Traditional Preventive Maintenance Research Articles

Predictive Maintenance of Machinery with Rotating Parts Using Convolutional Neural Networks

All kinds of vessels consist of dozens of complex machineries with rotating parts and electric motors that operate continuously in harsh environments with excess temperature, humidity, vibration, fatigue, and load. A breakdown or malfunction in one of these machineries can significantly impact a vessel’s operation and safety and, consequently, the safety of the crew and the environment. To maintain operational efficiency and seaworthiness, the shipping industry invests substantial resources in preventive maintenance and repairs. This study presents the economic and technical benefits of predictive maintenance over traditional preventive maintenance and repair by replacement approaches in the maritime domain. By leveraging modern technology and artificial intelligence, we can analyze the operating conditions of machinery by obtaining measurements either from sensors permanently installed on the machinery or by utilizing portable measuring instruments. This facilitates the early identification of potential damage, thereby enabling efficient strategizing for future maintenance and repair endeavors. In this paper, we propose and develop a convolutional neural network that is fed with raw vibration measurements acquired in a laboratory environment from the ball bearings of a motor. Then, we investigate whether the proposed network can accurately detect the functional state of ball bearings and categorize any possible failures present, contributing to improved maintenance practices in the shipping industry.

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.

Non-periodic preventive maintenance policies for complex multi-component products sold with loss-based two-dimensional warranties

Multi-component characteristics of complex industrial equipment, such as complex structures and stochastic dependencies, complicate analyzing the failure characteristics of such products and developing effective strategies to reduce warranty costs. In addition, a substantial customer loss or service interruption owing to the shutdown of these products forces manufacturers to formulate warranty contracts while considering customer loss. From the manufacturer’s perspective, with the aim of reducing the warranty costs while restraining the costs of customer loss, we propose the following two novel types of non-periodic imperfect preventive maintenance (PM) policies: unified and customized. The proposed PM policies simultaneously take into account the effects of customer usage rate heterogeneity and product deterioration on product failure. We characterize the failure relationship between the product and corresponding components based on the reliability block diagram and the Type I failure dependency model, and further construct the decision models for the proposed PM policies. The properties of the decision models are provided for certain special cases and are further used to design an algorithm for a general case. A comparison of the results of the numerical examples reveals the following: (1) Providing PM policies is beneficial for both the warranty providers and customers. The proposed PM policies are better than traditional periodic PM policies when shutdown loss costs are considered. (2) The stochastic dependencies will increase the costs of both the warranty and shutdown loss. It is necessary to take into account the stochastic dependencies of multi-component products when analyzing the warranty costs and formulating relevant decisions. (3) Beyond formulating effective PM policies, the manufacturer can mitigate the warranty cost pressure by adjusting the warranty coverage based on market information, or improving product reliability.

An Optimization Method for Maintenance Resource Allocation in Electrified Railway Catenary Systems

The traditional preventive maintenance plans of electrified railways are mainly formulated according to historical maintenance records and the experience of maintenance technicians. With the increase of the maintenance workload of catenary systems, traditional maintenance decision-making modes are hard to accommodate the demand for the high-efficiency and economical operation of catenary systems. To cater for the on-site maintenance demands, a conceptual framework towards the cost-effective optimization of catenary maintenance is proposed in this paper. The framework includes investigating and sorting the physical quantities in practical maintenance operations, modeling the maintenance operation process, establishing and solving the optimization model of the maintenance operator allocation with multiple optional objectives, and developing the human-machine application system, namely, the resource allocation system (RAS) with both hardware and software for electrified railway catenary maintenance. Field application shows that the implementation of RAS is superior to the current maintenance scheme in maintenance efficiency and cost, which can provide decision support for the scientific formulation of catenary system maintenance scheme, and promote the intelligent development of current maintenance modes.

Degradation prediction and rolling predictive maintenance policy for multi-sensor systems based on two-dimensional self-attention

Traditional preventive maintenance policy gradually failed to guarantee the security and economy of current mechanical systems. This paper proposed a highly efficient rolling predictive maintenance (RPdM) policy for multi-sensor system, to make maintenance decisions. In this policy, to cope with the uncertainty of remaining useful life (RUL) prediction, the degradation process of the system is first divided into four intervals according to the inspection interval and spare parts lead time. Then, the two-dimensional self-attention (TDSA) method, which extract time dimensional and feature dimensional features by parallel computation, is developed to predict the probabilities of system RUL in the four intervals instead of the point of RUL. In addition, the output probabilities of the TDSA method are utilized to calculate the maintenance cost rate of the current inspection point and future point. The maintenance decision including spare parts ordering time and maintenance time is determined by comparing the maintenance cost rate of each inspection point, and the decision is updated at the next inspection point. To verify the effectiveness of the proposed RPdM policy, the C-MAPSS dataset provided by NASA is employed to implement degradation prediction and maintenance decision. Experiment results show that the cost rate of RPdM policy is lower than preventive maintenance policy, and only 27.7% higher than ideal maintenance policy which is impossible in real engineering. Moreover, the impact of different out-of-stock costs and corrective costs are explored and shows the good robustness of the RPdM policy.

