Increasing market competition and growing mechanical structural complexity have urged the application of complex systems comprising multi-component machines. Ensuring the economical operation and maintenance (O&M) for such systems is paramount in industry. To achieve this, the component maintenance policy should cover both the machine-level disassembly sequence among critical components and the system-level configuration setting connecting individual machines. However, integrating such hierarchical structural dependencies into maintenance decision-making poses a formidable challenge and remains unexplored in existing literature. Facing this key issue, our study presents a component-targeting opportunistic maintenance policy considering hierarchical structural dependencies (COMP-HSD) to provide real-time cost-effective maintenance schemes. Aiming at deriving the optimal component maintenance group at each maintenance opportunity, this policy builds a decision-making process including triple-level analyzing framework to comprehensively involve the component degradation and the structural dependencies analyses of machine level and system level. Specifically, sequential component-level predictive maintenance (PdM) intervals are initially optimized according to individual health conditions. Then, a maintenance cost savings model is developed to assess the economy of adjusting PdM actions for each component at the current maintenance opportunity, accounting for the impacts of hierarchical structural dependencies. Furthermore, we incorporate maintenance resource limitation into the decision-making procedure to enhance the adaptability to industrial practice. Numerical examples extracted from a partner engine cylinder head manufacturing enterprise certify the significant cost reduction of this COMP-HSD methodology.Note to Practitioners—This work is motivated by the critical problem of calculating a cost-effective component-targeting maintenance policy for a complex production system with multi-component machines. According to the real O&M management of a manufacturing company, it is intractable to reasonably decide when to maintain each critical component in multiple CNC machine tools of a production system, which restricts the maintenance planning economy. This is due to the difficulty of evaluating the impacts brought by hierarchical structural dependencies (i.e., machine-level disassembly sequence and system-level series configuration). Existing approaches are limited to be directly applied to practice since they either focus solely on individual multi-component machine or assume that each machine in a multi-machine system only contains one component, neglecting the holistic evaluation. Besides, the maintenance resource restriction is rarely considered in the literature. To fill this gap, we present the COMP-HSD policy, which simultaneously considers the individual component degradations, systemic maintenance opportunities and complex structural dependencies analysis in the maintenance decision-making process. Referring to this policy, the manufacturers could determine the component maintenance timetable to conduct timely PdM actions with lower cost. Compared with the traditional maintenance policy, our study greatly reduces the total cost, and also offers extending applications such as maintenance tool preparation and spare part ordering according to the component maintenance scheme.

With the demand for flexible manufacturing of curved ship blocks, a bracket system with fixtures has emerged for positioning curved compliant parts. However, current experience-based fixturing schemes struggle to determine the appropriate amount and layout of flexible fixtures for compliant parts from various curved ship blocks. Thus, it is urgent to design fixturing schemes for corresponding parts to mitigate the impact of redundant fixtures and suboptimal layouts, ultimately reducing the total cost. In this paper, a novel fixturing scheme design policy (FSDP) is proposed for curved compliant parts. Firstly, a finite element equation-based method is built to calculate the dimensional deformation of parts under different fixturing schemes. Secondly, a fixturing scheme optimization model is formulated considering fixture amount and layout as the decision variables. The objective is to minimize the overall cost, which includes the fixture cost, quality loss, and penalty cost for violating the accuracy requirement. Finally, three curved shell plates with different aspect ratios from the shipyard are used to demonstrate the effectiveness and expandability of the FSDP. The results indicate that compared with the current fixed fixture layouts, the proposed FSDP can significantly reduce the fixture cost and quality loss by 70-85% while controlling the part deformation within the accuracy requirement. It can help shipyards adaptively optimize the flexible fixturing schemes under different compliant parts during ship construction. Note to Practitioners—This work is motivated by designing a fixturing scheme for various curved compliant parts in the shipbuilding industry. Traditional fixturing schemes result in redundant costs and poor geometrical quality of curved shell panels. The large and uneven deformation of parts will hinder the welding of subsequent stiffening members with manual trimming, which is time-consuming and labor-intensive. Thus, it is not suitable for a wide range of diverse compliant parts in the flexible construction transformation of curved blocks. In this paper, we introduce the FSDP to find optimal fixturing schemes for different curved compliant parts. Compared with the current practice, the FSDP significantly reduces the total cost. Besides, the geometrical quality of the parts is improved. The proposed policy enables practitioners to implement the optimal fixturing schemes instead of relying on experience-based current practice in the flexible construction of curved blocks.

