Task scheduling to extend platform useful life using prognostics.

International audienceIn this paper, we aim at maximizing the useful life of a heterogeneous distributed platform which has to deliver a given production. The machines (one nominal mode and several degraded ones). Depending on the profile, a machine reaches a given throughput. At each time the sum of the machine throughputs that are currentky running determines the global throughput. Moreover, each machine is supposed to be monitored and a prognostic module gives its remaining useful life depending on both its past and future usage (profile). the objective is to configure the platform so as to reach the demand as long as possible. We propose to discretize the time into periods and to choose a configuration for each period. We propose an Integer Linear Programming (ILP) model to find such configurations for a fixed time horizon. Due to the number of variables and constraints in the ILP, the largest horizon can be computed for small instances of the problem. For larger ones , we propose polynomial time heuristics to maximize the useful life. Exhaustive simulations show that the heuristics solutions are close to the optimal (5% in average) in the case where the optimal horizon can to computed. for other platforms with a very large number of machines, simulations assess the efficienty of our heuristics. The distance to the theoretical maximal value is about 8% in average

Prognostics-based Scheduling to Extend a Distributed Platform Production Horizon under Service Constraint: Model, Complexity and Resolution.

In the field of production scheduling, this paper addresses the problem of optimizing the useful life of a heterogeneous distributed platform composed of identical parallel machines and which has to provide a given production service. Each machine is supposed to be able to provide several throughputs corresponding to different operating conditions. The purpose is to provide a production scheduling that maximizes the production horizon. The use of Prognostics and Health Management (PHM) results in the form of Remaining Useful Life (RUL) allows to adapt the schedule to the wear and tear of machines. This work comes within the scope of Prognostics Decision Making (DM). The key point is to configure the platform, i.e., to select the appropriate profile for each machine during the whole production horizon so as to reach a total throughput based on a customer demand as long as possible. In the homogeneous case, the Longest Remaining Useful Life first algorithm (LRUL) is proposed to find a solution and its optimality is proven. The NP-Completeness of the general case is then shown. A Binary Integer Linear Programming (BILP) model which allows to find optimal solutions for fixed time horizons has been defined. As solving such a BILP is NP-Complete, solutions can however be computed in reasonable time only for small size instances of the problem. Many heuristics are then proposed to cope with large scale decision problems and are compared through simulation results. Exhaustive simulations assess the efficiency of these heuristics. Distance to the theoretical maximal value comes indeed close to 5% for the most efficient ones

PETRA: Process Evolution using a TRAce-based system on a maintenance platform

To meet increasing needs in the field of maintenance, we studied the dynamic aspect of process and services on a maintenance platform, a major challenge in process mining and knowledge engineering. Hence, we propose a dynamic experience feedback approach to exploit maintenance process behaviors in real execution of the maintenance platform. An active learning process exploiting event log is introduced by taking into account the dynamic aspect of knowledge using trace engineering. Our proposal makes explicit the underlying knowledge of platform users by means of a trace-based system called “PETRA”. The goal of this system is to extract new knowledge rules about transitions and activities in maintenance processes from previous platform executions as well as its user (i.e. maintenance operators) interactions. While following a Knowledge Traces Discovery process and handling the maintenance ontology IMAMO, “PETRA” is composed of three main subsystems: tracking, learning and knowledge capitalization. The capitalized rules are shared in the platform knowledge base in order to be reused in future process executions. The feasibility of this method is proven through concrete use cases involving four maintenance processes and their simulation

Intelligent maintenance management in a reconfigurable manufacturing environment using multi-agent systems

