Mohamed-Salah Ouali

Optimal condition based maintenance with imperfect information and the proportional hazards model

Condition based maintenance (CBM) is based on collecting observations over time, in order to assess equipments state, to prevent its failure and to determine the optimal maintenance strategies. In this paper, we derive an optimal CBM replacement policy when the state of equipment is unknown but can be estimated based on observed condition. We use a proportional hazards model (PHM) to represent the systems degradation. Since equipments state is unknown, the optimization of the optimal maintenance policy is formulated as a partially observed Markov decision process (POMDP), and the problem is solved using dynamic programming. Practical advantages of combining the PHM with the POMDP are shown.

Concurrent optimization of the design and manufacturing stages of product development

The problem of concurrent optimization of the design and the process planning stages when a new product is developed is addressed. The paper advocates for a simultaneous approach rather than the traditional sequential one. A mathematical representation of this approach is given for these two stages. A mathematical programming technique is used to find the optimal values of the design and the process characteristics. The objective function is a quality loss function. The constraints are the customer requirements, the products specification limits, the parts’ dimensional limits and the process capability. The traditional sequential approach of concurrent engineering is compared with the proposed simultaneous approach. A parametric analysis of the objective function is performed by applying an interactive multi-objective goal programming technique. A numerical example of a low-pass electrical circuit is given. It is shown that the proposed approach leads to better efficient solutions than the sequential approach. The decision-maker interacts with the optimization process and can choose the efficient solution that best satisfies the companys needs.

Replacement models with minimal repair

1. A Survey of Replacement Models with Minimal Repair.- 2. Information-based Minimal Repair Models.- 3. Minimal Repair Models with Two Categories of Competing Failure Modes.- 4. Preventive Maintenance Models: A Review.- 5. Optimal Schedules of Two Periodic Imperfect Preventive Maintenance Policies and Their Comparison.- 6. Warranty Servicing with Imperfect Repair for Products Sold with a Two-Dimensional Warranty.- 7. A Survey of Burn-in and Maintenance Models for Repairable Systems.- 8. Filtering and M-ary Detection in a Minimal Repair Maintenance Model.- 9. Efficient Product Support - Optimum and Realistic Spare Parts Forecasting.

Remaining useful life prediction using prognostic methodology based on logical analysis of data and Kaplan---Meier estimation

Most of the reported prognostic techniques use a small number of condition indicators and/or use a thresholding strategies in order to predict the remaining useful life (RUL). In this paper, we propose a reliability-based prognostic methodology that uses condition monitoring (CM) data which can deal with any number of condition indicators, without selecting the most significant ones, as many methods propose. Moreover, it does not depend on any thresholding strategies provided by the maintenance experts to separate normal and abnormal values of condition indicators. The proposed prognostic methodology uses both the age and CM data as inputs to estimate the RUL. The key idea behind this methodology is that, it uses Kaplan–Meier as a time-driven estimation technique, and logical analysis of data as an event-driven diagnostic technique to reflect the effect of the operating conditions on the age of the monitored equipment. The performance of the estimated RUL is measured in terms of the difference between the predicted and the actual RUL of the monitored equipment. A comparison between the proposed methodology and one of the common RUL prediction technique; Cox proportional hazard model, is given in this paper. A common dataset in the field of prognostics is employed to evaluate the proposed methodology.

Application of logical analysis of data to machinery-related accident prevention based on scarce data

Abstract This paper deals with the application of Logical Analysis of Data (LAD) to machinery-related occupational accidents, using belt-conveyor-related accidents as an example. LAD is a pattern recognition and classification approach. It exploits the advancement in information technology and computational power in order to characterize the phenomenon under study. The application of LAD to machinery-related accident prevention is innovative. Ideally, accidents do not occur regularly, and as a result, companies have little data about them. The first objective of this paper is to demonstrate the feasibility of using LAD as an algorithm to characterize a small sample of machinery-related accidents with an adequate average classification accuracy. The second is to show that LAD can be used for prevention of machinery-related accidents. The results indicate that LAD is able to characterize different types of accidents with an average classification accuracy of 72–74%, which is satisfactory when compared with other studies dealing with large amounts of data where such a level of accuracy is considered adequate. The paper shows that the quantitative information provided by LAD about the patterns generated can be used as a logical way to prioritize risk factors. This prioritization helps safety practitioners make decisions regarding safety measures for machines.