Accurate degradation modeling is a prerequisite for reliable prognostics. When historical data are scarce and operating conditions vary over time, conventional approaches struggle to balance accuracy, adaptability, and interpretability, and lack robustness to changing environments, especially when encoding the effects of operating conditions directly in the model. To overcome these limitations, this paper proposes a novel generalized degradation model based on a semi-physics-informed neural stochastic differential equation, where neural stochastic differential equation (NSDE) is utilized to describe the degradation dynamics. In contrast, the effect of operating conditions on degradation rate is injected in a plug-and-play prior without being locked into the NSDE structure. A variational inference-based generative training procedure jointly estimates the parameters of the NSDE and the prior, mitigating the adverse effect of imperfect physics and requiring only modest historical data. Then, an approximate closed-form distribution for the remaining useful lifetime (RUL) is derived. Thus, an approach for RUL prognostics of in-service products under dynamic operating conditions is established, leveraging the knowledge of degradation from historical data. Comprehensive studies on simulated and battery degradation data demonstrate the robustness and effectiveness of the proposed model.

Data-driven feature extraction for machine health monitoring has garnered significant attention, yet two key limitations remain unaddressed: lack of interpretability and the need for extensive historical fault data. To overcome these problems, an online two-dimensional optimization framework is proposed that enables interpretable time-frequency feature extraction and health index (HI) construction without requiring faulty samples for model training. Our approach introduces a convex hull-based closest point optimization model for estimating time-frequency instances and learning interpretable time-frequency features. By leveraging a small set of baseline vibration samples and recent online data, rapid fault diagnosis can be achieved based on optimized interpretable time-frequency features. This method also facilitates long-term degradation tracking by constructing and updating an HI from collected time-frequency spectrograms. Once machine faults appear, updated time-frequency features can show apparent and interpretable fault signatures for prompt fault alarming. Moreover, the proposed framework allows continuous HI updates for incipient fault detection and degradation tracking. The proposed framework is validated by using two run-to-failure datasets and ablation experiments are conducted to demonstrate its superiority.

Future reconfigurable manufacturing systems (RMSs) can dynamically modify the system structures to achieve personalization, customization, and consumer-maker co-creation. The advanced Internet of Things (IoT) integrating deep learning and intelligent maintenance is critical for ensuring the operation and maintenance of RMS. On the one hand, a multi-head neural network is developed under the variability of individual machine degradations for deriving machine-level prognostics. It learns degradation features with superior generalization performance by simultaneously fitting multiple candidate distributions and updates remaining useful lifetime (RUL) distributions from diversified distribution ensembles. On the other hand, a flexible opportunistic maintenance policy is proposed to optimize the dynamic maintenance scheduling for the multi-phase RMS by utilizing real-time updated RUL distributions. Meanwhile, considering the unique attributes of RMSs, maintenance opportunities that arise from the in-situ machine PdM, system structure, and sequential reconstruction time are fully utilized. This IoT-enabled prognostics & opportunistic maintenance (POM) framework can achieve a bi-level interactive mechanism and inner/outer loops to reduce decision-making complexity and maintenance costs for future reconfigurable manufacturing. Numerical experiments demonstrate that this framework has superior predictive performance and significant cost savings to empower intelligent maintenance.

