Purpose
– Developing an optimized project schedule that considers all decision criteria represents a challenge for project managers. The purpose of this paper is to provide a multi-objectives overall optimization model for project scheduling considering time, cost, resources, and cash flow. This development aims to overcome the limitations of optimizing each objective at once resulting of non-overall optimized schedule.
Design/methodology/approach
– In this paper, a multi-objectives overall optimization model for project scheduling is developed using particle swarm optimization with a new evolutionary strategy based on the compromise solution of the Pareto-front. This model optimizes the most important decisions that affect a given project including: time, cost, resources, and cash flow. The study assumes each activity has different execution methods accompanied by different time, cost, cost distribution pattern, and multiple resource utilization schemes.
Findings
– Applying the developed model to schedule a real-life case study project proves that the proposed model is valid in modeling real-life construction projects and gives important results for schedulers and project managers. The proposed model is expected to help construction managers and decision makers in successfully completing the project on time and reduced budget by utilizing the available information and resources.
Originality/value
– The paper presented a novel model that has four main characteristics: it produces an optimized schedule considering time, cost, resources, and cash flow simultaneously; it incorporates a powerful particle swarm optimization technique to search for the optimum schedule; it applies multi-objectives optimization rather than single-objective and it uses a unique Pareto-compromise solution to drive the fitness calculations of the evolutionary process.
This article presents a method for fault detection and diagnosis of stator inter-turn short circuit in three phase induction machines. The technique is based on modelling the motor in the dq frame for both health and fault cases to facilitate recognition of motor current. Using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to provide an efficient fault diagnosis tool. An artificial intelligence network determines the fault severity values using the stator current history. The performance of the developed fault analysis method is investigated using Matlab/Simulink® software. Stator turns faults are detected through current monitoring of a 2 Hp three phase induction motor under various loading conditions. Fault history is calculated under various loading conditions, and a wide range of fault severity.
The use of inducIion motors is erlensive in induslry. The working conditions of these moIors make Ihem subject lo mnnj fouh. These fauUs musI be defecled in on eorb stage before Bey lead lo calasfrophic failures. This poper presents o scheme for detecting inter-Iurn fouh in Ihe smar windings of induction mowrs ond esholing h e fouU sever& Delechbn of incipient inter-turn faults prevents further insulofion foilure. The proposed oigorithm monitors Ihe spechi content of sfnlor currents Io deled Ihe fouk After IhefouU is deIecfedund identified, o parjick swom o p p m h is used lo esrimate Ihe fauU severily. The swom esfirnntor updafe i s baed on the error bemeen fhe measured dofa ond a compkte model oflhe foul@ molor. An experimed setup is used to voiidale the deveioped scheme and lo i m p h e n t on oniinefouU defector.
L IntroductionInduction motors are the most important electric machinery in all the fields of industry. Their role in industry increased after the development of adjustable speed drives. Their low prices, ruggedness and efficiency make them attractive in a variety of applications. These motors are exposed to many loading and environmental conditions. This, acting together with the natural aging of the motor may lead to many failures [l]. Hence, monitoring the motor condition is crucial to detect any fault in an early stage eliminating the hazards of severe motor faults. Faults can be treated before totally damaging the machine and consequently that will reduce the maintenance cost and shutdown time. Thus, there is a growing need for a simple, reliable technique to detect incipient faults in an online mode.The stator inter-tun short circuit is one of the most common motor failures [21. It arises from insulation degradation. The insulation between different turns starts to breakdown resulting in a closed path for circulating currents. This produces thermal hotspots leading to progressive degradation that eventually can grow to a complete tnm-to-ground fault. One of its major detection problems is that it does not affect the performance of the motor noticeably in its early stages so there might not be any indication of such a growing fault but eventually it can lead to a catastrophic insulation failure. Several schemes for detecting inter turn faults were proposed.In [3], a fault detection scheme is based on measuring the negative sequence impedance. Monitoring the high order spectra of the radial machine vibration is proposed [4]. Detection based on measuring the line to neutral voltage was proposed in [5], but it is limited to star connected machines with an accessible neutral. Monitoring current harmonics is proposed in several schemes [6,7,8]. Although estimation of the number of faulty NmS (fault severity) and/or locating the faulty turns can increase the speed of repair and permit more optimal scheduling of the repair outage , no much results has been reported in this area. In [91 the occurrence of inter-turn fault is detected and its position within the winding is located by monitoring the axial ...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.