Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book

Data-Driven Fault Detection and Reasoning for Industrial Monitoring

This open access book assesses the potential of data-driven methods in industrial process monitoring engineering. The process modeling, fault detection, classification, isolation, and reasoning are studied in detail. These methods can be used to improve the safety and reliability of industrial processes. Fault diagnosis, including fault detection and reasoning, has attracted engineers and scientists from various fields such as control, machinery, mathematics, and automation engineering. Combining the diagnosis algorithms and application cases, this book establishes a basic framework for this topic and implements various statistical analysis methods for process monitoring. This book is intended for senior undergraduate and graduate students who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications. This is an open access book

A Review of Kernel Methods for Feature Extraction in Nonlinear Process Monitoring

Kernel methods are a class of learning machines for the fast recognition of nonlinear patterns in any data set. In this paper, the applications of kernel methods for feature extraction in industrial process monitoring are systematically reviewed. First, we describe the reasons for using kernel methods and contextualize them among other machine learning tools. Second, by reviewing a total of 230 papers, this work has identified 12 major issues surrounding the use of kernel methods for nonlinear feature extraction. Each issue was discussed as to why they are important and how they were addressed through the years by many researchers. We also present a breakdown of the commonly used kernel functions, parameter selection routes, and case studies. Lastly, this review provides an outlook into the future of kernel-based process monitoring, which can hopefully instigate more advanced yet practical solutions in the process industries

Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions

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Recent advances in the theory and practice of logical analysis of data

Logical Analysis of Data (LAD) is a data analysis methodology introduced by Peter L. Hammer in 1986. LAD distinguishes itself from other classification and machine learning methods by the fact that it analyzes a significant subset of combinations of variables to describe the positive or negative nature of an observation and uses combinatorial techniques to extract models defined in terms of patterns. In recent years, the methodology has tremendously advanced through numerous theoretical developments and practical applications. In the present paper, we review the methodology and its recent advances, describe novel applications in engineering, finance, health care, and algorithmic techniques for some stochastic optimization problems, and provide a comparative description of LAD with well-known classification methods

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Signal and data processing for machine olfaction and chemical sensing: A review

Signal and data processing are essential elements in electronic noses as well as in most chemical sensing instruments. The multivariate responses obtained by chemical sensor arrays require signal and data processing to carry out the fundamental tasks of odor identification (classification), concentration estimation (regression), and grouping of similar odors (clustering). In the last decade, important advances have shown that proper processing can improve the robustness of the instruments against diverse perturbations, namely, environmental variables, background changes, drift, etc. This article reviews the advances made in recent years in signal and data processing for machine olfaction and chemical sensing

Impedance spectroscopy techniques for condition monitoring of polymer electrolyte membrane fuel cells

Energy continues to remain the spine of all human development. As we continue to make advances in various levels, the need for energy in quantity, and even more recently, quality, continues to increase. The fuel cell presents itself as a promising prospect to solve one of mankind’s current challenge - clean energy. The fuel cell is essentially an electrochemical conversion system which takes in fuel supply to produce electricity. Some key features make the fuel cell attractive as a power source. Firstly, its efficiency in practical applications is approximately 50% compared to the typical efficiency of 40% for a typical internal combustion engine [1]. Secondly, unlike the systems such as the internal combustion engine that typically releases carbon-monoxide which is a major greenhouse gas, the typical fuel cell system, produces just water and heat, alongside the useful electrical energy. These characteristics make it attractive as a clean energy supply capable of replacing the fossil-based supplies that are currently the mainstay. Unfortunately, the fuel cell is far cry from an ideal system. Despite significant advantages of the fuel cell as a power supply, various challenges still exist which have hindered its widespread acceptance and deployment. The fuel cell at its core is a highly multi-physics system and its operational intricacies makes it highly prone to a series of fault conditions. This begs the question of durability - an important requirement of a viable power source. Another challenge is the fact that humanity currently struggles with an efficient method of producing hydrogen which is the fuel of choice for the fuel cell. Given the promises of the fuel cell however, research efforts continue to increase to further improve its viability as an energy source competitive enough to meet mankind’s need of clean energy. This work presents results bordering on efficient diagnostic approaches for the fuel cell, aimed at improving the durability of the fuel cell. Particularly, two techniques targeted at improving the popular Electrochemical Impedance Spectroscopy (EIS) are presented. Conventional EIS takes significant amount of time, rendering it unsuitable for real-time diagnostics. Multi-frequency perturbation signals have been proposed to address this challenge. These however introduces concerns surrounding the accuracy of the resulting impedance measurement. Part of this work addresses some of the challenges with the fuel cell multi-sine impedance spectroscopy, such as measurement accuracy, by defining an optimized signal synthesis formulation. The proposed approach is validated in simulation and compared to the popular exponential frequency distribution approach using the appropriately defined error metric. Secondly, the chirp – as a frequency rich signal, is investigated as an alternative perturbation signal. Consequently, the use of the wavelet transform as an analysis tool of choice is presented. The characteristic nature of the chirp signal makes a broadband frequency sweep over time possible, hence enabling a faster impedance estimation. The resulting decomposition is harnessed for impedance calculation. The approach is tested in simulation and results for equivalent circuits are presented. It is shown that the resulting impedance spectrum well approximates the theoretical values. To further validate both techniques in practice, a low-cost active load is designed and built. The active load enables the injection of an arbitrary signal using the load modulation technique. The device is tested and benchmarked against commercial frequency response analyzer (FRA) using the conventional single sine EIS technique. Both approaches developed – the improved multi-sine scheme and the chirp signal perturbation are demonstrated with the aid of the active load on a single cell fuel cell station. Outcomes of the experiment show significant accuracy from the two techniques in comparison with results obtained from the FRA equipment which implements the single sine technique. In addition, the two schemes enabled impedance results to be taken in a few seconds, compared to conventional single sine EIS which takes several minutes. Impedance measurements are also carried out in the presence of two prominent faulty conditions – flooding and drying, using the developed techniques. This demonstrates the capability of the proposed system to perform real-time diagnostics of the PEMFC using impedance information

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science