Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Support vector machine in machine condition monitoring and fault diagnosis

Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO 2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.

A review of data-driven building energy consumption prediction studies

The current article provides a summary of automated fault detection and diagnostics studies published since 2004 that are relevant to the commercial buildings sector. The review updates a previous review conducted in 2004 and published in 2005, and it categorizes automated fault detection and diagnostics methods into three groups. The examples of automated fault detection and diagnostics in the primary category are selectively reviewed to identify various methods that are suitable for building systems and to understand the strengths and weaknesses of the methods. The distribution of studies based on each automated fault detection and diagnostics method and heating, ventilation, and air-conditioning system is also described. Researchers and industries can use the current article as a guideline for selecting an appropriate automated fault detection and diagnostics method.

A review of fault detection and diagnostics methods for building systems

Chillers constitute a significant portion of energy consumption equipment in heating, ventilating and air-conditioning (HVAC) systems. The growing complexity of building systems has become a major challenge for field technicians to troubleshoot the problems manually; this calls for automated ldquosmart-service systemsrdquo for performing fault detection and diagnosis (FDD). The focus of this paper is to develop a generic FDD scheme for centrifugal chillers and also to develop a nominal data-driven (ldquoblack-boxrdquo) model of the chiller that can predict the system response under new loading conditions. In this vein, support vector machines, principal component analysis, and partial least squares are the candidate fault classification techniques in our approach. We present a genetic algorithm-based approach to select a sensor suite for maximum diagnosabilty and also evaluated the performance of selected classification procedures with the optimized sensor suite. The responses of these selected sensors are predicted under new loading conditions using the nominal model developed via the black-box modeling approach. We used the benchmark data on a 90-t real centrifugal chiller test equipment, provided by the American Society of Heating, Refrigerating and Air-Conditioning Engineers, to demonstrate and validate our proposed diagnostic procedure. The database consists of data from sixty four monitored variables of the chiller under 27 different modes of operation during nominal and eight faulty conditions with different severities.

Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers

Imperfect test outcomes, due to factors such as unreliable sensors, electromagnetic interference, and environmental conditions, manifest themselves as missed detections and false alarms. This paper develops near-optimal algorithms for dynamic multiple fault diagnosis (DMFD) problems in the presence of imperfect test outcomes. The DMFD problem is to determine the most likely evolution of component states, the one that best explains the observed test outcomes. Here, we discuss four formulations of the DMFD problem. These include the deterministic situation corresponding to perfectly observed coupled Markov decision processes to several partially observed factorial hidden Markov models ranging from the case where the imperfect test outcomes are functions of tests only to the case where the test outcomes are functions of faults and tests, as well as the case where the false alarms are associated with the nominal (fault free) case only. All these formulations are intractable NP-hard combinatorial optimization problems. Our solution scheme can be viewed as a two-level coordinated solution framework for the DMFD problem. At the top (coordination) level, we update the Lagrange multipliers (coordination variables, dual variables) using the subgradient method. At the bottom level, we use a dynamic programming technique (specifically, the Viterbi decoding or Max-sum algorithm) to solve each of the subproblems, one for each component state sequence. The key advantage of our approach is that it provides an approximate duality gap, which is a measure of the suboptimality of the DMFD solution. Computational results on real-world problems are presented. A detailed performance analysis of the proposed algorithm is also discussed.

/pdf/dynamic-multiple-fault-diagnosis-mathematical-formulations-2gr5a984mr.pdf

Dynamic Multiple Fault Diagnosis: Mathematical Formulations and Solution Techniques

Faulty automotive systems significantly degrade the performance and efficiency of vehicles and are often major contributors of vehicle breakdown; they result in large expenditures for repair and maintenance. Therefore, intelligent vehicle health-monitoring schemes are needed for effective fault diagnosis in automotive systems. Previously, we developed a data-driven approach using a data-reduction technique, coupled with a variety of classifiers, for fault diagnosis in automotive systems. In this paper, we consider the problem of fusing classifier decisions to reduce diagnostic errors. Specifically, we develop three novel classifier fusion approaches: 1. class-specific Bayesian fusion; 2. joint optimization of the fusion center and individual classifiers; and 3. dynamic fusion. We evaluate the efficacies of these fusion approaches on an automotive engine data. The results demonstrate that the proposed fusion techniques outperform traditional fusion approaches. We also show that learning the parameters of individual classifiers as part of the fusion architecture can provide better classification performance.

/pdf/novel-classifier-fusion-approaches-for-fault-diagnosis-in-dfifjvzmt3.pdf

Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems

In this article, we consider a data-driven approach for fault detection and isolation (FDI) of chillers in HVAC systems. To diagnose the faults of interest in the chiller, we employ multiway dynamic principal component analysis (MPCA), multiway partial least squares (MPLS), and support vector machines (SVMs). The simulation of a chiller under various fault conditions is conducted using a standard chiller simulator from the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE). We validated our FDI scheme using experimental data obtained from different types of chiller faults.

Fault diagnosis in HVAC chillers

Faulty automotive systems significantly degrade the performance and efficiency of vehicles, and oftentimes are the major contributors of vehicle breakdown; they result in large expenditures for repair and maintenance. Therefore, intelligent vehicle health-monitoring schemes are needed for effective fault diagnosis in automotive systems. Previously, we developed a data-driven approach using a data reduction technique, coupled with a variety of classifiers, for fault diagnosis in automotive systems. In this paper, we consider the problem of fusing classifier decisions to reduce diagnostic errors. Specifically, we develop three novel classifier fusion approaches: class-specific Bayesian fusion, joint optimization of fusion center and of individual classifiers, and dynamic fusion. We evaluate the efficacies of these fusion approaches on an automotive engine data. The results demonstrate that dynamic fusion and joint optimization, and class-specific Bayesian fusion outperform traditional fusion approaches. We also show that learning the parameters of individual classifiers as part of the fusion architecture can provide better classification performance.

/pdf/novel-classifier-fusion-approahces-for-fault-diagnosis-in-2d5m47qeha.pdf

Kihoon Choi

Papers

Data-Driven Modeling, Fault Diagnosis and Optimal Sensor Selection for HVAC Chillers

Dynamic Multiple Fault Diagnosis: Mathematical Formulations and Solution Techniques

Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems

Fault diagnosis in HVAC chillers

Novel classifier fusion approahces for fault diagnosis in automotive systems