Theoretical and experimental research indicates that radial loads have a significant influence on the value of belt-on-idler rolling resistances. Computational models discussed in literature use the notion of unit rolling resistance, i.e. rolling resistance per unit length of the idler. The total value of the rolling resistance of belt on a single idler is determined by integrating unit rolling resistance with respect to the length of the contact zone between the belt and the idler. This procedure requires the knowledge of normal load distribution along the contact zone between the belt and the idler. Loads acting on the idler set have been the object of both theoretical analyses and laboratory tests. Literature mentions several models which describe the distribution of normal loads along the contact zone between the belt and the idler set (Krause & Hettler, 1974; Lodewijks, 1996; Gładysiewicz, 2003; Jennings, 2014). Numerous experimental tests (Gładysiewicz & Kisielewski, 2017; Król, 2017; Król & Zombroń, 2012) demonstrated that the resultant normal loads acting on idlers are approximate to the loads calculated in theoretical models. If the resultant normal load is known, it is possible to assume the distribution of loads acting along the contact zone between the belt and the idler. This paper analyzes various hypothetical load distributions calculated for both the center idler roll and for the side idler roll. It also presents the results of calculations of belt rolling resistances for the analyzed distributions. In addition, it presents the results of calculations with allowance for load distribution along the generating line of the idler.
The paper consists the problem of developing a scientific toolkit allowing to predict the thermal state of the ingot during its formation in all elements of the casting and rolling complex, between the crystallizer of the continuous casting machine and exit from the furnace. As the toolkit for the decision making task the predictive mathematical model of the ingot temperature field is proposed. Displacement between the various elements of the CRC is accounted for by changing the boundary conditions. Mass-average enthalpy is proposed as a characteristic of ingot cross-section temperature state. The next methods of solving a number of important problems with the use of medium mass enthalpy are developed: determination of the necessary heat capacity of ingots after the continuous casting machine for direct rolling without heating; determination of the rational time of alignment of the temperature field of ingots having sufficient heat capacity for rolling after casting; determination of the total amount of heat (heat capacity) required to supply the metal for heating ingots that have insufficient amount of internal heat.
Wind turbines are nowadays one of the most promising energy sources. Every year, the amount of energy produced from the wind grows steadily. Investors demand turbine manufacturers to produce bigger, more efficient and robust units. These requirements resulted in fast development of condition-monitoring methods. However, significant sizes and varying operational conditions can make diagnostics of the wind turbines very challenging. The paper shows the case study of a wind turbine that had suffered a serious rolling element bearing (REB) fault. The authors compare several methods for early detection of symptoms of the failure. The paper compares standard methods based on spectral analysis and a number of novel methods based on narrowband envelope analysis, kurtosis and cyclostationarity approach. The very important problem of proper configuration of the methods is addressed as well. It is well known that every method requires setting of several parameters. In the industrial practice, configuration should be as standard and simple as possible. The paper discusses configuration parameters of investigated methods and their sensitivity to configuration uncertainties
Minimum Entropy Deconvolution (MED) has been recently introduced to the machine condition monitoring field to enhance fault detection in rolling element bearings and gears. MED proved to be an excellent aid to the extraction of these impulses and diagnosing their origin, i.e. the defective component of the bearing. In this paper, MED is revisited and re-introduced with further insights into its application to fault detection and diagnosis in rolling element bearings. The MED parameter selection as well as its combination with pre-whitening is discussed. Two main cases are presented to illustrate the benefits of the MED technique. The first one was taken from a fan bladed test rig. The second case was taken from a wind turbine with an inner race fault. The usage of the MED technique has shown a strong enhancement for both fault detection and diagnosis. The paper contributes to the knowledge of fault detection of rolling element bearings through providing an insight into the usage of MED in rolling element bearings diagnostic. This provides a guide for the user to select optimum parameters for the MED filter and illustrates these on new interesting cases both from a lab environment and an actual case.
The objective of the present study was to investigate the effects of Sn addition on the mechanical and corrosion properties of Mg-1Zn-1Zr-xSn (x = 1, 2, 3, 4, 5 wt.%) alloys prepared by powder-in-tube rolling (PTR) method. The PTR-treated Mg alloys reached 98.3% of theoretical density. The hardness of the alloy increased with Sn addition. Two main intermetallic phases, Mg2Sn and Zn2Zr3, were formed in the alloys. The Mg2Sn intermetallic particles were observed along the grain boundaries, while the Zn2Zr3 particles were distributed in the Mg matrix. The addition of 1 wt. % Sn caused the corrosion potential to shift toward a more positive value, and the resulting alloy exhibited low corrosion current density.