Nowadays, with the wide adoption of Industry 4.0 and service-oriented manufacturing, leased manufacturing systems are facing new informatics challenges in the efficiency, scalability, and profitability of operation and maintenance. On the one hand, the centralized cloud needs to serve multiple high-value leased machines located remotely far from the lessor in real time. On the other hand, massive field data will be generated locally through proliferated Internet of Things (IoT) devices. The traditional cloud frameworks are limited by the computational capability and the scheduling complexity. To handle the above problems, we propose an edge-based framework that integrates real-time prognosis with opportunistic maintenance for the leased manufacturing system. Firstly, we extend cloud computing capabilities with edge computing resources for supporting condition prognosis and maintenance scheduling. Then, the edge-level prognosis will update time-to-failure (TTF) distributions and evaluate dynamic predictive maintenance intervals (DPMIs) for each leased machine in real time. By identifying maintenance opportunities from edge sides, the cloud-level PM scheduling will be dynamically executed for the whole leased system. A real case study of a leased engine crankshaft system deployed the proposed edge-based framework is conducted. Numerical results significantly demonstrate the computing efficiency, communication improvement, and economic advantage of our edge-based framework compared with the cloud-based solutions for the leased manufacturing system.Note to Practitioners—This work is motivated by improving the efficiency, scalability, and profitability of prognosis&health management (PHM) in the leased manufacturing system through edge computing. Currently, most existing PHM frameworks are based on the cloud to monitor the health condition and schedule the PM for a single machine which cannot be directly applied to multi-unit leased manufacturing systems. Meanwhile, due to the limited cloud capability and proliferated IoT devices, the timely prognosis updating and optimal maintenance scheduling are often constrained under the product-service paradigm. It is essential to coordinate the field-level resources, edge-level prognosis, and cloud-level PM in a hierarchical framework for the multi-unit leased manufacturing system to promote computational performance and maximize leasing profits.To fill this research gap, we develop an edge-based framework coupling with the real-time prognosis based leasing profit optimization (RP-LPO) strategy to achieve PHM for the whole system, as well as the optimal PM scheduling. Compared with the cloud-based frameworks and conventional maintenance policy, our edge-based framework with RP-LPO strategy greatly reduces the data transmission, the space storage, the synchronization time and maximizes leasing profits.

Machine life cycle performance assessment is of great significance to use a health index to inform the time of incipient fault initiation in a normal stage and realize fault identification and fault trending in a performance degradation stage. However, most existing works consider using unexplainable model parameters and historical data to build models and infer their off-line parameters for machine life cycle performance assessment. To overcome these limitations, an online piecewise convex-optimization interpretable weight learning framework without needing any historical abnormal and faulty data is proposed in this article to generate a piecewise health index to practically implement machine life cycle performance assessment. Firstly, based on a separation criterion, the first submodel in the proposed framework is built to detect the time of incipient fault initiation. Here, the piecewise health index generated by the first submodel is continuously updated by on-line monitoring data to timely detect the occurrence of any abnormal health conditions. Secondly, once the time of incipient fault initiation is informed, online updated model weights are highly correlated with fault characteristic frequencies and informative frequency bands for immediate fault identification. Simultaneously, the second submodel integrated with monotonicity and fitness properties in the proposed framework is triggered to generate the piecewise health index to realize overall monotonic fault trending. The significance of this article is that only online monitoring data are used to continuously update interpretable model weights as fault frequencies and informative frequency bands to generate the proposed piecewise health index so as to practically realize machine life cycle performance assessment. Two run-to-failure cases are studied to show the effectiveness and superiority of the proposed framework.

