Khairy A. H. Kobbacy

A model for residual life prediction based on Brownian motion with an adaptive drift

Abstract A degradation model is presented in this paper for the prediction of the residual life using an adapted Brownian motion-based approach with a drifting parameter. This model differs from other Brownian motion-based approaches in that the drifting parameter of the degradation process is adapted to the history of monitored information. This adaptation is performed by Kalman filtering. We also use a threshold distribution instead of the usual single threshold line which is sometime difficult to obtain in practice. We demonstrate the model using some examples and show that the model performs reasonably well and has a better prediction ability than the standard Brownian motion-based model. The model is then fitted to the data generated from a simulator using the expectation–maximization algorithm. We also fit a standard Brownian motion-based model to the same data to compare the difference and performance. The result shows that the adapted model performs better in terms of certain test statistics and the total mean square errors.

Determining economical maintenance intervals

Abstract Several models have been proposed for scheduling the preventive maintenance (PM) of complex repairable systems in industry. These are often application-specific and some make unrealistic assumptions about stationarity of the process and quality of repairs. We investigate two principal types of general model, which have wider applicability. The first considers fixed PM intervals and is based on the delayed alternating renewal process. The second is adaptable, allowing variable PM intervals, and is based on proportional hazards or intensities. We describe how Bayesian methods of analysis can improve the decision making process for these models and discuss simulation algorithms for fitting the models to observed data. Finally, we identify some issues that need more research.

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.

A full history proportional hazards model for preventive maintenance scheduling

This paper is concerned with the development of a realistic preventive maintenance (PM) scheduling model. A heuristic approach for implementing the semi-parametric proportional-hazards model (PHM) to schedule the next preventive maintenance interval on the basis of the equipments full condition history is introduced. This heuristic can be used with repairable systems and does not require the unrealistic assumption of renewal during repair, or even during PM. Two PHMs are fitted, for the life of equipment following corrective work and the life of equipment following PM, using appropriate explanatory variables. These models are then used within a simulation framework to schedule the next preventive maintenance interval. Optimal PM schedules are estimated using two different criteria, namely maximizing availability over a single PM interval and over a fixed horizon. History data from a set of four pumps operating in a continuous process industry is also used to demonstrate the proposed approach. The results indicate a higher availability for the recommended schedule than the availability resulting from applying the optimal PM intervals as suggested by using the conventional stationary models.

Towards the development of an intelligent inventory management system

This thesis is concerned with the development of an intelligent inventory management system. The aim of the system is to bridge the substantial gap between the theory and the practice of inventory management and to help industrial inventory managers to achieve an effective and successful inventory management. The proposed system attempts to achieve this by providing automatic pattern identification and model selection facilities. Such a hybrid knowledge-based inventory system consists of a collection of techniques (or pattern identifier) for identifying demand and lead time patterns and a knowledge base (or rule base) for subsequent selection of a suitable inventory model taking into consideration aspects of the practical situation. There are no previous attempts in the inventory literature to develop such a system to guide model selection. In order to integrate the system into the established computer-based intelligent inventory management system and facilitate the function of the pattern identifier, a data manager has been developed to manipulate the history data required for statistical analysis and to load the data into the system from other applications. In order to establish the systems model base, the study of the modelling of inventory and the features and evolution of expert systems are reviewed. The published models which deal with similar inventory problems have been compared based on its applicability, clarity, and being suitable to be computerised. It was necessary to further develop and amend published models to fill gaps in the model base. The overall structure and salient features of the proposed system and the development of the system using Visual Basic have been described. The system has been tested using real life data supplied by the co-operating companies. Finally, achievements and shortcomings of the system are discussed and some suggestions for further research are outlined.

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elseviers ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested.

Setting preventive maintenance schedules when data are sparse

Abstract When new production lines are established, little information is available about their reliability. The evaluation of such systems is a learning process and knowledge is continually updated as more information becomes available. This paper considers stochastic models when data are sparse, with emphasis on preventive maintenance intervention to avoid system failure. Bayesian methods are adopted, leading to optimal strategies under the model assumptions. This approach also includes prior knowledge about the manufacturing process and similar systems. Our approach is a first reconnaissance into a new field, exemplary of ways to solve these problems, rather than an algorithm that can be readily applied.

Preventive maintenance modelling

Develops practical models for preventive maintenance policies using Bayesian methods of statistical inference. Considers the analysis of a delayed renewal process and a delayed alternating renewal process with exponential times to failure. This approach has the advantage of generating predictive distributions for numbers of failures and downtimes rather than relying on estimated renewal functions. Demonstrates the superiority of this approach in analysing situations with non‐linear cost functions, which arise in reality, by means of an example.

The development of a hybrid intelligent maintenance optimisation system (HIMOS)

This paper reports on the development of a hybrid intelligent maintenance optimisation system (HIMOS) for decision support. It is a follow-up to an earlier paper published in the Journal of the Operational Research Society in 1995. Both papers refer to systems where there are very many components which may break down independently. When a component breaks down, corrective action (CO) is required. The problem is to determine the optimal maintenance policy, essentially the frequency of preventive maintenance (PM) which minimises the sum of down time due to PM and CO.HIMOS, like its predecessor IMOS, uses an ‘intelligent’ decision support system to carry out an automated analysis of the maintenance history data. Maintenance data are presented to the system and the most suitable mathematical model from a model-base is identified utilising a hybrid knowledge/case based system (KBS/CBR). Thus initially a rule base is applied to select a model, as in the case of IMOS. If no model is matched, the system reverts to its historical case-base to match the current case with a similar case that has been previously modelled. This double reasoning adds to the systems true learning capabilities (intelligence) and increases the rate of success of model selection. A prototype system is written in Visual Basic® for an IBM compatible PC. The study results include optimal PM intervals for a sample of industrial data sets. The results of the validation exercise of HIMOS against expert advice has shown that the system functions satisfactorily.

The use of knowledge-based systems in evaluation and enhancement of maintenance routines

Abstract For large industrial organisations, a considerable saving in operations costs can be achieved through improving the effectiveness of the maintenance function. Special considerations, though, have to be given to the design of the maintenance software aimed at assisting with the evaluation and enhancement of the established routines for large technical systems. In this paper a knowledge-based approach is proposed. The essential features of such intelligent systems and the main technical characteristics such as data pattern recognition and model selection are discussed.