SCADA-Compatible and Scaleable Visualization Tool for Corrosion Monitoring of Offshore Wind Turbine Structures

The exploitation of offshore windfarms (WFs) goes hand in hand with large capital expenditures (CAPEX) and operational expenditures (OPEX), as these mechanical installations operate continuously for multiple decades in harsh, saline conditions. OPEX can account for up to 30% of the levelised cost of energy (LCoE) for a deployed offshore wind farm. To maintain the cost-competitiveness of deployed offshore WFs versus other renewable energy sources, their LCoE has to be kept in check, both by minimising the OPEX and optimising the offshore wind energy production. As corrosion, in particular uniform corrosion, is a major cause of failure of offshore wind turbine structures, there is an urgent need for corrosion management systems for deployed offshore wind turbine structures (WTs). Despite the fact that initial corrosion protection solutions are already integrated on some critical structural components such as WT towers, WT transition pieces or WT sub-structure (fixed or floating platforms), these components can still be harshly damaged by the corrosive environmental offshore conditions. The traditional preventive maintenance strategy, in which regular manual inspections by experts are necessary, is widely implemented nowadays in wind farm applications. Unfortunately, for such challenging operating environments, regular human inspections have a significant cost, which eventually increase the OPEX. To minimise the OPEX, remote corrosion monitoring solutions combined with supporting software (SW) tools are thus necessary. This paper focuses on the development of a software (SW) tool for the visualisation of corrosion measurement data. To this end, criteria for efficient structural corrosion analysis were identified, namely a scaleable, SCADA-compatible, secure, web accessible tool that can visualise 3D relationships. In order to be effective, the SW tool requires a tight integration with decision support tools. This paper provides three insights: Firstly, through a literature study and non-exhaustive market study, it is shown that a combined visualisation and decision SW tool is currently non-existing in the market. This gap motivates a need for the development of a custom SW tool. Secondly, the capabilities of the developed custom software tool, consisting of a backend layer and visualisation browser designed for this task are demonstrated and discussed in this paper. This indicates that a SCADA-compatible visualisation software tool is possible, and can be a major stepping stone towards a semi-automated decision support toolchain for offshore wind turbine corrosion monitoring.

Opportunistic preventive maintenance scheduling for multi-unit reusable rocket engine system based on the variable maintenance task window method

The reusable rocket engine (RRE), recognized as future propulsion system for space transportation, is composed of multiple components of which each one plays an essential role for the whole system. In rocket launches, these components are impaired by severe service conditions while the maintenance actions can only be executed in mission intervals. To program a reasonable maintenance scheme for the RRE system, an opportunistic preventive maintenance (PM) scheduling strategy is proposed: at the component-level, we evaluate the PM intervals dynamically by minimizing the maintenance cost rate over a life cycle under the reliability constraint where two imperfect maintenance actions: maintenance repair and replacement, are taken into account; at the system-level, a variable maintenance task window (VMTW) method is applied to dynamically rearrange maintenance actions when the maintenance opportunities arise. The novel strategy can effectively reduce the PM cost and avoid unnecessary depot maintenance. Its efficiency is validated by a comparison with traditional optimal preventive maintenance schedule derived from the genetic algorithm.

Role of sensors in the paradigm of industry 4.0 and IIoT

The purpose of this article is to review new trends in monitoring the condition of oil on all factory area processes. New solutions are being introduced into this industry with new advantages in the development of artificial intelligence, as well as machine learning and sensor technologies, which are applicable for data-based maintenance. They are called predictive maintenance. This paradigm is going to replace the old one. It changes the traditional routine preventive maintenance scheme and provides a deep understanding of the equipment performance [1]. Monitoring and checkout of conditions are necessary to maintain in a real-time environment because on-line control of equipment status can put down an operating cost, by eliminating the need for equipment outage for everyday diagnostics. The analysis based on oil samples is an effective tribotechnical systems approach for early diagnosis of failures, as it contains valuable information about the process of degradation of oil and the state of tribotechnical pairs [2]. But there are some problems with this method. The first is the way of oil sampling. There are lots of mistakes that may be made during the oil sampling process, and they can affect the results. The second is a delivery to laboratory which complicates the diagnostic process. That's why we cannot say this approach is an on-line method of diagnostics. For the better prognosis of pending machinery failure one needs to know a real-time correlation between size, shapes, and concentration of wear debris parts [3].

