A synergetic approach for assessing and improving equipment performance in offshore industry based on dependability

Abstract

The objective of this paper is to present a framework for assessing and improving offshore equipment performance based on dependability. The main idea is to employ principle component analysis (PCA) and importance analysis (IA) to provide insight on the equipment performance. The validity of the model is verified and validated by data envelopment analysis (DEA). Furthermore, a non-parametric correlation method, namely, Spearman correlation experiment shows a high level of correlation between the findings of PCA and DEA. The equipment of offshore industries is considered according to OREDA classification. The approach identifies the critical equipment, which could initiate the major hazards in the system. At first PCA is used for assessing the performance of the equipment and ranking them. IA is then performed for the worst equipment which could have most impact on the overall system effectiveness to classify their components based on the component criticality measures (CCM). The analysis of the classified components can ferret out the leading causes and common-cause events to pave a way toward decreasing failure interdependency and magnitude of incidents which ultimately maximize overall operational effectiveness.

Introduction

With the growing complexity of industrial systems, such as offshore and chemical plants, and the increasingly stringent safety and reliability requirements, attention has been focused on dependability assessment and effectiveness improvement. Major factors influencing the overall effectiveness and efficiency of an industrial organization are identified as technology, equipment, management, personnel, rules and procedures [1], [4], [20]. Equipment plays an important role in the overall performance of manufacturing systems. In fact, equipment performance is correlated with the overall effectiveness of a manufacturing system. According to US National Research Council Report in 1990, one of the research priorities of US manufacturing is equipment reliability and maintainability. Furthermore, the quality performance of an industrial organization is often assessed by reliability and safety of its equipment.

Fordham et al. [10] concluded that efficiency, reliability and availability play the most important role in effectiveness improvement of power and nuclear plants. Ingruber [14] considered efficiency and reliability aspects for improving of harmonic-control amplifiers. Hinkel [13] introduced a procedure to reach a high-performance of pump through enhancing efficiency and reliability indicators. Moustafa and Sadek [17] regarded maintainability as a key factor in their analysis and proposed multivariate models for making maintenance policies for proper and efficient functions of the manufacturing system. Generic Markov models for availability and failure analysis in petroleum refineries were presented by Chochran and Krishnamurthy [8]. A petri net was introduced for failure detection as an effective tool for preventive maintenance in reliability enhancement [22]. Bowlin [5] showed that data envelopment analysis (DEA) can also be used to evaluate the efficiency of systems which are responsible for utilizing resources to obtain outputs of interest. The equipment factor is mainly concerned with equipment condition or status in a specified period. Preventive maintenance, machine layout and calibration influence machine condition. Equipment performance is measured through factors/indicators which are defined in terms of available time, operating time, active repair time, down time, etc.

The impact of equipment failures on total effectiveness is an important concern for manufacturing systems. Today, many industries utilize advanced methods to enhance their knowledge and understanding about the equipment performance and its impact on system behavior. Therefore, the need for an integrated approach for continuous assessment and the improvement of system availability based on equipment dependability becomes essential.

This study has identified dependability indicators as major technical factors, which have the most contribution on overall system effectiveness. Dependability is defined as the trustworthiness of a system such that reliance can justifiably be placed on the service it provides. Dependability is an overall ability which has other measures such as reliability, availability, maintainability and safety. These terms are defined, respectively; (1) reliability: the probability that a equipment will perform in a satisfactory manner, (2) availability: the probability that an equipment will be operational at a given time, (3) maintainability: the probability that a given active maintenance action, for an item under given conditions of use, can be carried out within a stated time interval when the maintenance is performed under stated conditions and using stated procedures and resources, (4) safety: a measure of the continuous delivery of proper service free from occurrences of catastrophic failures.

The integrated approach takes four steps which are: (1) assessing the performance of equipment based on availability, maintainability and reliability, (2) ranking equipment to recognize the critical equipment based on PCA scores, (3) verification and validation of the PCA results by using DEA, (4) importance analysis for the components of critical equipment by using the component criticality measures.

The paper is organized as follows. In Section 2, the models are presented. In Section 3, we introduce the integrated approach discussed in this paper. Section 4 contains our conclusions and directions.