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

Technology 2002: the Third National Technology Transfer Conference and Exposition, Volume 1

The proceedings from the conference are presented. The topics covered include the following: computer technology, advanced manufacturing, materials science, biotechnology, and electronics

Fault Prognostics Using Logical Analysis of Data and Non-Parametric Reliability Estimation Methods

RÉSUMÉ : Estimer la durée de vie utile restante (RUL) d’un système qui fonctionne suivant différentes conditions de fonctionnement représente un grand défi pour les chercheurs en maintenance conditionnelle (CBM). En effet, il est difficile de comprendre la relation entre les variables qui représentent ces conditions de fonctionnement et la RUL dans beaucoup de cas en pratique à cause du degré élevé de corrélation entre ces variables et leur dépendance dans le temps. Il est également difficile, voire impossible, pour des experts d’acquérir et accumuler un savoir à propos de systèmes complexes, où l'échec de l'ensemble du système est vu comme le résultat de l'interaction et de la concurrence entre plusieurs modes de défaillance. Cette thèse présente des méthodologies pour le pronostic en CBM basé sur l'apprentissage automatique, et une approche de découverte de connaissances appelée Logical Analysis of Data (LAD). Les méthodologies proposées se composent de plusieurs implémentations de la LAD combinées avec des méthodes non paramétriques d'estimation de fiabilité. L'objectif de ces méthodologies est de prédire la RUL du système surveillé tout en tenant compte de l'analyse des modes de défaillance uniques ou multiples. Deux d’entre elles considèrent un mode de défaillance unique et une autre considère de multiples modes de défaillance. Les deux méthodologies pour le pronostic avec mode unique diffèrent dans la manière de manipuler les données. Les méthodologies de pronostique dans cette recherche doctorale ont été testées et validées sur la base d'un ensemble de tests bien connus. Dans ces tests, les méthodologies ont été comparées à des techniques de pronostic connues; le modèle à risques proportionnels de Cox (PHM), les réseaux de neurones artificiels (ANNs) et les machines à vecteurs de support (SVMs). Deux ensembles de données ont été utilisés pour illustrer la performance des trois méthodologies: l'ensemble de données du turboréacteur à double flux (turbofan) qui est disponible au sein de la base de données pour le développement d'algorithmes de pronostic de la NASA, et un autre ensemble de données obtenu d’une véritable application dans l'industrie. Les résultats de ces comparaisons indiquent que chacune des méthodologies proposées permet de prédire avec précision la RUL du système considéré. Cette recherche doctorale conclut que l’approche utilisant la LAD possède d’importants mérites et avantages qui pourraient être bénéfiques au domaine du pronostic en CBM. Elle est capable de gérer les données en CBM qui sont corrélées et variantes dans le temps. Son autre avantage et qu’elle génère un savoir interprétable qui est bénéfique au personnel de maintenance.----------ABSTRACT : Estimating the remaining useful life (RUL) for a system working under different operating conditions represents a big challenge to the researchers in the condition-based maintenance (CBM) domain. The reason is that the relationship between the covariates that represent those operating conditions and the RUL is not fully understood in many practical cases, due to the high degree of correlation between such covariates, and their dependence on time. It is also difficult or even impossible for the experts to acquire and accumulate the knowledge from a complex system, where the failure of the system is regarded as the result of interaction and competition between several failure modes. This thesis presents systematic CBM prognostic methodologies based on a pattern-based machine learning and knowledge discovery approach called Logical Analysis of Data (LAD). The proposed methodologies comprise different implementations of the LAD approach combined with non-parametric reliability estimation methods. The objective of these methodologies is to predict the RUL of the monitored system while considering the analysis of single or multiple failure modes. Three different methodologies are presented; two deal with single failure mode and one deals with multiple failure modes. The two methodologies for single mode prognostics differ in the way of representing the data. The prognostic methodologies in this doctoral research have been tested and validated based on a set of widely known tests. In these tests, the methodologies were compared to well-known prognostic techniques; the proportional hazards model (PHM), artificial neural networks (ANNs) and support vector machines (SVMs). Two datasets were used to illustrate the performance of the three methodologies: the turbofan engine dataset that is available at NASA prognostic data repository, and another dataset collected from a real application in the industry. The results of these comparisons indicate that each of the proposed methodologies provides an accurate prediction for the RUL of the monitored system. This doctoral research concludes that the LAD approach has attractive merits and advantages that add benefits to the field of prognostics. It is capable of dealing with the CBM data that are correlated and time-varying. Another advantage is its generation of an interpretable knowledge that is beneficial to the maintenance personnel

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

Diesel Engine

Diesel engines, also known as CI engines, possess a wide field of applications as energy converters because of their higher efficiency. However, diesel engines are a major source of NOX and particulate matter (PM) emissions. Because of its importance, five chapters in this book have been devoted to the formulation and control of these pollutants. The world is currently experiencing an oil crisis. Gaseous fuels like natural gas, pure hydrogen gas, biomass-based and coke-based syngas can be considered as alternative fuels for diesel engines. Their combustion and exhaust emissions characteristics are described in this book. Reliable early detection of malfunction and failure of any parts in diesel engines can save the engine from failing completely and save high repair cost. Tools are discussed in this book to detect common failure modes of diesel engine that can detect early signs of failure

Special oils for halal and safe cosmetics

Three types of non conventional oils were extracted, analyzed and tested for toxicity. Date palm kernel oil (DPKO), mango kernel oil (MKO) and Ramputan seed oil (RSO). Oil content for tow cultivars of dates Deglect Noor and Moshkan was 9.67% and 7.30%, respectively. The three varieties of mango were found to contain about 10% oil in average. The red yellow types of Ramputan were found to have 11 and 14% oil, respectively. The phenolic compounds in DPKO, MKO and RSO were 0.98, 0.88 and 0.78 mg/ml Gallic acid equivalent, respectively. Oils were analyzed for their fatty acid composition and they are rich in oleic acid C18:1 and showed the presence of (dodecanoic acid) lauric acid C12:0, which reported to appear some antimicrobial activities. All extracted oils, DPKO, MKO and RSO showed no toxic effect using prime shrimp bioassay. Since these oils are stable, melt at skin temperature, have good lubricity and are great source of essential fatty acids; they could be used as highly moisturizing, cleansing and nourishing oils because of high oleic acid content. They are ideal for use in such halal cosmetics such as Science, Engineering and Technology 75 skin care and massage, hair-care, soap and shampoo products