Time-based Preventive Maintenance Research Articles

Feasibility and foundational elements of CBM+ technology application in weapon systems

The maintenance strategies of weapon systems have evolved from corrective maintenance post-failure to time-based preventive maintenance, driven by the growing adoption of diagnostic technologies and data utilization. Currently, there is a transition to Condition-Based Maintenance (CBM) and, where applicable, Condition-Based Maintenance Plus (CBM+). This study investigated the feasibility of applying CBM+ technology using sensor data from weapon systems and explored methods for implementing the related foundational elements. First, the feasibility of CBM+ technology was confirmed by integrating data collection devices into the engine components of selected weapon systems, enabling sensor data collection and monitoring. Second, empirical evidence was gathered on the foundational elements necessary for the broader adoption of CBM+ technology. Practical application methods were proposed based on this evidence, including criteria for implementing CBM+ technology in major operational weapon systems, estimation of sensor data capacity, network methods for data transmission and collection, and the standardization of sensor data. The study’s findings are expected to provide reference points for selecting target weapon systems for the development and application of CBM+ technology, as well as outline essential foundational elements, such as security, networking, and data standardization, critical for leveraging related technologies.

Phase-type distribution models for performance evaluation of condition-based maintenance

ABSTRACT Condition-based maintenance (CBM) is gaining attention due to sensor and cloud-based analytics advancements, but research on its impact on system-level performance is limited. Insufficient understanding during CBM implementation can lead to confidence issues and failures. This study introduces a class of models using phase-type distribution to assess three maintenance strategises: run-to-failure (RTF), time-based preventive maintenance (TBM), and CBM. Employing machine health-index, the framework characterizes production performance by estimating effective process times. The model demonstrates how adjusting CBM thresholds influences process time variations and assesses the impact of changing maintenance frequency for TBM. Applied to a smart cellular manufacturing system, the model shows CBM’s early-stage implementation. Findings indicate CBM with optimized thresholds boosts maximum throughput by 6.77%. Further, CBM achieves an additional 6.84% increase assuming corrective maintenance time can be reduced by 20%. This approach can help manufacturing become smarter through smarter maintenance in the Industry 4.0 era and beyond.

Designing a Resilient Building Maintenance Program: Integrating Preventive and Predictive Maintenance at the U.S. Navy

ABSTRACT Building equipment failure can have drastic effects on a company's operations and budget. This paper presents two types of maintenance approaches that if done effectively, can prevent or significantly reduce the failure of building equipment assets. The first is traditional time-based preventive maintenance (PM), which conducts prefailure inspections and tasks in a cyclic time-based approach. The second, is predictive maintenance (PdM), which conducts maintenance functions based on the condition of the equipment found through continuous or cyclic measurements and analysis during machine operation. The purpose of investigating these maintenance approaches is to determine whether we can improve on the U.S. Navy's (Navy) existing facility maintenance program, helping to reduce overall costs while improving sustainability, equipment resiliency, and efficiency. By presenting each maintenance program and leveraging today's technologies we show that these advancements in technology can directly improve the Navy's operational mission and warfighter readiness. Research was conducted through the following methods: interviews, books, third party reports, journal articles, industry websites and articles that focus on equipment maintenance. The Navy, the National Aeronautics and Space Administration (NASA), and the University of Washington provided case studies, existing facility/utility maintenance data, and budget information used in this research. The results show that PdM approaches using advanced analytics are more effective in diagnosing equipment, prescribing equipment problems, and predicting equipment failure. It will also show that when a PdM model is used, building tenants have less operational impacts as equipment operates longer with less downtime between maintenance events. By changing to a PdM program, facility managers and owners can improve asset efficiency and resilience, directly improving environmental sustainability and lowering overall longterm costs. It highlights the significant capital costs of a fully online PdM program and the benefits of using a hybrid model of PM and PdM. This research concludes with an overview of how building maintenance is currently being conducted on Navy bases by Naval Facilities Engineering Systems Command (NAVFAC), and how they are transitioning to a more sustainable maintenance program leveraging existing advanced metering infrastructure (AMI), building control systems (BCS), and utility control systems (UCS) with Smart Grid (SG), OSI Pi, and advanced analytics. In addition, major gaps in this transition are identified, and solutions are proposed to optimize these building system investments.

