The objective of this study was to model the physical and mechanical properties of 100% cotton slub yarns commonly used in denim and other casual wear. Statistical models were developed using central composite experimental design of the response surface methodology. Yarn’s linear density, slub thickness, slub length and pause length were used as the key input variables while yarn strength, elongation, coefficient of mass variation, imperfections and hairiness were used as response/output variables. It was concluded that yarn strength and elongation increased with increase in linear density and pause length, and decreased with increase in slub thickness and slub length. Yarn mass variation and total imperfections increased with increase in slub thickness and pause length, whereas yarn imperfections and hairiness decreased with increase in slub length. It was further concluded that due to statistically significant square and interaction effects of some of the input variables, only the quadratic model instead of the linear models can adequately represent the relationship between the input and the output variables. These statistical models will be of great importance for the industrial personnel to improve their productivity and reduce sampling.
Digitalization of the industrial sector and Industry 4.0 have opened new horizons in many technical fields, including electrical machine diagnostics and operation, as well as machine condition monitoring. This paper addresses a selection of electrical machine diagnostics methods that are applicable for the use in the perspective of Industry 4.0, to be used in hand with cloud environments and the possibilities granted by the Internet of Things. The need for further research and development in the field is pointed out. Some potentially applicable future approaches are presented.
Electrical machines, induction motors in particular, play a key role in domestic and industrial applications. They act as a work horse in almost every industry and are responsible for a big proportion of total generated electricity consumption worldwide. The faults in induction motors are degenerative in nature and can lead to a catastrophic situation if not diagnosed earlier. The failures can cause considerable financial loss in the form of unexpected downtime. Broken rotor bar is a very common and frequently occurring fault in most of industrial induction motors. To select a better, more accurate and reliable fault diagnostic technique, this paper presents a comprehensive literature survey on the existing motor current signature analysis (MCSA) based fault diagnostic techniques. Different well-known MCSA based fault diagnostic techniques are summarized in the form of basic theories, considering complexity of their implementation, merits and demerits.
In this paper, the harmonic contribution of the broken rotor bar of an induction machine is investigated using an effective combination of the fast Fourier transform (FFT) and a band stop filter. The winding, spatial, grid fed and fault-based harmonics are investigated. Since the fundamental component is the most powerful component as compared to the other frequencies, it decreases the legibility of spectrum, making logarithmic scale inevitable. It also remains a potential threat of burying the fault representative side band frequencies because of its spectral leakage. In this paper, a band stop Chebyshev filter is used to attenuate the fundamental component, which makes the spectrum clearer and easier to understand even on the linear scale. Its good transition band and low passband ripples make it suitable for attenuating the main supply frequency with low impact on the neighbouring side band frequencies. To study the impact of fault on magnetic flux distribution, simulation is done using finite element method with good number of mesh elements and very small step size. The line current is calculated and frequency spectrum is investigated to segregate the spatial and fault frequencies using the proposed technique. The results are further validated by implementing the algorithm on the data measured in the laboratory environment including the grid fed harmonics.