F. Di Maio

Ensemble-approaches for clustering health status of oil sand pumps

Centrifugal slurry pumps are widely used in the oil sand industry, mining, ore processing, waste treatment, cement production, and other industries to move mixtures of solids and liquids. Wear of slurry pump components, caused by abrasive and erosive solid particles, is one of the main causes of reduction in the efficiency and useful life of these pumps. This leads to unscheduled outages that cost companies millions of dollars each year. Traditional maintenance strategies can be applied, but they provide insufficient warning of impending failures. On the other hand, condition monitoring and on-line assessment of the wear status of wetted components in slurry pumps are expected to improve maintenance management and generate significant cost savings for pump operators. In this context, the objective of the present work is to develop and compare two unsupervised clustering ensemble methods, i.e., fuzzy C-means and hierarchical trees, for the assessment and measurement of the wear status of slurry pumps when available data is extremely limited. The idea is to combine predictions of multiple classifiers to reduce the variance of the results so that they are less dependent on the specifics of a single classifier. This will also reduce the variance of the bias, because a combination of multiple classifiers may learn a more expressive concept class than a single classifier.

Fuzzy C-Means Clustering of Signal Functional Principal Components for Post-Processing Dynamic Scenarios of a Nuclear Power Plant Digital Instrumentation and Control System

This paper addresses the issue of the classification of accident scenarios generated in a dynamic safety and reliability analyses of a Nuclear Power Plant (NPP) equipped with a Digital Instrumentation and Control system (I&C). More specifically, the classification of the final state reached by the system at the end of an accident scenario is performed by Fuzzy C-Means clustering the Functional Principal Components (FPCs) of selected relevant process variables. The approach allows capturing the characteristics of the process evolution determined by the occurrence, timing, and magnitudes of the fault events. An illustrative case study is considered, regarding the fault scenarios of the digital I&C system of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The results obtained are compared with those of the Kth Nearest Neighbor (KNN), and Classification and Regression Tree (CART) classifiers.

Reconstruction of missing data in multidimensional time series by fuzzy similarity

We address the problem of missing data in multidimensional time series.We propose a novel method based on a fuzzy similarity measure.The performance is compared with that of an Auto Associative Kernel Regression.The method is applied to shut-down transients of a Nuclear Power Plant (NPP) turbine. The present work addresses the problem of missing data in multidimensional time series such as those collected during operational transients in industrial plants. We propose a novel method for missing data reconstruction based on three main steps: (1) computing a fuzzy similarity measure between a segment of the time series containing the missing data and segments of reference time series; (2) assigning a weight to each reference segment; (3) reconstructing the missing values as a weighted average of the reference segments. The performance of the proposed method is compared with that of an Auto Associative Kernel Regression (AAKR) method on an artificial case study and a real industrial application regarding shut-down transients of a Nuclear Power Plant (NPP) turbine.

Fusion of artificial neural networks and genetic algorithms for multi-objective system reliability design optimization

In this work, artificial neural networks (ANNs) are used to include the decision-makers preference structure within a genetic algorithm (GA) search of the optimal system reliability configuration. For verification, the proposed approach is applied to two literature case studies of increasing complexity concerning the optimization of the reliability design of a series system.

Ensemble of unsupervised fuzzy C-Means classifiers for clustering health status of oil sand pumps

Detection of anomalies and faults in slurry pumps is an important task with implications for their safe, economical, and efficient operation. Wear, caused by abrasive and erosive solid particles, is one of the main causes of failure. Condition monitoring and on-line assessment of the wear status of wetted components in slurry pumps are expected to improve maintenance management and generate significant cost savings for pump operators. In this context, the objective of the present work is to present a framework for the assessment and measurement of the wear status of slurry pumps when available data is extremely limited. Four sequential steps are performed: data collection, feature extraction, feature selection and classification. The main idea is to combine the predictions of multiple unsupervised classifiers fed with different inputs taken from different signals, based on fuzzy C-means clustering, to reduce the variance of the results so that they are less dependent on the specifics of a single classifier. This will also reduce the variance of the bias, because a combination of multiple classifiers may learn a more expressive concept class than a single classifier.