Big Data Analytics for Mobility Prediction and Its Classification

Kern forecasting is the detection of which customers may leave a service or cancel a subscription for a service. This is an important forecast for many businesses because gaining new customers will cost more than retaining existing customers. The Retail Banking Seer Forecast is an AI-based model that helps customers assess the likelihood of your bank being blocked. Odor is a good indicator of growth. By comparing and analyzing these two metrics that monitor lost customers, and growth rates, new customers, Kern rates accurately tell you how much your business is growing over time. If growth is greater than recession, you can say that your business is growing. With increasing skill requirements and requirements for quality of experience, mobility forecasting has become widely used for mobile communication and has become one of the key processors that use historical transport information to predict the future locations of traffic users. Predictive maintenance refers to the use of data-driven, efficient maintenance methods, via designed to analyze the Status and maintenance of equipment When Predict what needs to be done. Forecasting maintenance is a type of maintenance that directly monitors the health, condition and performance of an asset in real time. Predictive maintenance is aimed at minimizing costly and unexpected breakdowns and gives the manufacturer the opportunity to plan maintenance around their own production schedule. Some examples of the use of forecast maintenance and forecast maintenance sensors include vibration analysis, oil analysis, Includes thermal imaging and equipment monitoring. This approach guarantees cost of savings in routine or time-based preventive maintenance, due to the tasks are only done when guaranteed. Predictive maintenance techniques to determine the condition of equipment in service Designed to help and evaluate when maintenance should be done. Big Data Analytics is structured, semi-structured, Is the use of advanced analytical techniques against very large, diverse datasets. and an unstructured data of various sizes ranging from different sources to terabytes to zeta bytes. Choosing a career in the field of big data and Analytics can be an exciting career endeavor, and it can be the type of role you are trying to find. Machine learning (ML) is a prediction that considers large-scale multidimensional data from a variety of sources allows you to create models. Several studies have been conducted on the use of ML algorithms to predict road traffic. Traffic forecast is floating car data and traffic flow, average traffic speed and based on historical traffic data such as traffic events The task is to predict real-time traffic information.

Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem

This paper addresses a problem faced by maintenance service providers: performing maintenance activities at the right time on geographically distributed machines subjected to random failures. This problem requires determining for each technician the sequence of maintenance operations to perform to minimize the total expected costs while ensuring a high level of machine availability. To date, research in this area has dealt with routing and maintenance schedules separately. This study aims to determine the optimal maintenance and routing plan simultaneously. A new bi-objective mathematical model that integrates both routing and maintenance considerations is proposed for time-based preventive maintenance. The first objective is to minimize the travel cost related to technicians’ routing. The second objective can either minimize the total preventive and corrective maintenance cost or the failure cost. New general variable neighborhood search (GVNS) and variable neighborhood descent (VND) algorithms based on the Pareto dominance concept are proposed and performed over newly generated instances. The efficiency of our approach is demonstrated through several experiments. Compared to the commercial solver and existing multi-objective VND and GVNS, these new algorithms obtain highly competitive results on both mono-objective and bi-objective variants.

Maintenance in Marginal Oilfield Production Facilities: A Review

Natural decline in various mainstream oilfield reserves and the high investment capital in upstream exploration and project development have promoted attention towards smaller oilfields referred to as Marginal fields. This provides operators the opportunity to commence exploration and production with minimum requirements of design, installation, and operations. Although the low Capital Expenditure (CAPEX) requirement favors the start-up of marginal oilfield operations, several operators are not able to sustain the field’s operations due to the high Operational Expenditure (OPEX), particularly arising from facilities’ maintenance. The aim of this paper is to review the maintenance strategies adopted in marginal oilfields, assess their effectiveness, and provide a pointer towards efficient and viable maintenance strategies for the sustainability of marginal oilfields. The study showed that time-based preventive maintenance is predominant in the oil industry, which constitutes up to 40% of net operational expenses. In other cases, reactive maintenance is adopted, which often results in an unplanned shutdown, known to be responsible for nearly half of the overall losses of an oil facility. A paradigm shift in maintenance to Reliability Centered Maintenance (RCM) was explored for marginal oilfield, with a comprehensive review of various maintenance strategies, ranging from maintenance optimization strategies, Heuristics and Metaheuristics, Artificial Intelligence (AI), and Data Mining techniques. It was observed that the application of AI best addresses the proposed RCM for marginal oilfields. This was drawn from the recorded limitations of the other concepts from verifiable similar works, where different AI techniques and Data analytics methods have been successfully applied to aid RCM.