Prognostics of multiple failure modes in rotating machinery using a pattern-based classifier and cumulative incidence functions

This paper presents a novel methodology for multiple failure modes prognostics in rotating machinery. The methodology merges a machine learning and pattern recognition approach, called logical analysis of data (LAD), with non-parametric cumulative incidence functions (CIFs). It considers the condition monitoring data collected from a system that experiences several competing failure modes over its life span. LAD is used as a non-statistical classification technique to detect the actual state of the system, based on the condition monitoring data. The CIF provides an estimate for the marginal probability of each failure mode in the presence of the other competing failure modes. Accordingly, the assumption of independence between the failure modes, which is essential in many prognostic methods, is irrelevant in this paper. The proposed methodology is validated using vibration data collected from bearing test rigs. The obtained results are compared to those of two common machine learning prediction techniques: the artificial neural network and support vector regression. The comparison shows that the proposed methodology has a stable performance and can predict the remaining useful life of an individual system accurately, in the presence of multiple failure modes.

A Survey of Replacement Models with Minimal Repair

This presentation, which is mainly of an expository nature, is divided into two parts. The first part is concerned with age replacement age replacement models with minimal repair minimal repair and the second part deals with block replacement block replacement models with minimal repair. Each part focuses mainly on the mathematical modeling of the notion of minimal repair. To limit the scope of this survey, we decided to limit ourselves almost exclusively to research on papers where a cost function cost function is designed specifically and optimal replacement replacement times optimal replacement times that yield minimum cost are sought.

Pattern-based prognostic methodology for condition-based maintenance using selected and weighted survival curves

This paper proposes a pattern-based prognostic methodology that combines logical analysis of data (LAD) as an event-driven diagnostic technique, and Kaplan–Meier (KM) estimator as a time-driven technique. LAD captures the effect of the instantaneous conditions on the health state of a monitored system, while KM estimates the baseline reliability curve that reflects the effect of aging, based on the observed historical failure times. LAD is used to generate a set of patterns from the observed values of covariates that represent the operating conditions and condition indicators. A pattern selection procedure is carried out to select the set of significant patterns from all the generated patterns. A survival curve is estimated, for each subset of observations covered by each selected pattern. A weight that reflects the coverage of each pattern is assigned to its survival curve. Given a recently collected observation, the survival curve of a monitored system is updated on the basis of the patterns covering that observation. The updated curve is then used to predict the remaining useful life of the monitored system. The proposed methodology is validated using a common dataset in prognostics: the turbofan degradation dataset that is available at NASA prognostic repository. Copyright

Multiple failure modes prognostics using logical analysis of data

In this paper, we propose a multiple fault prognostic methodology which considers the condition monitoring data collected from equipment that experiences one of several different failure modes over its life span. The methodology is based on the exploitation of historical data for knowledge extraction and representation in the form of relevant patterns. Since the technique used is non statistical, none of the usual statistical assumptions, such as the independency of failure modes, are necessary. The idea of the proposed methodology is to merge the Logical Analysis of Data (LAD) approach with a set of non-parametric cause-specific survival functions. The former reflects the effect of the condition monitoring data of each failure mode, which is collected from the monitored equipment, on its failure time. The latter provides estimate of the marginal probability of each failure mode in the presence of the other competing failure modes. The results obtained show t hat the proposed methodology is capable of describing accurately the state of each individual equipment based on the collected condition monitoring data, and to use this information in order to provide accurate prognostics.

Sensibility of Bayesian inference methods for reliability prediction of ageing systems, case of Diesel locomotives

This paper addresses the problem of reliability analysis of in-service identical systems when a limited number of lifetime data is available compared to censored ones. Lifetime (resp. censored) data characterise the life of failed (resp. non-failed) systems in the sample. Because, censored data induce biassed estimators of reliability model parameters, a methodology approach is proposed to overcome this inconvenience and improve the accuracy of the parameter estimation based on Bayesian inference methods. These methods combine, in an effective way, system’s life data and expert opinions learned from failure diagnosis of similar systems. Three Bayesian inference methods are considered: Classical Bayesian, Extended Bayesian and Bayesian Restoration Maximisation methods. Given a sample of lifetime data, simulated according to prior opinions of maintenance expert, a sensibility analysis of each Bayesian method is performed. Reliability analysis of critical subsystems of Diesel locomotives is established under the proposed methodology approach. The relevance of each Bayesian inference methods with respect to collected reliability data of critical subsystems and expert opinions is discussed.