance of power and performance, an energy efficient adaptive Bloom filter is designed with the capability of adjusting the number of active hash functions according to current workload. In addition, a method is given for implementation on both two-stage and multi-stage platforms. Nevertheless, false positive rates still prevents the Bloom filter from extensive utilization; a cache-based counting Bloom filter is presented in this work to get rid of the false positives for fast and precise matching. Finally, in future work, in order to estimate the effect of power savings, models will be built for routers and DPI, which will also analyze the latency impact of dynamic frequency adaption to current traffic. Besides, a low power DPI system will be designed with a single or multiple DPI engines. Results and evaluation of the low power DPI model and system will be produced in future