Thesis (M. Tech.) -- Central University of Technology, Free State, 2010Traditional corrective maintenance is both costly and ineffective. In some situations it is more cost effective to replace a device than to maintain it; however it is far more likely that the cost of the device far outweighs the cost of performing routine maintenance. These device related costs coupled with the profit loss due to reduced production levels, makes this reactive maintenance approach unacceptably inefficient in many situations. Blind predictive maintenance without considering the actual physical state of the hardware is an improvement, but is still far from ideal. Simply maintaining devices on a schedule without taking into account the operational hours and workload can be a costly mistake. The inefficiencies associated with these approaches have contributed to the development of proactive maintenance strategies. These approaches take the device health state into account. For this reason, proactive maintenance strategies are inherently more efficient compared to the aforementioned traditional approaches. Predicting the health degradation of devices allows for easier anticipation of the required maintenance resources and costs. Maintenance can also be scheduled to accommodate production needs. This work represents the design and simulation of an intelligent maintenance management system that incorporates device health prognosis with maintenance schedule generation. The simulation scenario provided prognostic data to be used to schedule devices for maintenance. A production rule engine was provided with a feasible starting schedule. This schedule was then improved and the process was determined by adhering to a set of criteria. Benchmarks were conducted to show the benefit of optimising the starting schedule and the results were presented as proof. Improving on existing maintenance approaches will result in several benefits for an organisation. Eliminating the need to address unexpected failures or perform maintenance prematurely will ensure that the relevant resources are available when they are required. This will in turn reduce the expenditure related to wasted maintenance resources without compromising the health of devices or systems in the organisation

Reliability Modeling and Improvement of Critical Infrastructures: Theory, Simulation, and Computational Methods

This dissertation presents a framework for developing data-driven tools to model and improve the performance of Interconnected Critical Infrastructures (ICIs) in multiple contexts. The importance of ICIs for daily human activities and the large volumes of data in continuous generation in modern industries grant relevance to research efforts in this direction. Chapter 2 focuses on the impact of disruptions in Multimodal Transportation Networks, which I explored from an application perspective. The outlined research directions propose exploring the combination of simulation for decision-making with data-driven optimization paradigms to create tools that may provide stakeholders with optimal policies for a wide array of scenarios and conditions. The flexibility of the developed simulation models, in combination with cutting-edge technologies, such as Deep Reinforcement Learning (DRL), sets the foundation for promising research efforts on the performance, analysis, and optimization of Inland Waterway Transportation Systems. Chapter 3 explores data-driven models for condition monitoring and prognostics, with a focus on using Deep Learning (DL) to predict the Remaining Useful Life of turbofan engines based on sequential sensor measurements. A myriad of approaches exist for this type of problems, and the main contribution for future efforts might be centered around combining this type of data-driven methods with simulation tools and computational methods in the context of network resilience optimization. Chapter 4 revolves around developing data-driven methods for estimating all-terminal reliability of networks with arbitrary structures and outlines research directions for data-driven surrogate models. Furthermore, the use of DRL for network design optimization and maximizing all-terminal network reliability is presented. This poses a promising research venue that has been extended to network reliability problems involving dynamic decision-making on allocating new resources, maintaining and/or improving the edges already in the network, or repairing failed edges due to aging. The outlined research presents various data-driven tools developed to collaborate in the context of modeling and improvement for Critical Infrastructures. Multiple research venues have been intertwined by combining various paradigms and methods to achieve this goal. The final product is a line of research focused on reliability estimation, design optimization, and prognostics and health management for ICIs, by combining computational methods and theory

Predictive maintenance for industry 4.0, a holistic approach to performing predictive maintenance as a service.

In modern collaborative industry, the machine equipment involved has rapidly increased. Many of the involved machines are complex and can only work in good maintenance conditions. Any failure of this equipment and related tools can easily lead to unintended disruption. Due to the collaborative nature of the manufacturing systems, one machine failure could result in undesired downtimes beyond single production lines and add costs to the value-added processes of the partner enterprises in the entire value chain. Industry 4.0 provides a concept of the interoperation of data, processes, and services within one enterprise as well as interoperation among different partner organizations. This increases dependencies and potential for failure related costs. There is, however, a lack of work that focusses on predictive maintenance services in the context of Industry 4.0 supported architecture and standards. This thesis looks at how data-driven predictive maintenance under existing Industry 4.0 concepts, architecture, and platforms can be supported. A flexible predictive maintenance case is used to design the predictive maintenance modules that fit within the industry standard Reference Architectural Model Industrie 4.0 (RAMI 4.0) model. Beyond looking at predictive maintenance for a specific manufacturing type, the research further looks at predictive maintenance as a service as well as forming a virtual factory specialized in supporting predictive maintenance. Adopting the design science research methodology, the dissertation designs Industry 4.0 Predictive Maintenance Architecture, algorithms of predictive maintenance modules for estimating RUL (Remaining Useful Life) and maintenance scheduling modules for supporting multiple machines/components. The design of architecture and algorithms are implemented within the leading FIWARE platform. The results are verified in terms of performance. The modular predictive model achieves higher accuracy and lower RMSE score at over 19% than comparator methods. The predictive maintenance service enabled by designed algorithms of predictive model and maintenance service scheduling can offer over 30% for optimal cost and 10% for downtime impact to the manufacturing network