Machine degradation modeling is an enabling methodology to use monitoring data to evaluate machine health conditions. Fault detection needs to confirm whether there exists an incipient fault in a machine while machine diagnostics require knowing where the fault occurs and checking a specific fault type. In this article, an anchor discrimination learning model (ADLM) for physics-informed machine degradation modeling is innovatively proposed to find a projection direction that minimizes a distance between an anchor and samples with a same label of the anchor, and simultaneously maximizes a distance between the anchor and samples with a different label of the anchor. Subsequently, the ADLM is mathematically derived and formulated as a generalized Rayleigh quotient. Instead of using hand-crafted features, this article directly inputs normal and abnormal raw square envelope spectra into the ADLM for machine degradation modeling and the responses of the ADLM, namely an optimal direction, can automatically localize informative frequency components for immediate machine fault detection and diagnostics. Unlike most data-driven methodologies, the proposed methodology is physics-informed and its outputs are capable of indicating physical fault frequencies and their relevant frequency bands for quick fault detection and diagnostics. Two experimental studies are conducted to verify the feasibility of the proposed ADLM for machine degradation modeling.

Nowadays, with the rapidly growing size of random orders and machine scales, cloud manufacturing (CMfg) is suffering from complex difficulties in scheduling enormous production services in real time. Especially when coupled with preventive maintenance (PM) scheduling for massive distributed machines, most current methods fail to promise agility and effectiveness simultaneously under this service-oriented paradigm. Therefore, this article aims to propose a collaborative scheduling algorithm to support real-time production and opportunistic maintenance (OM) in the highly dynamic CMfg environment. First, a novel collaborative scheduling model that combines real-time production and PM decision-making is formulated. Then, to address significant complexity in production scheduling, a dynamic suborder-oriented scheduling (DSS) strategy is designed to utilize frequent triggered events for allocating optimal manufacturing services in real time. Then, for improving computational capability in PM decision-making, a production-driven PM policy with two stage adjustment (TSA) is developed to adopt suborder changeovers as PM opportunities to formulate group PM decisions for massive machines. Finally, a collaborative DSS-TSA algorithm is designed to cyclically coordinate the real-time production scheduling with OM decision-making to avoid repetitively solving large-scale joint problems. Our collaborative scheduling algorithm is verified in a real CMfg scenario for gas turbine blades. The results prove the significant improvement in production timeliness, machine availability, and system profitability.

Nowadays, the rise of operating leases has promoted the popularity of two-stage leasing in the aviation industry. That is, after the first lease expires, the aircraft will be subleased after system reconditioning. Due to the differentiated system reliability and customer requirements at two leasing stages, this leasing mode has posed new challenges to the two-stage maintenance service design. Especially, for service-oriented aviation manufacturers, the competition with independent maintenance providers (IMPs), the influence of component sales, and the maintenance capacity shortage have aggravated this difficulty. To address these challenges, this article proposes a novel approach for manufacturers to determine the optimal maintenance service strategy for two-stage leased systems under capacity constraints. Particularly, the reconditioning action after the first lease and the capacity investment are introduced into the collaborative strategy. To ensure both cost efficiency and competitiveness of the proposed strategy, a maintenance service competition mechanism is established considering two-stage differentiated customer utilities. Then, the time-varying maintenance demand accumulated from the two stages is dynamically predicted. Further, aiming at maximizing the profit from both maintenance services and component selling, two patterns addressing capacity shortage: independent investment and cooperation with an IMP are modeled. Finally, case studies validate the effectiveness of the proposed models and provide important managerial insights into pattern selection.

Infrared thermography is increasingly exploited to track mechanical degradation in a noncontact manner, readily available for further prognostics. Recently, wavelet networks have coalesced deep learning and wavelet transform (WT), expected to achieve data-driven and interpretable prognostics. However, traditional wavelet networks neither possess enough adaptability to extract degradation-related features nor sufficiently fuse learned wavelet coefficients. Thus, this article presents a spatiotemporal dynamic convolution-based wavelet network to handle the above difficulties in industry. First, a spatiotemporal dynamic convolution layer is presented to flexibly modulate kernels according to input samples and the multidimensional kernel space. Second, a learnable lifting scheme structure is constructed to perform signal-adapted WT while incorporating crucial properties to link the optimization of lifting filters and degradation-related feature learning. Finally, a bilinear feature fusion is implemented to jointly represent extracted wavelet energy across decomposition levels, facilitating synergistic optimization. The superiority of the proposed method is illustrated through infrared degradation image datasets.

