Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Friction, Vibration and Dynamic Properties of Transmission System under Wear Progression

This reprint focuses on wear and fatigue analysis, the dynamic properties of coating surfaces in transmission systems, and non-destructive condition monitoring for the health management of transmission systems. Transmission systems play a vital role in various types of industrial structure, including wind turbines, vehicles, mining and material-handling equipment, offshore vessels, and aircrafts. Surface wear is an inevitable phenomenon during the service life of transmission systems (such as on gearboxes, bearings, and shafts), and wear propagation can reduce the durability of the contact coating surface. As a result, the performance of the transmission system can degrade significantly, which can cause sudden shutdown of the whole system and lead to unexpected economic loss and accidents. Therefore, to ensure adequate health management of the transmission system, it is necessary to investigate the friction, vibration, and dynamic properties of its contact coating surface and monitor its operating conditions

EV fleet charging load forecasting based on multiple decomposition with CEEMDAN and swarm decomposition.

As the transition to electric mobility is accelerating, EV fleet charging loads are expected to become increasingly significant for power systems. Hence, EV fleet load forecasting is vital to maintaining the reliability and safe operation of the power system. This paper presents a new multiple decomposition based hybrid forecasting model for EV fleet charging. The proposed approach incorporates the Swarm Decomposition (SWD) into the Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) method. The multiple decomposition approach offers more stable, stationary, and regular features of the original signals. Each decomposed signal is fed into artificial intelligence-based forecasting models including multi-layer perceptron (MLP), long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM). Real EV fleet charging data sets from the field are used to validate the performance of the models. Various statistical metrics are used to quantify the prediction performance of the proposed model through a comparative analysis of the implemented models. It is demonstrated that the multiple decomposition approach improved the model performance with an R2 value increasing from 0.8564 to 0.9766 as compared to the models with single decomposition

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

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

Machine Learning in Tribology

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology

A Digital Triplet for Utilizing Offline Environments to Train Condition Monitoring Systems for Rolling Element Bearings

Manufacturing competitiveness is related to making a quality product while incurring the lowest costs. Unexpected downtime caused by equipment failure negatively impacts manufacturing competitiveness due to the ensuing defects and delays caused by the downtime. Manufacturers have adopted condition monitoring (CM) techniques to reduce unexpected downtime to augment maintenance strategies. The CM adoption has transitioned maintenance from Breakdown Maintenance (BM) to Condition-Based Maintenance (CbM) to anticipate impending failures and provide maintenance actions before equipment failure. CbM is the umbrella term for maintenance strategies that use condition monitoring techniques such as Preventive Maintenance (PM) and Predictive Maintenance (PdM). Preventive Maintenance involves providing periodic checks based on either time or sensory input. Predictive Maintenance utilizes continuous or periodic sensory inputs to determine the machine health state to predict the equipment failure. The overall goal of the work is to improve bearing diagnostic and prognostic predictions for equipment health by utilizing surrogate systems to generate failure data that represents production equipment failure, thereby providing training data for condition monitoring solutions without waiting for real world failure data. This research seeks to address the challenges of obtaining failure data for CM systems by incorporating a third system into monitoring strategies to create a Digital Triplet (DTr) for condition monitoring to increase the amount of possible data for condition monitoring. Bearings are a critical component in rotational manufacturing systems with wide application to other industries outside of manufacturing, such as energy and defense. The reinvented DTr system considers three components: the physical, surrogate, and digital systems. The physical system represents the real-world application in production that cannot fail. The surrogate system represents a physical component in a test system in an offline environment where data is generated to fill in gaps from data unavailable in the real-world system. The digital system is the CM system, which provides maintenance recommendations based on the ingested data from the real world and surrogate systems. In pursuing the research goal, a comprehensive bearing dataset detailing these four failure modes over different collection operating parameters was created. Subsequently, the collections occurred under different operating conditions, such as speed-varying, load-varying, and steadystate. Different frequency and time measures were used to analyze and identify differentiating criteria between the different failure classes over the differing operating conditions. These empirical observations were recreated using simulations to filter out potential outliers. The outputs of the physical model were combined with knowledge from the empirical observations to create ”spectral deltas” to augment existing bearing data and create new failure data that resemble similar frequency criteria to the original data. The primary verification occurred on a laboratory-bearing test stand. A conjecture is provided on how to scale to a larger system by analyzing a larger system from a local manufacturer. From the subsequent analysis of machine learning diagnosis and prognosis models, the original and augmented bearing data can complement each other during model training. The subsequent data substitution verifies that bearing data collected under different operating conditions and sizes can be substituted between different systems. Ostensibly, the full formulation of the digital triplet system is that bearing data generated at a smaller size can be scaled to train predictive failure models for larger bearing sizes. Future work should consider implementing this method for other systems outside of bearings, such as gears, non-rotational equipment, such as pumps, or even larger complex systems, such as computer numerically controlled machine tools or car engines. In addition, the method and process should not be restricted to only mechanical systems and could be applied to electrical systems, such as batteries. Furthermore, an investigation should consider further data-driven approximations to specific bearing characteristics related to the stiffness and damping parameters needed in modeling. A final consideration is for further investigation into the scalability quantities within the data and how to track these changes through different system levels

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

Applied Mathematics to Mechanisms and Machines

This book brings together all 16 articles published in the Special Issue "Applied Mathematics to Mechanisms and Machines" of the MDPI Mathematics journal, in the section “Engineering Mathematics”. The subject matter covered by these works is varied, but they all have mechanisms as the object of study and mathematics as the basis of the methodology used. In fact, the synthesis, design and optimization of mechanisms, robotics, automotives, maintenance 4.0, machine vibrations, control, biomechanics and medical devices are among the topics covered in this book. This volume may be of interest to all who work in the field of mechanism and machine science and we hope that it will contribute to the development of both mechanical engineering and applied mathematics