Stochastic Renewal Process Model for Condition-Based Maintenance

This thesis deals with the reliability and maintenance of structures that are damaged by shocks arriving randomly in time. The degradation is modeled as a cumulative stochastic point process. Previous studies mostly adopted expected cost rate criterion for optimizing the maintenance policies, which ignores practical implications of discounting of maintenance cost over the life cycle of the system.Therefore, detailed analysis of expected discounted cost criterion has been done, which provides a more realistic basis for optimizing the maintenance. Examples of maintenance policies combining preventive maintenance with age- based replacement are analyzed. Derivation for general cases involving preventive maintenance damage level have been discussed. Special cases are also considered

Reliability and Condition-Based Maintenance Analysis of Deteriorating Systems Subject to Generalized Mixed Shock Model

For successful commercialization of evolving devices (e.g., micro-electro-mechanical systems, and biomedical devices), there must be new research focusing on reliability models and analysis tools that can assist manufacturing and maintenance of these devices. These advanced systems may experience multiple failure processes that compete against each other. Two major failure processes are identified to be deteriorating or degradation processes (e.g., wear, fatigue, erosion, corrosion) and random shocks. When these failure processes are dependent, it is a challenging problem to predict reliability of complex systems. This research aims to develop reliability models by exploring new aspects of dependency between competing risks of degradation-based and shock-based failure considering a generalized mixed shock model, and to develop new and effective condition-based maintenance policies based on the developed reliability models. In this research, different aspects of dependency are explored to accurately estimate the reliability of complex systems. When the degradation rate is accelerated as a result of withstanding a particular shock pattern, we develop reliability models with a changing degradation rate for four different shock patterns. When the hard failure threshold reduces due to changes in degradation, we investigate reliability models considering the dependence of the hard failure threshold on the degradation level for two different scenarios. More generally, when the degradation rate and the hard failure threshold can simultaneously transition multiple times, we propose a rich reliability model for a new generalized mixed shock model that is a combination of extreme shock model, δ-shock model and run shock model. This general assumption reflects complex behaviors associated with modern systems and structures that experience multiple sources of external shocks. Based on the developed reliability models, we introduce new condition-based maintenance strategies by including various maintenance actions (e.g., corrective replacement, preventive replacement, and imperfect repair) to minimize the expected long-run average maintenance cost rate. The decisions for maintenance actions are made based on the health condition of systems that can be observed through periodic inspection. The reliability and maintenance models developed in this research can provide timely and effective tools for decision-makers in manufacturing to economically optimize operational decisions for improving reliability, quality and productivity.Industrial Engineering, Department o

Predictive condition monitoring of industrial systems for improved maintenance and operation

Maintenance strategies based on condition monitoring of the different machines and devices in an industrial process can minimize downtime, increase the safety of plant operations and help in the process of decision-taking for control and maintenance actions in order to reduce maintenance and operating costs. Multivariate statistical methods are widely used for process condition monitoring in modern industrial sites due to the quantity of data available and the difficulties of building analytical models in complex facilities. Nevertheless, the performance of these methodologies is still far away from being ideal, due to different issues such as process nonlinearities or varying operational conditions. In addition application of the latest approaches developed for process monitoring is not widely extended in real industry. The aim of this investigation is to develop new and improve existing methodologies for predictive condition monitoring through the use of multivariate statistical methods. The research focuses on demonstrating the applicability of multivariate algorithms in real complex cases, the improvement of these methods in terms of fault detection and diagnosis by means of data fusion and the estimation of process performance degradation caused by faults.Marie Curi

Condition based maintenance optimization using data driven methods

In condition based maintenance (CBM), maintenance activities are scheduled based on the predicted equipment failure times, and the predictions are performed based on conditon monitoirng data, such as vibration and acoustic data. The reported health condition prediction methods can be roughly classified into model-based, and data-driven, and integrated methods. Our research mainly focuses on CBM optimization using data driven methods, such as proportional hazards model (PHM) and artificial neural network (ANN), which don't require equipment physical models. In CBM optimization using PHM, the accuracy of parameter estimation for PHM greatly affects the effectiveness of the optimal maintenance policy. Directly using collected condition monitoring data may iv introduce noise into the CBM optimization, and thus the optimal maintenance policy obtained based on this model may not be really optimal. Therefore, a data processing method, where the actual measurements are fitted first using the Generalized Weibull-FR function, is proposed to remove the external noise before fitting it into the PHM. Effective CBM optimization methods utilizing ANN prediction information are currently not available due to two key challenges: (1) ANN prediction models typically only give a single remaining life prediction value, and it is hard to quantify the uncertainty associated with the predicted value; (2) simulation methods are generally used for evaluating the cost of the CBM policies, while more accurate and efficient numerical methods are not available. Therefore, we propose an ANN based CBM optimization approach and a numerical cost evaluation method to address those key challenges. It is observed that the prediction accuracy often improves with the increase of the age of the component. Therefore, we develop a method to quantify the remaining life prediction uncertainty considering the prediction accuracy improvements by modeling the relationship between the mean value as well as standard deviation of prediction error and the life percentage. An effective CBM optimization approach is also proposed to optimize the maintenance schedule. The proposed approaches are demonstrated using some simulated degradation data sets as well as some real-world vibration monitoring data set. They contribute to the general knowledge of CBM, and have the potential to greatly benefit various industries