Infrared thermography captures real-time degradation temperature information, facilitating noncontact machine health monitoring. However, the inherent multiscale characteristics and spatiotemporal degradation discrepancy in infrared images pose a challenge in learning discriminative degradation features and adaptive prognostic analytics. This article presents a sparse hierarchical parallel residual networks ensemble (SHPRNE) method to tackle this challenge. First, the hierarchical parallel residual network (HPRN) leverages parallel multiscale kernels to capture complementary degradation patterns separately and embeds a hierarchical residual connection procedure to facilitate the interactivity between coarse-to-fine level features. Moreover, SHPRNE develops a sparse ensemble algorithm integrated with a synergy of network pruning and local minima perpetuation to derive diverse HPRNs while alleviating the parameter storage budget. Pruned HPRNs with varying sparsity and local minima are further integrated into an ensemble learner with higher generalization. Case studies on two infrared image datasets are conducted to demonstrate the effectiveness and superiority of the proposed method.

Degradation modeling and prognostics serve as the basis for system health management. Recently, various sensors provide plentiful monitoring data that can reflect the system status. A multitude of feature fusion techniques based on multisensor data have been proposed to generate a composite health index (HI) for prognostics, which can represent the underlying degradation mechanism. Most existing methods have used linear fusion models and neglected the practical requirements for HI construction, which are insufficient to reveal the nonlinear relations among features and difficult to obtain accurate HIs for complicated systems. This study proposes a novel feature fusion-based HI construction method with deep learning and multiobjective optimization. Multiple degradation features are fused by a deep neutral network (DNN). Several desired properties that the HIs should have for prognostics are adopted to formulate the objective functions of DNN training. To balance the spatial complexity and performance of the fusion model, a multiobjective optimization model is generated for training the DNN. Then, a generalized nonlinear Wiener process model is used to predict the remaining useful life with the resulted HIs. Finally, two cases are analyzed to illustrate the effectiveness and robustness of the proposed method.

Performance degradation modeling is promising to construct an advanced health index (HI). Currently, domain knowledge including monotonicity, trendabilty, and identifiability has been widely recognized as desirable properties to evaluate the suitability of an HI. Nevertheless, a quantitative criterion to evaluate the suitability of the curvature of an HI is still lacked, which can be used to describe different machine degradation rates. In this article, a new property of an HI named “shapeness” is proposed and it expects that an HI would have a monotonic degradation rate when the HI has a monotonic curve. Subsequently, a mathematical quantitative formulation and optimization model of the shapeness is put forward to be integrated with other desirable properties, which can formulate a composite optimization degradation model. Here, the main variables of the proposed optimization degradation model are weights that are used to fuse spectra lines of vibration signals so that the sum of weighted spectra lines can be used to form a generalized HI. Besides, it is proved that the proposed optimization model is a convex optimization problem so that a global optimal solution is uniquely guaranteed. Two run-to-failure case studies are conducted and some comparisons with state-of-the-art models show that the proposed HI has better performance.

Convex hulls based maximum margin classification has been widely studied for machine fault diagnosis, while its exploration for machine degradation modeling is seldom reported. In this study, a sparse and flexible convex-hull representation for machine degradation modeling is proposed to realize degradation trajectory tracking and fault diagnosis at a same time. First, considering using vibration data as health monitoring signals, globally normal and abnormal spectral lines can be obtained based on the fast Fourier transform and they are, respectively, characterized as individually flexible convex hulls. Subsequently, a sparse and flexible convex-hull representation degradation model is constructed by simultaneously finding the closest pair of samples and its sparse regularization between normal and abnormal convex hulls. Finally, a health indicator can be developed for early fault detection and degradation trajectory tracking during a machine life cycle. Meanwhile, quick fault diagnosis can be realized by finding a difference between the optimal closest samples in a normal convex hull and an abnormal convex hull. Two experimental cases are used to show the effectiveness and superiority of the proposed model to recent existing works.