Model-Based Control Allocation Strategies for Predictive Maintenance of Saturated Actuators

Predictive maintenance techniques are getting increasing interest in industry because instrumental to determine suitable reliability conditions of in-service equipment aimed at smartly establishing when a maintenance operation should take place. This approach presents economic benefits when compared to more traditional routines or time-based preventive maintenance because tasks are performed only when warranted. Within this context, this work presents a control allocation scheme based on Model Predictive Control ideas to deal with actuator loss of effectiveness related to their usage. These undesired events occur when the associated degradation effects, whose evolution is assumed to be measurable and/or predictable, overcome a given threshold. The proposed scheme consists of two modules: a prognostic unit in charge of monitoring the actuators’ health and a reconfigurable control allocation module whose action is based on the current actuators’ degradation level. Its main goal is to establish a suitable time interval during which a maintenance service can occur without affecting the stability of the controlled system and the feasibility properties of the control allocation scheme.

Wavelet Scattering Network Based Bearing Fault Detection

Rotating machines are broadly used in the manufacturing industry and they usually need to remain operational for an extended period and in harsh environments, which causes degradation and can eventually lead to failures in components.
 Bearings are one the most critical components in rotating machines. Bearing failures result in unplanned downtime and thus impacting the production and maintenance costs. To reduce the costs, monitoring the condition of bearings plays, therefore, a vital role in the maintenance program of the industry. It could allow to move from a time-based preventive maintenance program to a condition-based maintenance (CBM) or predictive maintenance (PdM) strategy. An essential aspect in bearing fault detection are methodologies that compute and/or calculate condition indicators (features) from noisy vibration signals acquired during early fault stages. These strategies ought to be: (1) data-efficient: one is faced, in many industrial settings, with a lack of training data, a lack of ground truth and scarce information on machine design and operational conditions, (2) power-efficient: it is important that the edge device that is monitoring the health of the machine is power-efficient, as edge devices are often battery-powered, and (3) insensitive to industrial disturbances such as signals originating from other mechanical or electrical components or sensor noise. These are challenges often faced by companies. Providing technological solutions to these issues reduces unexpected downtime and additional costs, including avoiding unnecessary and costly replacement of healthy machine components.
 In light of the aforementioned technological challenges, we present the wavelet scattering network application to bearing fault detection, this technique is based on wavelet transform, where it computes translation-invariant features that are locally stable to deformation, making it particularly useful in classification and clustering purposes. The method is experimentally validated with data acquired using the Smart Maintenance Living Lab of Flanders Make. It consists of seven identical drive train sub-systems representing a fleet of machines, on which accelerated life time tests of bearings have been performed. Seventy runs overall have been gathered, twenty-one of which are healthy bearing runs, and the other forty-nine are runs to failure with a small initial indent on the bearing inner race.
 Furthermore, the wavelet scattering network feature extraction method is benchmarked to : (1) manually-engineered statistical features such as root mean square, kurtosis, crest factor and peak value, (2) a physics-based bearing fault detection method that is a version of the squared envelope spectrum method of vibration signals extended with several noise reductions and signal enhancement techniques, (3) a convolutional neural network based detection method that autonomously learns useful features and does not require a significant level of knowledge of signal processing techniques and domain expertise..
 This benchmark is done on three industrially relevant performance metrics namely : Penalized accuracy, Data efficiency and Power efficiency.