Statistical Sample Size Determination Methods for Inspections of Engineering Systems

A statistical sample size determination (SSD) method is designed for the maintenance of engineering components of similar structure within an overall system. The maintenance problem is defined as a sequential decision-making process, in which the optimal sample sizes are derived by an approach based on the value of information (VoI) concept. Firstly, various sample size determination methods are summarized, and their advantages and disadvantages are discussed. This comparison highlights that, in many cases, the VoI-based approach is superior to traditionally used methods. Existing standards for engineering components are then categorized, based on the comparison, and the rationale behind each standard is described. Potential advantages of using a VoI-based approach are suggested and discussed. Secondly, the theoretical superiority of VoI-based methods is demonstrated in the context of a diagnostic inspection problem, in which the traditional SSD method, the hypothesis-testing approach, can be defined. After the hypothesis-testing context is translated into a sequential decision-making problem, theoretical and numerical results are compared for the VoI-based and traditional methods. Thirdly, the models for condition-based maintenance problems are defined with a time-dependent degradation process called the gamma process. The models mathematically describe how temporal and parameter uncertainties of the degradation process affect the VoI-based analysis. Computational calculation techniques are introduced and compared with each other. Additionally, the model is generalized as a dynamic programming problem and formulated as a multiple-inspection problem. Finally, the effectiveness of the SSD approach is demonstrated through application to an actual degrading system. Based on data from nuclear power plants, numerical analyses are shown for both single and two inspection cases. The results provide operators with guidelines for maintenance and inspection policies that minimize the expected cost throughout the remaining lifetime of the system

Statistical Sample Size Determination Methods for Inspections of Engineering Systems

A statistical sample size determination (SSD) method is designed for the maintenance of engineering components of similar structure within an overall system. The maintenance problem is defined as a sequential decision-making process, in which the optimal sample sizes are derived by an approach based on the value of information (VoI) concept. Firstly, various sample size determination methods are summarized, and their advantages and disadvantages are discussed. This comparison highlights that, in many cases, the VoI-based approach is superior to traditionally used methods. Existing standards for engineering components are then categorized, based on the comparison, and the rationale behind each standard is described. Potential advantages of using a VoI-based approach are suggested and discussed. Secondly, the theoretical superiority of VoI-based methods is demonstrated in the context of a diagnostic inspection problem, in which the traditional SSD method, the hypothesis-testing approach, can be defined. After the hypothesis-testing context is translated into a sequential decision-making problem, theoretical and numerical results are compared for the VoI-based and traditional methods. Thirdly, the models for condition-based maintenance problems are defined with a time-dependent degradation process called the gamma process. The models mathematically describe how temporal and parameter uncertainties of the degradation process affect the VoI-based analysis. Computational calculation techniques are introduced and compared with each other. Additionally, the model is generalized as a dynamic programming problem and formulated as a multiple-inspection problem. Finally, the effectiveness of the SSD approach is demonstrated through application to an actual degrading system. Based on data from nuclear power plants, numerical analyses are shown for both single and two inspection cases. The results provide operators with guidelines for maintenance and inspection policies that minimize the expected cost throughout the remaining lifetime of the system

Whole life costing optimisation with integrated logistics support considerations.

It has long been recognised that, in the military sector, the Integrated Logistics Support ILS can significantly enhance system effectiveness and add value to their competitiveness. Hence, it is not surprising that many organisations outside to the military support the ILS adoption to increase their competence level. Even though the ILS underlying theory is general, there is a lack of suitable methodology that facilitates ILS implementation in other industries such as Oil & Gas industry. In particular when considering complex systems with long life-span, the optimisation of maintenance-related activities is important to fulfil system readiness, safety and whole life cost requirements. Modern petroleum equipment like gas turbines and drilling rigs are dependent on readily available maintenance supports in order to maximise their operational ability. Therefore, it has been identified that the study should be conducted to an effective use of ILS with the petroleum industry. In doing so, the usage of the ILS framework as a decision tool for maintenance optimisation is outlined. This framework embraces ILS concepts to support asset managers in developing their maintenance strategies. Level of repair analysis and spare parts management have been identified as potential areas for enhancing the use of ILS. In particular, maintenance optimisation is approached as a trade-off between investment in spare parts level and repair capacity. The developed framework delivers cost-effective support strategies obtained with iterative optimisation algorithm built on heuristics and genetic algorithm techniques. Finally, this algorithm has been implemented into computational algorithms. The framework can be employed to identify the optimum level of spare parts and the optimum amount of repair capacity for multi echelon repair network and multi-indenture systems. The framework has been used to carry out optimisations intended to maximise the availability of gas turbines by varying logistics support parameters. Typical results have shown that a joint optimisation of spare parts and level of repair analysis leads to better results than optimising them separately and emphasises the need for the developed framework. As part of this research, an expert panel validation method has been used to both refine the design of the developed framework and also evaluate its functionality from experienced practitioners within the Algerian petroleum industry. The results of this validation have demonstrated the advantages of integrating spare part management and level of repair analysis LORA to the problem of maintenance optimisation and shown that the framework is able to deliver optimal maintenance supportability decisions. The generic framework developed in this thesis can be seen a novel and comprehensive model for integrating two ILS elements into the operating tool in a manner that improves maintenance support provision, while remaining both flexible and usable; and therefore as a contribution to a better adoption of ILS technique within Algerian Petroleum Industry