Optimization of equal-cycle maintenance strategy considering imperfect preventive maintenance

Preventive maintenance is an important means to extend equipment life and improve equipment reliability. Traditional preventive maintenance decision-making is often based on components or the entire system, the granularity is too large and the decision-making is not accurate enough. The meta-action unit is more refined than the component or system, so the maintenance decision-making based on the meta-action unit is more accurate. Therefore, this paper takes the meta-action unit as the research carrier, considers the imperfect preventive maintenance, based on the hybrid hazard rate model, established the imperfect preventive maintenance optimization model of the meta-action unit, and the optimization solution algorithm was given for the maintenance strategy. Finally, through numerical analysis, the validity of the model is verified, and the influence of different maintenance costs on the optimal maintenance strategy and optimal maintenance cost rate is analyzed.

An opportunistic maintenance strategy for wind turbines

Maintenance strategies aim at reduction of the operation and maintenance cost of wind turbines. An opportunistic maintenance strategy offers cost advantages over the traditional preventive replacement maintenance strategy by considering cost dependency among maintenance activities. A new opportunistic maintenance strategy of wind turbines is proposed here. First, the proposed strategy includes two sets of reliability thresholds to handle subassemblies those are in operational and failed wind turbines. Using the reliability thresholds, ten maintenance modes and their service conditions are presented. Second, the Weibull distribution with an age reduction factor is introduced to describe reliability variation of subassemblies under imperfect maintenance. Third, the maintenance cost in each maintenance mode is expressed by a reliability variation-based function that is dependent on the stage of maintenance activities. Fourth, due to the randomness of failures, the expected total maintenance cost is determined with Monte Carlo simulation. The proposed approach has been validated with the data provided by a wind farm located in Northern China.

Hierarchical multi-agent predictive maintenance scheduling for trains using price-based approach

While the progress of predictive maintenance has been rising in various application fields and several feasibility studies and prototypes have been developed, the operational implementation of predictive maintenance requires a more flexible and adaptive scheduling of the predicted maintenance interventions. Contrary to the traditional preventive maintenance tasks that are typically known long in advance, predictive maintenance may require very short-term schedule changes which are also affecting the operation. Railway rolling stock has some special requirements in terms of time table adherence and vehicle scheduling and routing that make the operational implementation of predictive maintenance particularly challenging. In this paper, we propose a hierarchical multi-agent framework for the predictive maintenance scheduling of passenger railway wagons. Besides fulfilling the requirement of scheduling the maintenance of a wagon before it fails (and potentially causes delays), also the passenger demand must be satisfied and the trains must be accordingly assigned to different routes following the time table. We propose a hierarchical distributed learning algorithm using dual decomposition and mechanism design approach. The proposed framework enables to preserve local preferences and particularities and to avoid high computational cost. In the proposed method, first, we decompose the centralized problem using the dual decomposition method, and handle the passenger demand fulfillment constraints by Lagrange multiplier (“shadow price”). Furthermore, the wagons with the private information on the system health need to perform the maintenance before their failure time (with a predicted remaining useful lifetime (RUL) and the corresponding uncertainty). To achieve this aim, we propose a mechanism design approach to align the wagons’ objective function to the aim of the central system using an incentive signal. The incentive signal creates a non-cooperative game among the wagons. We prove that the Nash Equilibrium (NE) of this game is the optimal solution of the predictive maintenance scheduling. The effectiveness of the proposed method is demonstrated on a case study of a small railway network.

Predictive Maintenance as a Driver for Corporate Sustainability: Evidence from a Public-Private Co-Financed R&D Project

Can public–private research favor sustainable economic growth? Can innovation in terms of predictive maintenance (a recently consolidated evolution compared to the more traditional final and preventive maintenance) favor sustainable business balance? Drawing on the Quadruple Helix model and adopting the users’ (fourth helix) perspective, this paper seeks to provide initial answers to these two questions. Following an exploratory approach, it applies case study methodology to present the research outcomes of the D.I.A.S.E.I. Project, a co-financed research and development (R&D) project. Using a mixed-methods approach, narrative and quantitative, the study highlights that investing in predictive maintenance allows companies to reach better profitability ratios and sustainable value indexes. This is possible because of the improved quality of the provided services, higher revenues and the reduction of extraordinary maintenance costs. Furthermore, if companies support investment in predictive maintenance through correct financial decisions, they may create value over time and favor sustainable business balance. The work is the first empirical investigation, which should encourage further explorations. There are numerous implications, including reference to the co-creation of value, the effects on decision support systems and sustainable competitive advantage.