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.Note to Practitioners—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.

Prognostics and health management aims to use on-line sensor data to monitor and predict current and future health conditions of degraded systems and components. Nowadays, constructing composite health indices for characterizing current health conditions of a system from multiple degradation-based sensor data has attracted much attention. These kinds of “data-level” models show strong ability to provide a better degradation characterization of a degraded system than a model solely depending on data from an individual sensor. Although numerous efforts have been made to propose “data-level” fusion methodologies for process data, such as temperature, pressure, speeds, etc., little research has targeted “data-level” fusion models for nonprocess data, such as vibration and acoustic signals. In this article, a methodology for constructing a composite health index from fusion of spectrum amplitudes is proposed. Here, each spectrum amplitude can be regarded as “an individual sensor.” The goal of this article is that a composite health index generated from fusion of spectrum amplitudes can simultaneously detect incipient faults and provide a monotonically increasing trend for degradation assessment. Our proposed methodology was verified by two illustrative examples including gearbox run-to-failure vibration data and bearing run-to-failure vibration data. Results showed that our proposed methodology is better than popular sparse measures for gear and bearing health monitoring and degradation assessment.Note to Practitioners—Process data, such as temperature, pressure, speed, etc., are capable of directly showing degradation trends of degraded systems and components. “Data-level” fusion models can be directly used to fuse process data from multiple sensors to show a better-fused degradation trend than a sole trend obtained from an individual process data sensor. Being different from process data, nonprocess data, such as vibration and acoustic data, cannot be used to directly show degradation trends unless they are transformed into a health index. One of the benefits of nonprocess data is that they have been proved to be sensitive to incipient machine faults. Nevertheless, due to complicated transmission paths and multiple responses, transforming nonprocess data into a health index is still a challenging task. This article presents a methodology to fuse spectrum amplitudes to form a health index that can simultaneously detect incipient machine faults and assess monotonic machine degradation. The main idea of this article is to regard each spectrum amplitude as “an individual sensor” and the sum of weighted spectrum amplitudes as a health index. To implement the proposed methodology, it is necessary: 1) to transform temporal nonprocess data into frequency spectra by using the well-known Fourier transform; 2) to know two essential properties about detecting incipient machine faults and assessing machine degradation; and 3) to train weights based on the essential properties by any convex optimization algorithms. Once optimal weights are obtained, the health index has ability to simultaneously detect incipient machine faults and monotonically assess machine degradation.

Infrared thermography provides abundant spatiotemporal degradation information, facilitating non-contact condition monitoring. Reducing domain shift between simulated and industrial infrared images is significantly desired for leveraging labeled simulated data to tackle practical insufficiency of run-to-failure samples. Recently, adversarial-based domain adaptation (DA) techniques have aroused broad concern in solving domain shifts. However, simultaneously aligning marginal and conditional distributions in cross-domain remaining useful life (RUL) prediction is rarely researched in adversarial-based DA. In this article, an adversarial regressive domain adaptation (ARDA) approach is, thus, put forward to address this challenge. First, a regressive disparity discrepancy is designed to describe the dissimilarity between distributions and derive the generalization bound for cross-domain prognostics. Guided by this bound, the ARDA effectively aligns marginal and conditional distributions by learning indistinguishable features and considering the relationship between samples and prediction tasks. Simulated and experimental infrared degradation image datasets are used to demonstrate the effectiveness and superiority of the proposed approach over existing methods for cross-domain RUL prediction.

