In most of the industries, classical reliability-centered maintenance (RCM) is employed to decide the maintenance strategies using reliability data without having adequate interaction with the design and operational systems. This means that the RCM process will be conducted with no or limited access to the design and operational data/knowledge. Commonly, the developed maintenance strategies are implemented and managed within the computerized maintenance management system (CMMS), which is usually separate from the RCM automated environment. This paper presents the detailed system design and mechanism of improved RCM process as integrated with CMMS. The proposed solution is integrated with design and operational systems and consolidates some successful maintainability approaches to formulate an effective solution for optimized plant maintenance. The major components of the enhanced RCM process are identified and a prototype system is implemented as integrated with the various modules of the adopted CMMS (MAXIMOt). A case study is used to show the effectiveness of the proposed RCM-based CMMS solution in optimizing plant maintenance over the traditional approaches. r 2003 Elsevier Ltd. All rights reserved.

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Computer-aided RCM-based plant maintenance management system

In-service inspection of complex systems such as nuclear steam generator (SG) tubes and their surrounding support structures is challenged by overlapping degradation modes. In these complex systems the simultaneous and accurate measurement of more than two interdependent parameters is difficult using standard statistical regression analysis tools. Recently, artificial neural networks (ANNs) have been investigated for dealing with the complex relation between inspection data and defect properties. In this paper, pulsed eddy current data were obtained using a single driver with an array of eight pick-up coils configured for inspection of Alloy-800 SG tube fretting, accompanied by tube offset within a simulated corroding ferromagnetic support structure. Time-voltage data were processed by a modified principal component analysis (MPCA) to reduce data dimensionality, and MPCA scores were input into an ANN that simultaneously targeted four parameters associated with support structure hole size, tube off-centering in two dimensions, and fret depth. The neural network was trained, tested, and validated on experimental data and provided estimates within 2% of hole inner diameter (ID) and 3% of fret depth targets. The estimates of hole ID and tube position were further improved when fret depth was used as an input, as might occur if fret depth inspection results are available.

Simultaneous Multiparameter Measurement in Pulsed Eddy Current Steam Generator Data Using Artificial Neural Networks

A Bayesian approach to modeling and predicting pitting flaws in steam generator tubes

Degradation and fouling of support structures in nuclear steam generators (SGs) can lead to SG tube damage and loss of SG efficiency. Inspection and monitoring of support structures combined with preventative maintenance programs can alleviate these effects and extend SG life. Conventional eddy current inspection technologies are extensively used for detecting and sizing indications from wall loss, frets at supports, cracks and other degradation modes in the tubes, as well as assessing the condition of support structures. However, these methods have limited capabilities when more than one degradation mode is present simultaneously, or when combined with fouling. Pulsed eddy current combined with principal components analysis (PCA) was examined for inspection of 15.9 mm (5/ $8^{{\prime }{\prime }}$ ) Alloy-800 tubes and surrounding stainless steel (SS410) support structures. Clear separation of PCA scores associated with tubes from those associated with ferromagnetic SS410 supports permitted measurement of tube-to-support gaps, in either the presence of tube fretting or variation of relative position of the tube within SS410 supports. For concentric tubes, frets could be sized independently of SS410 hole diameter variations, which in other materials could represent support corrosion. Capability to clearly separate scores was attributed to large differences in relaxation times for diffusion of transient fields through the tube compared with diffusion into the ferromagnetic support structure.

C. Maruska

Papers

CANDU steam generator life management