Integrated scheduling and flexible maintenance in deteriorating multi-state single machine system using a reinforcement learning approach

This paper studies an integrated optimization problem of production scheduling and flexible preventive maintenance (PM) in a multi-state single machine system with deteriorating effects. A flexible PM strategy is proposed to proactively cope with machine failures while ensuring relatively regular PM intervals, which is composed of time-based PM (TBPM) and condition-based PM (CBPM). TBPM is conducted within every flexible time window and CBPM is implemented immediately after the most deteriorated yet still functional state. An illustrative case is presented using the enumeration approach to demonstrate the integration of production scheduling and machine maintenance. Then, Q-learning-based solution framework (QLSF) is further designed with proper state and action sets and reward functions to facilitate the determination of appropriate production scheduling rule under the constraint of the flexible maintenance. Numerical experiments show that the proposed QLSF outperforms the other four state-of-the-art scheduling rules in different scenarios. Moreover, the performance of the proposed flexible PM strategy is also examined and validated in comparison with three candidate maintenance strategies, i.e., run-to-failure corrective maintenance (CM), combination of TBPM and CM, and CBPM. The proposed flexible maintenance and solution approach can enrich the relevant academic knowledge base, and provide managerial insights and guidance in practical production systems.

Estimating maintenance effectiveness of a repairable system under time-based preventive maintenance

Corrective maintenance (CM) is carried out to correct failures while preventive maintenance (PM) is to avert failure. They both play an important role in asset management. Accurately estimating the effectiveness of PM is needed as it has an impact on system health management. Given failure times and maintenance related data, it is possible to estimate the effectiveness of maintenance. This paper proposes PM and CM models based on the different combinations of the type of maintenance carried out, estimates the parameters in those models, simulates their failure intensities and then studies the maintenance effectiveness using maintenance data of industrial equipment.

A generalized three-type lifetime probabilistic models-based hybrid maintenance policy with a practical switcher for time-based preventive maintenance and condition-based maintenance

This paper proposes a generalized hybrid maintenance policy for maintenance scheduling with the help of both time-based maintenance and condition-based maintenance techniques, in which three-type product lifetime probabilistic models are utilized. A dispersion of lifetime estimates-based switcher is proposed to recommend the choice of time-based maintenance or condition-based predictive maintenance schedules in a real-time manner. Within the condition-based predictive maintenance policy, which is a part of the hybrid maintenance policy, a novel weighted average of the maintenance schedules is proposed to recommend maintenance acts, which are estimated based on two types of product lifetime probabilistic models namely type-II and type-III lifetime probabilistic models. The hybrid maintenance policy includes the classical time-based maintenance policy, the traditional condition-based predictive maintenance policy, and the proposed condition-based predictive maintenance policy as special cases. An extensive numerical investigation for the stochastic linear degradation model verifies the effectiveness of the proposed hybrid maintenance policy, highlighting the existence of a special space among time-based maintenance and condition-based predictive maintenance polices, which provides even better maintenance performance than a solely condition-based predictive maintenance policy.

Optimization of on-condition thresholds for a system of degrading components with competing dependent failure processes

An optimization model has been formulated and solved to determine on-condition failure thresholds and inspection intervals for multi-component systems with each component experiencing multiple failure processes due to simultaneous exposure to degradation and shock loads. In this new model, we consider on-condition maintenance optimization for systems of degrading components, which offers cost benefits over time-based preventive maintenance or replace-on-failure policies. For systems of degrading components, this can be a particularly difficult problem because of the dependent degradation and dependent failure times. In previous research, preventive maintenance and periodic inspection models have been considered; however, for systems whose costs due to failure are high, it is prudent to avoid the event of failure, i.e., the components or system should be repaired or replaced the before the failure happens. The determination of optimal on-condition thresholds for all components is effective to avoid failure and to minimize cost. Low on-condition thresholds can be inefficient because they waste component's life, and high on-condition thresholds are risky because the components are prone to costly failure. In this paper, we formulated and solved a new optimization model to determine optimal on-condition thresholds and inspection intervals. In our model, when the system is inspected, all components are inspected at that time. An inspection interval may be optimal for one component, but might be undesirable for another component, so the optimization requires a compromise. The on-condition maintenance optimization model is demonstrated on several examples.

