Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Aluminium Process Fault Detection and Diagnosis

The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation. Each element is explained together with examples of existing systems. A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data. A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators’ view of an industrial plant, so that it permits a situation-oriented action in real working environments

HYDRODYNAMIC AND MASS TRANSFER PARAMETERS IN LARGE-SCALE SLURRY BUBBLE COLUMN REACTORS

The design, modeling, optimization and scaleup of slurry bubble column reactors (SBCRs) require, among others, the knowledge of the kinetics, hydrodynamics, and mass as well as heat transfer characteristics in larger-scale reactors, operating under typical industrial conditions. In this study, the hydrodynamic (gas holdup, ƒÕG, bubble size distribution, dB, and the Sauter-mean bubble diameter, d32), gas solubility (C*) and mass parameters (gas-liquid interfacial area, a, and volumetric liquid-side mass transfer coefficient, kLa) were measured for various gases (H2, CO, N2, CH4 and He) in an organic liquid (Isopar-M) in the absence and presence of two different solids (glass beads and alumina powder) in two large-scale SBCRs. The data for the five gases were obtained in a cold SBCR (0.301 m ID) under wide ranges pressures (P = 1-8 bar), temperatures (T = 293-305 K), superficial gas velocities (UG = 0.08-0.20 m/s), and solid concentrations (CV = 0-36 vol.%); and the data for He and N2 were obtained in a hot SBCR (0.29 m ID) under wide ranges pressures (P = 7-25 bar), temperatures (T = 323-453 K), superficial gas velocities (UG = 0.08-0.30 m/s), and solid concentrations (CV =0-20 vol.%). All the experiments and the operating ranges were selected following the Central Composite Statistical Design (CCSD) approach. The experimental data obtained showed that the gas holdup and volumetric liquid-side mass transfer coefficients increased with pressure due to the increase of small gas bubbles holdup; increased with superficial gas velocity due to the increase of the gas momentum; and significantly decreased with solid concentration due to a reduction of small gas bubble population. The gas holdup and volumetric liquid-side mass transfer coefficients were found to increase with temperature due to the decrease of the Sauter mean bubble diameter and increase of the mass transfer coefficient (kL). The gas holdup, however, was found to decrease with temperature when the solid concentration was greater or equal 15 vol.% due to the reduction of froth stability under such conditions.Empirical and back propagation neural network (BPNN) models were developed to correlate the hydrodynamic and mass transfer parameters in BCRs and SBCRs obtained in our laboratory and those from the literature. The developed models were then used to predict the effects of pressure, superficial gas velocity, temperature and catalyst loading on the total syngas holdup and mass transfer coefficients for the Low-Temperature Fischer-Tropsch (LTFT) synthesis carried out in a 5 m ID SBCR with iron oxides and cobalt-based catalysts. The predicted total syngas holdup and mass transfer coefficients appeared to increase with reactor pressure, superficial gas velocity and the number of orifices in the gas sparger. The predicted values, however, were found to decrease with catalyst loading and reactor temperature. Also, under similar LTFT operating conditions (P = 30 bar, T = 513 K, CW = 30 and 50 wt%), the total syngas holdup and mass transfer coefficients predicted for H2/CO ratio of 2:1 with cobalt-based catalyst were consistently lower than those obtained for H2/CO ratio of 1:1 with iron oxide catalyst in the superficial gas velocity range from 0.005 to 0.4 m/s

Artificial Intelligence Application in Machine Condition Monitoring and Fault Diagnosis

The subject of machine condition monitoring and fault diagnosis as a part of system maintenance has gained a lot of interest due to the potential benefits to be learned from reduced maintenance budgets, enhanced productivity and improved machine availability. Artificial intelligence (AI) is a successful method of machine condition monitoring and fault diagnosis since these techniques are used as tools for routine maintenance. This chapter attempts to summarize and review the recent research and developments in the field of signal analysis through artificial intelligence in machine condition monitoring and fault diagnosis. Intelligent systems such as artificial neural network (ANN), fuzzy logic system (FLS), genetic algorithms (GA) and support vector machine (SVM) have previously developed many different methods. However, the use of acoustic emission (AE) signal analysis and AI techniques for machine condition monitoring and fault diagnosis is still rare. In the future, the applications of AI in machine condition monitoring and fault diagnosis still need more encouragement and attention due to the gap in the literature

A stable and accurate control-volume technique based on integrated radial basis function networks for fluid-flow problems

Radial basis function networks (RBFNs) have been widely used in solving partial differential equations as they are able to provide fast convergence. Integrated RBFNs have the ability to avoid the problem of reduced convergence-rate caused by differentiation. This paper is concerned with the use of integrated RBFNs in the context of control-volume discretisations for the simulation of fluid-flow problems. Special attention is given to (i) the development of a stable high-order upwind scheme for the convection term and (ii) the development of a local high-order approximation scheme for the diffusion term. Benchmark problems including the lid-driven triangular-cavity flow are employed to validate the present technique. Accurate results at high values of the Reynolds number are obtained using relatively-coarse grids

Real-Time Gas Identification by Analyzing the Transient Response of Capillary-Attached Conductive Gas Sensor

In this study, the ability of the Capillary-attached conductive gas sensor (CGS) in real-time gas identification was investigated. The structure of the prototype fabricated CGS is presented. Portions were selected from the beginning of the CGS transient response including the first 11 samples to the first 100 samples. Different feature extraction and classification methods were applied on the selected portions. Validation of methods was evaluated to study the ability of an early portion of the CGS transient response in target gas (TG) identification. Experimental results proved that applying extracted features from an early part of the CGS transient response along with a classifier can distinguish short-chain alcohols from each other perfectly. Decreasing time of exposition in the interaction between target gas and sensing element improved the reliability of the sensor. Classification rate was also improved and time of identification was decreased. Moreover, the results indicated the optimum interval of the early transient response of the CGS for selecting portions to achieve the best classification rates

Deterministic Artificial Intelligence

Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book

Gas Turbines

This book is intended to provide valuable information for the analysis and design of various gas turbine engines for different applications. The target audience for this book is design, maintenance, materials, aerospace and mechanical engineers. The design and maintenance engineers in the gas turbine and aircraft industry will benefit immensely from the integration and system discussions in the book. The chapters are of high relevance and interest to manufacturers, researchers and academicians as well

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field