Microstructures and mechanical properties of as-cast Al-6.5Mg-1.5Zn-0.5Fe alloys newly alloy-designed for the parts of automobile were investigated in detail. The aluminum (Al) sheets of 4 mm thickness, 30 mm width and 100 mm length were reduced to a thickness of 1mm by multi-pass rolling at ambient temperature and subsequently annealed for 1h at 200~500°C. The as-cast Al sheet was deformed without a formation of so large cracks even at huge rolling reduction of 75%. The recrystallization begun to occur at 250°C, it finished at 350°C. The as-rolled material showed tensile strength of 430 MPa and tensile elongation of 4.7%, however the specimen after annealing at 500°C showed the strength of 305 MPa and the elongation of 32%. The fraction of high angle grain boundaries above 15 degree increased greatly after annealing at high temperatures. These characteristics of the specimens after annealing were discussed in detail.
The paper presents the results of the experimental tests of Mg/Al bimetallic bars rolling process in classic and multi-radial modified round-oval-round passes. The bimetallic bar consist of magnesium core, grade AZ31 and aluminium outer layer, grade 1050A. The stocks were round bars with diameter 22.5 mm with an aluminium layer share of 28%. As a result of rolling in four passes, bars of a diameter of about 17 mm were obtained. A bimetallic feedstock was manufactured using an explosive welding method. The use of the designed arrangement of multi-radial modified stretching passes resulted in obtaining Mg/Al bimetallic bars with an uniform distribution of the cladding layer over the bar perimeter and high quality of shear strength between individual layers compared to Mg/Al bars obtained in the classic passes.
At present, most of the existing target detection algorithms use the method of region proposal to search for the target in the image. The most effective regional proposal method usually requires thousands of target prediction areas to achieve high recall rate.This lowers the detection efficiency. Even though recent region proposal network approach have yielded good results by using hundreds of proposals, it still faces the challenge when applied to small objects and precise locations. This is mainly because these approaches use coarse feature. Therefore, we propose a new method for extracting more efficient global features and multi-scale features to provide target detection performance. Given that feature maps under continuous convolution lose the resolution required to detect small objects when obtaining deeper semantic information; hence, we use rolling convolution (RC) to maintain the high resolution of low-level feature maps to explore objects in greater detail, even if there is no structure dedicated to combining the features of multiple convolutional layers. Furthermore, we use a recurrent neural network of multiple gated recurrent units (GRUs) at the top of the convolutional layer to highlight useful global context locations for assisting in the detection of objects. Through experiments in the benchmark data set, our proposed method achieved 78.2% mAP in PASCAL VOC 2007 and 72.3% mAP in PASCAL VOC 2012 dataset. It has been verified through many experiments that this method has reached a more advanced level of detection.
The study proposed the model of “guide mark” defects formation on the internal surface of pipes, produced on PRM mills of PRP – 140. The research of pipe forming at plug rolling mill with stub mandrel has been carried out; regularities of the dimensionless parameters characterizing the deformation of the gap release, depending on the reduction ratio, were determined. The model of “guide mark” defect formation on the internal surface of the pipe has been proposed. This allows for lesser wall thickness variation of rough tubes. It has been shown that, when using dioctahedral pass designs in comparison with hexagonal pass designs the proportion of displaced volume along the pipe axis is greater but the value is lower; thereby, the risk of “guide mark” defect forming is reduced.
This paper presents mechanical fault detection in squirrel cage induction motors (SCIMs) by means of two recent techniques. More precisely, we have analyzed the rolling element bearing (REB) faults in SCIM. Rolling element bearing faults constitute a major problem among different faults which cause catastrophic damage to rotating machinery. Thus early detection of REB faults in SCIMs is of crucial importance. Vibration analysis is among the key concepts for mechanical vibrations of rotating electrical machines. Today, there is massive competition between researchers in the diagnosis field. They all have as their aim to replace the vibration analysis technique. Among them, stator current analysis has become one of the most important subjects in the fault detection field. Motor current signature analysis (MCSA) has become popular for detection and localization of numerous faults. It is generally based on fast Fourier transform (FFT) of the stator current signal. We have detailed the analysis by means of MCSA-FFT, which is based on the stator current spectrum. Another goal in this work is the use of the discrete wavelet transform (DWT) technique in order to detect REB faults. In addition, a new indicator based on the MCSA-DWT technique has been developed in this study. This new indicator has the advantage of expressing itself in the quantity and quality form. The acquisition data are presented and a comparative study is carried out between these recent techniques in order to ensure a final decision. The proposed subject is examined experimentally using a 3 kW squirrel cage induction motor test bed.