Optimal maintenance strategy selection based on a fuzzy analytical network process

Maintenance is one of the most crucial issues in today’s competitive manufacturing environment. Nowadays, productive methods are used in manufacturing plants to improve operations capabilities, which alternatively change business environmental factors leading to a competitive market. Consequently, selection and illustration of an optimal maintenance strategy play a significant role in achieving these goals. Also, lack of an integrated model is felt, one that consists of available criteria and options, a systematic approach in setting maintenance instructions and robust maintenance decision-making. So, based on the fuzzy analytical network process (FANP) method, which composes criteria and options, a new approach to selecting optimal maintenance strategy is proposed in this article. Here, using multi-attribute decision-making in conjunction with application of fuzzy numbers structure has been regarded as an efficacious method for determining the significance of each criterion and option. The criteria that are considered in the FANP method are organization, safety, administration, staff, and technical requirements. The options (strategies) that are taken into consideration in this study are time-based preventive maintenance, corrective maintenance, condition-based maintenance, reliability-centered maintenance, and predictive maintenance. After implementing the method proposed for a 5-MW powerhouse (a case study undertaken for the purpose of this study), the predictive maintenance strategy was selected. According to expert opinions, it is the administrative and staff requirements that must be highlighted for selecting the best strategies.

Lloyd's Register—Cyber CBM (Condition based maintenance)

Classification survey requirements are anchored on the concept of a periodic, time-based preventive maintenance regime. The underlying principle of the periodic maintenance philosophy is that parts and components of equipment, machinery, systems and structures eventually wear out, thus the safety and reliability of parts and components – indeed the entire vessel – are directly correlated with time and operational age. While development of techniques and technologies have continuously improved surveys and inspection, the periodic maintenance philosophy has been the de-facto standard in marine and offshore industries for many decades. This paper presents Lloyd Register’s Cyber condition based maintenance (CBM) technique: a scalable evaluation of components, equipment, machinery, systems and structures that detects their emergent faults, and characterises their progression to failure. This is an important step in the evolution of in-service classification:moving away from a periodic, time-based maintenance regime into an intelligent, real-time and predictive vessel health maintenance regime.

Optimizing preventive maintenance: a deteriorating system with buffers

Purpose Making decisions on preventive maintenance (PM) policy and buffer sizing, as is often studied, may not result in overall optimization. The purpose of this paper is to propose a joint model that integrates PM and buffer sizing with consideration of quality loss for a degenerating system, which aims to minimize the average operation cost for a finite horizon. The opportunistic maintenance (OM) policy which could increase the output and decrease the cost of the system is also explored. Design/methodology/approach A joint PM and buffer size model considering quality loss is proposed. In this model, the time-based PM and the condition-based PM are taken on the upstream and the downstream machine, respectively. Further, the OM policy based on the theory of constraints (TOC) is also considered. An iterative search algorithm with Monte Carlo is developed to solve the non-linear model. A case study is conducted to illustrate the performance of the proposed PM policies. Findings The superiority of the proposed integrated policies compared with the separate PM policy is demonstrated. Effects of the policies are testified. The advantages of the proposed TOC-based OM policy is highlighted in terms of low-cost and high-output. Originality/value Few studies have been carried out to integrate decisions on PM and buffer size when taking the quality loss into consideration for degenerating systems. Most PM models treat machines equally ignoring the various roles of them. A more comprehensive and integrated model based on TOC is proposed, accompanied by an iterative search algorithm with Monte Carlo for solving it. An OM policy to further improve the performance of system is also presented.

Considering human resource constraints for real joint production and maintenance schedules

This study deals with the problem of scheduling jobs and preventive maintenance (PM) activities in production workshops jointly in order to reduce failure occurrence. PM activities can be planned at fixed periods (time-based PM) or in response to a signal provided from a machine’s captor after anomaly detection (condition-based PM). Maintenance activities’ planning depends on different constraints like human resources, spare parts availability and past operating duration of the machine.Proposed related works evaluate possible arrangements of both production and maintenance activities according to different criteria (makespan (Cmax: completion date of the last operation on the last machine), machine’s failure risk, jobs’ lateness, etc.). However, maintenance activities are frequently considered as production jobs. In fact, maintenance activities present different constraints and cannot be planned in the same manner as production ones. In addition to that, most of current works deal with the time-based maintenance planning even so condition-based maintenance is qualified as more realistic and more economic than the former.This work discusses a new approach to integrate the scheduling of production and maintenance operations. Proposed approach takes explicitly into account human resources availability and skills when updating integrated production and maintenance schedules. It is based on multi-agent systems for modeling the production workshop.