An approach to a maintenance plan for a turbine of hydroelectric power plant. Optimisation based in RCM and FMECA analysis

<p class="JARTEAbstract">The generation of electric energy is a continuous process of great strategic importance in the development of countries. The evolution in the last decades of different environmental control protocols at a global level has influenced the rise of renewable energies with low impact on the environment. These reasons make it important that this process has an effective maintenance management system that guarantees adequate reliability, availability and useful life of its assets. This article describes the implementation of the optimization of the traditional preventive maintenance that is carried out in regular temporary intervals with unavailability of the hydroelectric group, to be able to take advantage of the data base of operation of the group (failure modes, criticality, application of maintenance strategies to condition and maintenance 4.0, etc…).</p>

On optimal life extension for degrading systems

We consider life extension models for critical, complex systems with relatively long lifecycles. In contrast to traditional optimal preventive maintenance that usually minimizes the corresponding long run cost rate, a finite number of preventive maintenances are performed to increase the expected lifetime of these systems in an optimal way. The cases of periodic and aperiodic preventive maintenance actions are discussed. The proposed novel approach to life extension allows for simple sensitivity analysis with respect to parameters of the model. The obtained optimal solutions can result in a noticeable increase in the useful life of complex systems. Our findings are illustrated by numerical examples.

Enhanced POM strategy and its application to wind turbines

Deficiencies exist during applying the traditional preventive opportunistic maintenance (POM) strategy to wind turbines. For example, its rough cost analysis only provides a total instead of the staged cost of once maintenance, which cannot give precise cost composition. Setting identical thresholds for all subassemblies reduces the potential of cost saving etc. An enhanced POM strategy for wind turbines is presented in this study with the consideration of maintenance modes including no maintenance, imperfect maintenance and replacement. The reliability variations of subassemblies under different maintenance modes are described by Weibull distribution with an age reduction factor. On the basis of reliability variations, a formula is proposed to uniformly compute maintenance cost under each maintenance mode via subdividing the cost in the light of diverse maintenance stages. An integrated objective function minimising the maintenance cost is established and solved, where reliability thresholds of each subassembly are separately set as optimisation variables. The enhanced POM strategy is applied to a wind farm in North China, verifying its effectiveness.

Dynamic preventive maintenance strategy for MMC considering multi-term thermal cycles

With the integration of large-scale renewable energy, preventive maintenance (PM) of modular multi-level converter (MMC) has become a critical technique for guaranteeing the secure operation of long-distance transmission systems. However, existing PM strategies result in errors and extra costs because of ignoring the time-varying operational conditions (e.g. wind speed, air temperature and power current alternating) and constant redundant sub-modules (SM). To reduce PM-related costs with an acceptable level of reliability considering different operational conditions, a dynamical PM strategy is proposed. Compared with the traditional PM strategy, adjustable redundant SMs in every operational stage and PM interval are added to dynamically coordinate reliability and PM-related costs of MMC. The reliability model of MMC proposed in this paper considers the impact of different operational conditions based on multi-term thermal cycles of components and operating characteristics of MMC. Monte Carlo method is used to estimate the reliability of MMC, and the genetic algorithm is applied to optimize the PM interval and the number of redundant SM in every operational stage. Case studies show that the PM results vary with different operational conditions. Besides, compared with existing strategies, the proposed dynamic PM strategy could reduce the PM-related costs with an acceptable level of reliability by dynamically adjusting the PM interval and redundancy on every stage.

