The detection and identification of extraneous metals in material conveyance systems

The aim of this work was to design and build an equipment which can detect ferrous and non-ferrous objects in conveyed commodities, discriminate between them and locate the object along the belt and on the width of the belt. The magnetic induction mechanism was used as a means of achieving the objectives of this research. In order to choose the appropriate geometry and size of the induction field source, the field distributions of different source geometries and sizes were studied in detail. From these investigations it was found the square loop geometry is the most appropriate as a field generating source for the purpose of this project. The phenomena of field distribution in the conductors was also investigated. An equipment was designed and built at the preliminary stages of thework based on a flux-gate magnetometer with the ability to detect only ferrous objects.The instrument was designed such that it could be used to detect ferrous objects in the coal conveyors of power stations. The advantages of employing this detector in the power industry over the present ferrous metal electromagnetic separators were also considered. The objectives of this project culminated in the design and construction of a ferrous and non-ferrous detector with the ability to discriminate between ferrous and non-ferrous metals and to locate the objects on the conveying system. An experimental study was carried out to test the performance of the equipment in the detection of ferrous and non-ferrous objects of a given size carried on the conveyor belt. The ability of the equipment to discriminate between the types of metals and to locate the object on the belt was also evaluated experimentally. The benefits which can be gained from the industrial implementations of the equipment were considered. Further topics which may be investigated as an extension of this work are given.

A theoretical and experimental analysis of the no-load air gap magnetic field in squirrel cage induction machines

In induction machines the tooth frequency losses due to permeance variation constitute a signif'icant, portion of the total loss. In order to predict and estimate these losses it, is essential to obtain a clear understanding of the no-load distribution of the air gap magnetic field and the magnitude of flux pulsation in both stator and rotor teeth. The existing theories and methods by which the air gap permeance variation in a doubly slotted structure is calculated are either empirical or restricted. The main objective of this thesis is to obtain a detailed analysis of the no-load air gap magnetic field distribution and the effect of air gap geometry on the magnitude and waveform of the tooth flux pulsation. In this thesis a detaiiled theoretical and experimental analysis of flux distribution not only leads to a better understanding of the distribution of no-load losses but also provides theoretical analysis for calculating the losses with greater accuracy

Non-invasive winding fault detection for induction machines based on stray flux magnetic sensors

Non-intrusive monitoring of health state of induction machines within industrial process and harsh environments poses a technical challenge. In the field, winding failures are a major fault accounting for over 45% of total machine failures. In the literature, many condition monitoring techniques based on different failure mechanisms and fault indicators have been developed where the machine current signature analysis (MCSA) is a very popular and effective method at this stage. However, it is extremely difficult to distinguish different types of failures and hard to obtain local information if a non-intrusive method is adopted. Typically, some sensors need to be installed inside the machines for collecting key information, which leads to disruption to the machine operation and additional costs. This paper presents a new non-invasive monitoring method based on GMRs to measure stray flux leaked from the machines. It is focused on the influence of potential winding failures on the stray magnetic flux in induction machines. Finite element analysis and experimental tests on a 1.5-kW machine are presented to validate the proposed method. With time-frequency spectrogram analysis, it is proven to be effective to detect several winding faults by referencing stray flux information. The novelty lies in the implement of GMR sensing and analysis of machine faults.