9 research outputs found
A new transient field balancing method of a rotor system based on empirical mode decomposition
Effective reduction of the vibration in rotor and stator at critical speed is important for steady operation of rotor systems. A new transient field balancing method is proposed in this paper. The empirical mode decomposition (EMD) method coupled with holospectral technique is used to extract rotating frequency information including precise frequency, amplitude and phase nearby the critical speed from the run-up vibration signals. Reasonable trial weights are selected through estimating the unbalance masses and position. Moreover, the correction masses and position are obtained by holo-balancing method. Compared with the traditional dynamic balancing method, this method does not need obtain steady-state vibration signals, and the rotor can pass through the critical speed smoothly. The principle and detailed procedures of this method are described in this paper, and the effectiveness of the new method was validated by field balancing of rotor kit system
Concurrent solution of WATC scheduling with WPPW due date assignment for environmentally weighted customers, jobs and services using SA and its hybrid
After industrial revolution environmental problems increased drastically. Air, water and soil pollution became a serious threat for the mankind. In order to overcome this threat everyone should take responsibility and try to preserve environment as much as possible. Environmentally conscious actions, people, law and foundations should be supported. When it came to determining due dates and scheduling, one of the important criteria should be the supporting the environment. In this study environmentally conscious customers, jobs, and services are rewarded, on the other hand unconscious customers, jobs, and services are penalized, while determining due dates and schedules. Simulated annealing and its hybrid with random search are applied to get environmentally better due dates and schedules
Improved railway vehicle inspection and monitoring through the integration of multiple monitoring technologies
The effectiveness and efficiency of railway vehicle condition monitoring is increasingly critical to railway operations as it directly affects safety, reliability, maintenance efficiency, and overall system performance. Although there are a vast number of railway vehicle condition monitoring technologies, wayside systems are becoming increasingly popular because of the reduced cost of a single monitoring point, and because they do not interfere with the existing railway line. Acoustic sensing and visual imaging are two wayside monitoring technologies that can be applied to monitor the condition of vehicle components such as roller bearing, gearboxes, couplers, and pantographs, etc. The central hypothesis of this thesis is that it is possible to integrate acoustic sensing and visual imaging technologies to achieve enhancement in condition monitoring of railway vehicles. So this thesis presents improvements in railway vehicle condition monitoring through the integration of acoustic sensing and visual imaging technologies
Applicability and Interpretability of Logical Analysis of Data in Condition Based Maintenance
Résumé
Cette thĂšse Ă©tudie lâapplicabilitĂ© et lâadaptabilitĂ© dâune approche dâexploration de donnĂ©es basĂ©e sur lâintelligence artificielle proposĂ©e dans [Hammer, 1986] et appelĂ©e analyse logique de donnĂ©es (LAD) aux applications diagnostiques dans le domaine de la maintenance conditionnelle CBM). La plupart des technologies utilisĂ©es Ă ce jour pour la prise de dĂ©cision dans la maintenance conditionnelle ont tendance Ă automatiser le processus de diagnostic, sans offrir aucune connaissance ajoutĂ©e qui pourrait ĂȘtre utile Ă lâopĂ©ration de maintenance et au personnel de maintenance. Par comparaison Ă dâautres techniques de prise de dĂ©cision dans le domaine de
la CBM, la LAD possĂšde deux avantages majeurs : (1) il sâagit dâune approche non statistique, donc les donnĂ©es nâont pas Ă satisfaire des suppositions statistiques et (2) elle gĂ©nĂšre des formes interprĂ©tables qui pourraient aider Ă rĂ©soudre les problĂšmes de maintenance. Une Ă©tude sur
lâapplication de la LAD dans la maintenance conditionnelle est prĂ©sentĂ©e dans cette recherche dont lâobjectif est (1) dâĂ©tudier lâapplicabilitĂ© de la LAD dans des situations diffĂ©rentes qui nĂ©cessitent des considĂ©rations particuliĂšres concernant les types de donnĂ©es dâentrĂ©e et les dĂ©cisions de maintenance, (2) dâadapter la mĂ©thode LAD aux exigences particuliĂšres qui se posent Ă partir de ces applications et (3) dâamĂ©liorer la mĂ©thodologie LAD afin dâaugmenter lâexactitude de diagnostic et dâinterprĂ©tation de rĂ©sultats.
Les aspects innovants de la recherche prĂ©sentĂ©s dans cette thĂšse sont (1) lâapplication de la LAD dans la CBM pour la premiĂšre fois dans des applications qui bĂ©nĂ©ficient des propriĂ©tĂ©s uniques de cette technologie et (2) les modifications innovatrices de la mĂ©thodologie de la LAD, en
particulier dans le domaine de la gĂ©nĂ©ration des formes, afin dâamĂ©liorer ses performances dans le cadre de la CBM et dans le domaine de classification multiclasses.
La recherche menĂ©e dans cette thĂšse a suivi une approche Ă©volutive afin dâatteindre les objectifs
Ă©noncĂ©s ci-dessus. La LAD a Ă©tĂ© utilisĂ©e et adaptĂ©e Ă trois applications : (1) la dĂ©tection des composants malveillants (Rogue) dans lâinventaire de piĂšces de rechange rĂ©parables dâune compagnie aĂ©rienne commerciale, (2) la dĂ©tection et lâidentification des dĂ©fauts dans les transformateurs de puissance en utilisant la DGA et (3) la dĂ©tection des dĂ©fauts dans les rotors en utilisant des signaux de vibration. Cette recherche conclut que la LAD est une approche de prise de dĂ©cision prometteuse qui ajoute dâimportants avantages Ă la mise en oeuvre de la CBM dans
lâindustrie.----------Abstract
This thesis studies the applicability and adaptability of a data mining artificial intelligence
approach called Logical Analysis of Data (LAD) to diagnostic applications in Condition Based
Maintenance (CBM). Most of the technologies used so far for decision support in CBM tend to
automate the diagnostic process without offering any added knowledge that could be helpful to
the maintenance operation and maintenance personnel. LAD possesses two key advantages over
other decision making technologies used in CBM: (1) it is a non-statistical approach; as such no
statistical assumptions are required for the input data, and (2) it generates interpretable patterns
that could help solve maintenance problems. A study on the implementation of LAD in CBM is
presented in this research whose objective are to study the applicability of LAD in different CBM
situations requiring special considerations regarding the types of input data and maintenance
decisions, adapt the LAD methodology to the particular requirements that arise from these
applications, and improve the LAD methodology in line with the above two objectives in order to
increase diagnosis accuracy and result interpretability.
The novelty of the research presented in this thesis is (1) the application of LAD to CBM for the
first time in applications that stand to benefit from the advantages that this technology provides;
and (2) the innovative modifications to LAD methodology, particularly in the area of pattern
generation, in order to improve its performance within the context of CBM.
The research conducted in this thesis followed an evolutionary approach in order to achieve the
objectives stated in the Introduction. The research applied LAD in three applications: (1) the
detection of Rogue components within the spare part inventory of reparable components in a
commercial airline company, (2) the detection and identification of faults in power transformers
using DGA, and (3) the detection of faults in rotor bearings using vibration signals. This research
concludes that LAD is a promising decision making approach that adds important benefits to the
implementation of CBM in the industry