Criticality-based reliability-centered maintenance for healthcare

PurposeMedical technologies and assets are one of the main drivers of increasing healthcare cost. The rising number and complexity of medical equipment have forced hospitals to set up and regulate medical equipment management programs to ensure critical devices are safe and reliable. The purpose of this paper is to gain insights into maintenance management-related activities for medical equipment. The paper proposes applying a tailored reliability-centered maintenance (RCM) approach for maintenance activities selection for medical equipment. Such approach will support assets management teams in enhancing operation, decrease risk and cost, and ultimately improve health of patients served by these equipment.Design/methodology/approachThe traditional RCM approach will be used with a focus on criticality reduction. By criticality, the authors refer to the severity of failures and occurrence. The proposed method relies on the use of reliability growth analysis for opportunity identification followed by a thorough failure mode and effect analysis to investigate major failure modes and propose ways to reduce criticality. The effectiveness of the proposed method will be demonstrated using a case of one of the leading obstetric and gynecological hospitals in United Arab Emirates and in the Gulf Cooperation Council region.FindingsThe case examines the relationship between the current practice of planned preventive maintenance and the failure rates of the equipment during its life span. Although a rigorous preventive maintenance program is implemented in the hospital under study, some critical equipment show an increasing failure rates. The analysis highlights the inability of traditional time-driven preventive maintenance alone in preventing failures. Thus, a systematic RCM approach focused on criticality is more beneficial and more time and cost effective than traditional time-driven preventive maintenance practices.Practical implicationsThe study highlights the need for utilizing RCM approach with criticality as the most important prioritization criterion in healthcare. A proper RCM implementation will decrease criticality and minimize the risk of failure, accidents and possible loss of life. In addition to that, it will increase the availability of equipment, and reduce cost and time.Originality/valueThis paper proposes a maintenance methodology that can help healthcare management to improve availability and decrease the risk of critical medical equipment failures. Current practices in healthcare facilities have difficulty identifying the optimal maintenance strategy. Literature focused on medical maintenance approach selection is rather limited, and this paper will help in this discussion. In addition to that, the Association for the Advancement of Medical Instrumentation supports the initiative of adopting RCM on a large scale in healthcare. Therefore, this paper address the gap in the literature for medical equipment maintenance and the work is in line with the recommendation of leading healthcare association. The paper also presents statistical review of the total number of received maintenance work orders during one full year in the hospital under study. The analysis supports the need for more research to examine current practice and propose more effective maintenance approaches.

An optimal maintenance strategy for multi-state systems based on a system linear integral equation and dynamic programming

An optimal preventive maintenance strategy for multi-state systems based on an integral equation and dynamic programming is described herein. Unlike traditional preventive maintenance strategies, this maintenance strategy is formulated using an integral equation, which can capture the system dynamics and avoid the curse of dimensionality arising from complex semi-Markov processes. The linear integral equation of the system is constructed based on the system kernel. A numerical technique is applied to solve this integral equation and obtain all of the mean elapsed times from each reliable state to each unreliable state. An analytical approach to the optimal preventive maintenance strategy is proposed that maximizes the expected operational time of the system subject to the total maintenance budget based on dynamic programming in which both backward and forward search techniques are used to search for the local optimal solution. Finally, numerical examples concerning two different scales of systems are presented to demonstrate the performance of the strategy in terms of accuracy and efficiency. Moreover a sensitivity analysis is provided to evaluate the robustness of the proposed strategy.

Cost-oriented predictive maintenance based on mission reliability state for cyber manufacturing systems

With the advent of Industry 4.0, maintenance strategy faces new demands to avoid the hysteresis of the conventional passive maintenance mode and the non-feasibility of the periodic preventive maintenance model. In view of the inherent polymorphism of manufacturing systems and with the objective of maximizing benefits, a novel cost-oriented predictive maintenance based on mission reliability state for manufacturing systems is proposed. First, the cyber-physical system is adopted to organize and analyze big data in the operational process of manufacturing systems in terms of predictive analytics in cyber manufacturing environment. Second, a new connotation of mission reliability is defined based on the big operational data to comprehensively characterize the dynamic state of the equipment health states and the qualified degree of the production task. Third, the predictive maintenance mode based on mission reliability state is quantified by the comprehensive cost, and the relationship between mission reliability and cost is established. Thereafter, cost-oriented dynamic predictive maintenance strategy is proposed. Finally, a case study on the maintenance decision-making problem of a cylinder head manufacturing system is presented. The final result shows that the comprehensive cost can be further reduced by the proposed method relative to the traditional periodic preventive maintenance strategy.

An optimisation research on two-dimensional age-replacement interval of two-dimensional products

For the failures of two-dimensional products affected by the calendar time and usage time, traditional one-dimensional preventive maintenance cannot satisfy the actual maintenance demand of two-dimensional products. Therefore, this paper proposes two-dimensional preventive maintenance strategy for two-dimensional products and makes decision for two-dimensional products through calendar time and usage time. Firstly, we establish failure rate function expression of two-dimensional products and analyse the process of two-dimensional preventive maintenance in detail, after proposing a cost model and availability model from the view of economy and mission in finite span. According to the cost and availability model, through examples, we derive the optimal two-dimensional age-replacement interval by numerical algorithm and particle swarm algorithm, and verify the model's applicability and validity through contrast and analysis. Lastly, we give a brief discussion of future research area about two-dimensional maintenance.