Cost benefits of postponing time-based maintenance under lifetime distribution uncertainty

We consider the problem of scheduling time-based preventive maintenance under uncertainty in the lifetime distribution of a unit, with the understanding that every time a maintenance action is carried out, additional information on the lifetime distribution becomes available. Under such circumstances, typically either point estimates for the unknown parameters are used, or expected costs are minimized taking the uncertainty in the parameters into account. Both approaches, however, ignore that the uncertainty is reduced much faster if preventive maintenance actions are postponed. Although this initially leads to higher costs due to a higher risk of breakdowns, the obtained additional information can be exploited thereafter as it enables better maintenance decisions going forward. We assess the long-term benefits of initially postponing preventive maintenance, and perform a numerical study to identify under what circumstances these benefits are largest. This study is the first to recognize that the choice of a maintenance strategy influences the information that becomes available, and aims to initiate follow-up research in the area of maintenance planning.

Selection of optimum maintenance strategy based on FAHP integrated with GRA–TOPSIS

In today’s competitive environment, industries are required to increase reliability and safety level of their equipment with reasonable cost. Consequently, it is essential to select the appropriate maintenance strategy. In the paper industry, pumping of cooked pulp requires abrasion- and sometimes corrosion-resistant pumps that are able to handle up to 70 % solids content. But the maintenance engineer and supervisor often face failures of the pump. Hence, this article describes the application of multi criteria decision-making (MCDM) technique for the selection of optimum maintenance strategy for pumps used in the paper industry. The proposed hybrid MCDM model comprises fuzzy analytical hierarchy process (FAHP), grey relational analysis (GRA), and technique for order preference by similarity to ideal solution (TOPSIS) technique. The FAHP is used to compute the criteria weights whereas GRA–TOPSIS is used for determining the ranking of alternatives. This study discusses four maintenance strategies: corrective maintenance, predictive maintenance, time-based preventive maintenance, and condition-based maintenance. Four main criteria—safety, cost, added value, and feasibility—have been used to evaluate the optimum maintenance strategy.

OPPORTUNISTIC MAINTENANCE OPTIMIZATION FOR WIND TURBINE SYSTEMS CONSIDERING IMPERFECT MAINTENANCE ACTIONS

Currently corrective maintenance and time-based preventive maintenance strategies are widely used in wind power industry. However, few methods are applied to optimize these strategies. This paper aims to develop opportunistic maintenance approaches for an entire wind farm rather than individual components that most of the existing studies deal with. Furthermore, we consider imperfect actions in the preventive maintenance tasks, which address the issue that preventive maintenance do not always return components to the as-good-as-new status in practice. In this paper we propose three opportunistic maintenance optimization models, where the preventive maintenance is considered as perfect, imperfect and two-level action, respectively. Simulation methods are developed to evaluate the costs of the proposed opportunistic maintenance policies. Numerical examples are provided to demonstrate the advantage of the proposed opportunistic maintenance methods in reducing the maintenance cost. The two-level action method demonstrates to be the most cost-effective in different cost situations, while the imperfect maintenance policy, which is a simpler method, is a close second. The developed methods are expected to bring immediate benefits to wind power industry.

A Joint Model of Production and Preventive Maintenance Plan

We integrated run-based preventive maintenance (PM) into the production plan at the tactical level. The production plan was based on the single level capacitated lot-sizing problem (CLSP). We assumed the system reliability should be kept above certain threshold value to ensure the feasibility of the production plan; the reliability would be affected only by system running. Thus, PM would be triggered by the reliability requirement and the accumulated running time. We established this relationship as a nonlinear integer constraint in the joint model. We also proposed a branch and bound algorithm using CPLEX to solve the model. Finally, a numerical example was given to illustrate the difference between run-based and time-based PM.