Risk-Based Optimal Scheduling for the Predictive Maintenance of Railway Infrastructure

In this thesis a risk-based decision support system to schedule the predictive maintenance activities, is proposed. The model deals with the maintenance planning of a railway infrastructure in which the due-dates are defined via failure risk analysis.The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, according to ISO 55000 guidelines, thus implying that the maintenance priorities are based on asset criticality, determined taking into account the relevant failure probability, related to asset degradation conditions, and the consequent damages

An RUL-informed approach for life extension of high-value assets

The conventional approaches for life-extension (LE) of industrial assets are largely qualitative and focus only on a few indicators at the end of an asset’s design life. However, an asset may consist of numerous individual components with different useful lives and therefore applying a single LE strategy to every component will not result in an efficient outcome. In recent years, many advanced analytics techniques have been proposed to estimate the remaining useful life (RUL) of the assets equipped with sensor technology. This paper proposes a data-driven model for LE decision-making based on RUL values predicted on a real-time basis during the asset’s operational life. Our proposed LE model is conceptually targeted at the component, unit, or subsystem level; however, an asset-level decision is made by aggregating information across all components. Consequently, LE is viewed and assessed as a series of ongoing activities, albeit carefully orchestrated in a manner similar to operation and maintenance (O&M). The application of the model is demonstrated using the publicly available NASA C-MAPSS dataset for large commercial turbofan engines. This approach will be very beneficial to asset owners and maintenance engineers as it seamlessly weaves LE strategies into O&M activities, thus optimizing resources

Practical Methods for Optimizing Equipment Maintenance Strategies Using an Analytic Hierarchy Process and Prognostic Algorithms

Many large organizations report limited success using Condition Based Maintenance (CbM). This work explains some of the causes for limited success, and recommends practical methods that enable the benefits of CbM. The backbone of CbM is a Prognostics and Health Management (PHM) system. Use of PHM alone does not ensure success; it needs to be integrated into enterprise level processes and culture, and aligned with customer expectations. To integrate PHM, this work recommends a novel life cycle framework, expanding the concept of maintenance into several levels beginning with an overarching maintenance strategy and subordinate policies, tactics, and PHM analytical methods. During the design and in-service phases of the equipment’s life, an organization must prove that a maintenance policy satisfies specific safety and technical requirements, business practices, and is supported by the logistic and resourcing plan to satisfy end-user needs and expectations. These factors often compete with each other because they are designed and considered separately, and serve disparate customers. This work recommends using the Analytic Hierarchy Process (AHP) as a practical method for consolidating input from stakeholders and quantifying the most preferred maintenance policy. AHP forces simultaneous consideration of all factors, resolving conflicts in the trade-space of the decision process. When used within the recommended life cycle framework, it is a vehicle for justifying the decision to transition from generalized high-level concepts down to specific lower-level actions. This work demonstrates AHP using degradation data, prognostic algorithms, cost data, and stakeholder input to select the most preferred maintenance policy for a paint coating system. It concludes the following for this particular system: A proactive maintenance policy is most preferred, and a predictive (CbM) policy is more preferred than predeterminative (time-directed) and corrective policies. A General Path prognostic Model with Bayesian updating (GPM) provides the most accurate prediction of the Remaining Useful Life (RUL). Long periods between inspections and use of categorical variables in inspection reports severely limit the accuracy in predicting the RUL. In summary, this work recommends using the proposed life cycle model, AHP, PHM, a GPM model, and embedded sensors to improve the success of a CbM policy