The degradation data of highly reliable products are usually analyzed by stochastic process models, such as Wiener process, gamma process and inverse Gaussian process models. If such a specific degradation model is wrongly assumed, then poor analysis results of reliability assessment would be obtained. Therefore, a class of exponential-dispersion processes, named Tweedie exponential-dispersion process (TEDP), is proposed to describe the products’ degradation paths. The TEDP model which comprises the aforementioned stochastic processes as its special cases, is more flexible and applicable for degradation modeling. Considering the nonlinear characteristics of degradation paths and the unit-to-unit variability among the product units, random-effect models are established based on the nonlinear TEDP models with random drift and dispersion parameters. To improve the mathematical tractability of these models, the variational inference, expectation maximization algorithm and differential evolution algorithm are used to estimate the unknown model parameters. Furthermore, two nonlinear TEDP models with accelerated factors and random effects are developed for accelerated degradation analysis. Finally, a simulation study and three real applications are presented to show the effectiveness and superiority of the proposed models and methods.

Increasing machine investments and expensive operations and maintenance (O&M) costs have made manufacturing system leasing and maintenance service outsourcing gaining a momentum. Leading original equipment manufacturers, as lessors, have focused on providing cost-effective maintenance schemes to serve their client-enterprises (lessees) all over the world. However, individual equipment degradations, complex system structures, and global network layout bring challenges for the real-time decision-making. This article comprehensively develops a service-outsourcing progressive opportunistic maintenance methodology for a global service-outsourcing network with prognostic updating and dynamical optimization. At the equipment layer, an automated prognostic model is utilized to characterize and update the individual path of each leased equipment's degradation signals. At the local layer, an opportunistic maintenance policy is developed for balancing production capacity and optimizing maintenance decisions of each system with even series-parallel structure. At the global layer, a routing optimization policy is proposed for the service-outsourcing network by integrating service time windows and multiple geographical locations to optimize the service route of required maintenance teams and the service start time of each group set. Finally, this hierarchical methodology has been verified in a multilocation service-outsourcing network. Its mechanism with real-time prognostic updating and dynamical O&M optimization can significantly ensure cost reduction, service timeliness and network robustness.

Degradation modeling is an important method of reliability analysis for highly reliable products. The common degradation models are based on specific stochastic processes. This limits the widespread application of the modeling methods. A unified approach toward general degradation models is lacked. To address this issue, this article uses Tweedie exponential dispersion processes (TEDP) to establish degradation models. In such a way, the common stochastic processes turn out to be the special cases of TEDP. Then, the TEDP models can provide us more suitable models to describe the degradation paths and thereby improve the accuracy of reliability analysis. To develop the mathematical tractability of TEDP, we use the saddle-point approximation method to approximate the probability density function. Considering the unit-to-unit variability and imperfect observation, the TEDP model incorporated random effects and measurement errors are discussed. To illustrate the applicability and advantages of the TEDP models, we propose a Bayesian framework for the prognostic. A component-wise Metropolis-Hastings algorithm is developed to update the distributions of remaining useful life. Additionally, we also construct an optimization model under the constraint of budget for the accelerated degradation test planning by using TEDP. Finally, two case studies are presented to illustrate the proposed methods.

Considering the operation process rebuilding (OPR) of manufacturing/operation systems, we propose a dynamic interactive bilevel maintenance methodology to satisfy rapid market changes. Predictive maintenance (PdM) intervals at the machine level are dynamically scheduled by a multiobjective model for each diverse machine. A system-level opportunistic maintenance (OM) policy is proposed to facilitate PdM optimizations according to OPR activities. This novel OPR-OM policy utilizes a variable maintenance time window to construct optimal maintenance schedules that are suitable for changeable system structures. The results obtained by applying this methodology at Shanghai Port indicate that the proposed methodology can help a port transportation system to achieve rapid responses to OPR activities, which can significantly improve system efficiency and economy.