Alessandro Beghi

Bio: Alessandro Beghi is an academic researcher from University of Padua. The author has contributed to research in topics: Model predictive control & HVAC. The author has an hindex of 27, co-authored 214 publications receiving 2813 citations.

Machine Learning for Predictive Maintenance: A Multiple Classifier Approach

Abstract: In this paper, a multiple classifier machine learning (ML) methodology for predictive maintenance (PdM) is presented. PdM is a prominent strategy for dealing with maintenance issues given the increasing need to minimize downtime and associated costs. One of the challenges with PdM is generating the so-called “health factors,” or quantitative indicators, of the status of a system associated with a given maintenance issue, and determining their relationship to operating costs and failure risk. The proposed PdM methodology allows dynamical decision rules to be adopted for maintenance management, and can be used with high-dimensional and censored data problems. This is achieved by training multiple classification modules with different prediction horizons to provide different performance tradeoffs in terms of frequency of unexpected breaks and unexploited lifetime, and then employing this information in an operating cost-based maintenance decision system to minimize expected costs. The effectiveness of the methodology is demonstrated using a simulated example and a benchmark semiconductor manufacturing maintenance problem.

A Predictive Maintenance System for Epitaxy Processes Based on Filtering and Prediction Techniques

Abstract: Silicon epitaxial deposition is a process strongly influenced by wafer temperature behavior, which has to be constantly monitored to avoid the production of defective wafers. However, temperature measurements are not reliable, and the sensors have to be appropriately calibrated with some dedicated procedure. A predictive maintenance (PdM) system is proposed with the aim of predicting process behavior and scheduling control actions on the sensors in advance. Two different prediction techniques have been employed and compared: the Kalman predictor and the particle filter with Gaussian kernel density estimator. The accuracy of the PdM module has been tested on real industrial production datasets.

A multi-phase genetic algorithm for the efficient management of multi-chiller systems

Abstract: In HVAC plants of medium-high cooling capacity, multiple-chiller systems are often employed. In such systems, chillers are independent of each other to provide standby capacity, operational flexibility, and less disruption maintenance. However, the problem of efficiently managing multiple-chiller systems is complex in many respects. In particular, the electrical energy consumption in the chiller plant markedly increases if the chillers are managed improperly, therefore significant energy savings can be achieved by optimizing the chiller operation of HVAC systems. In this paper an unified method for Multi-Chiller Management optimization is presented, that deals simultaneously with the Optimal Chiller Loading and Optimal Chiller Sequencing problems. The main objective is that of reducing both power consumption and operative costs. The approach is based on a cooling load estimation algorithm, and the optimization step is performed by means of a multi-phase genetic algorithm, that provides an efficient and suitable approach to solve this kind of complex multi-objective optimization problem. The performance of the algorithm is evaluated by resorting to a dynamic simulation environment developed in Matlab/Simulink, where the plant dynamics are accurately described. It is shown that the proposed algorithm gives superior performance with respect to standard approaches, in terms of energy performance.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.