The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

Low-cost GPS receivers provide geodetic positioning information using the NMEA protocol, usually with eight digits for latitude and nine digits for longitude. When these geodetic coordinates are converted into Cartesian coordinates, the positions fit in a quantization grid of some decimeters in size, the dimensions of which vary depending on the point of the terrestrial surface. The aim of this study is to reduce the quantization errors of some low-cost GPS receivers by using a Kalman filter. Kinematic tractor model equations were employed to particularize the filter, which was tuned by applying Monte Carlo techniques to eighteen straight trajectories, to select the covariance matrices that produced the lowest Root Mean Square Error in these trajectories. Filter performance was tested by using straight tractor paths, which were either simulated or real trajectories acquired by a GPS receiver. The results show that the filter can reduce the quantization error in distance by around 43%. Moreover, it reduces the standard deviation of the heading by 75%. Data suggest that the proposed filter can satisfactorily preprocess the low-cost GPS receiver data when used in an assistance guidance GPS system for tractors. It could also be useful to smooth tractor GPS trajectories that are sharpened when the tractor moves over rough terrain.

Error is always present in the GPS guidance of a tractor along a desired trajectory. One way to reduce GPS guidance error is by improving the tractor positioning. The most commonly used ways to do this are either by employing more precise GPS receivers and differential corrections or by employing GPS together with some other local positioning systems such as electronic compasses or Inertial Navigation Systems (INS). However, both are complex and expensive solutions. In contrast, this article presents a simple and low cost method to improve tractor positioning when only a GPS receiver is used as the positioning sensor. The method is based on placing the GPS receiver ahead of the tractor, and on applying kinematic laws of tractor movement, or a geometric approximation, to obtain the midpoint position and orientation of the tractor rear axle more precisely. This precision improvement is produced by the fusion of the GPS data with tractor kinematic control laws. Our results reveal that the proposed method effectively reduces the guidance GPS error along a straight trajectory.

The aim of this study was to propose and evaluate a very small wind turbine (VSWT) that competes with commercial grid-connected VSWTs in terms of simplicity, robustness and price. Its main components are a squirrel-cage induction generator (SCIG) driven by a frequency converter. The system has a direct-drive shaft, and may be constructed with commercial equipment. Simulation of the wind turbine effect is done with a motor. A control program regulates the variable-speed of rotation through three operational modes: (i) to drive the turbine to its optimum operation point; (ii) to limit its maximum rotational speed; and (iii) to limit the maximum power it generates. Two tests were performed, in order to evaluate the dynamic response of this system under variable wind speeds. The tests demonstrate that the system operates at the optimum operational point of the turbine, and within the set limits of maximum rotational speed and maximum generated power. The drop in performance in relation to its nominal value is about 75%, when operating at 50% of the nominal power. In summary, this VSWT with its proposed control program is feasible and reliable for operating direct-shaft grid-connected VSWTs.

A method for the prediction of Energy Production (EP) in Concentrating Photovoltaic (CPV) installations is examined in this study. It presents a new method that predicts EP by using Global Horizontal Irradiation (GHI) and the Photovoltaic Geographical Information System (PVGIS) database, instead of Direct Normal Irradiation (DNI) data, which are rarely recorded at most locations. EP at four Spanish CPV installations is analyzed: two are based on silicon solar cells and the other two on multi-junction III-V solar cells. The real EP is compared with the predicted EP. Two methods for EP prediction are presented. In the first preliminary method, a monthly Performance Ratio (PR) is used as an arbitrary constant value (75%) and an estimation of the DNI. The DNI estimation is obtained from GHI measurements and the PVGIS database. In the second method, a lineal model is proposed for the first time in this paper to obtain the predicted EP from the estimated DNI. This lineal model is the regression line that correlates the real monthly EP and the estimated DNI in 2009. This new method implies that the monthly PR is variable. Using the new method, the difference between the predicted and the real EP values is less than 2% for the annual EP and is in the range of 5.6%–16.1% for the monthly EP. The method that uses the variable monthly PR allows the prediction of the EP with reasonable accuracy. It is therefore possible to predict the CPV EP for any location, using only widely available GHI data and the PVGIS database.

Farmers experience whole-body vibrations when they drive tractors. Among the various factors that influence the vibrations to which the driver is exposed are terrain roughness, tractor speed, tire type and pressure, rear axle width, and tractor seat height above the ground. In this paper the influence of tractor seat height above the ground on the lateral vibrations to which the tractor driver is exposed is studied by means of a geometrical and an experimental analysis. Both analyses show that: (i) lateral vibrations experienced by a tractor driver increase linearly with tractor-seat height above the ground; (ii) lateral vibrations to which the tractor driver is exposed can equal or exceed vertical vibrations; (iii) in medium-size tractors, a feasible 30 cm reduction in the height of the tractor seat, which represents only 15% of its current height, will reduce the lateral vibrations by around 20%; and (iv) vertical vibrations are scarcely influenced by tractor-seat height above the ground. The results suggest that manufacturers could increase the comfort of tractors by lowering tractor-seat height above the ground, which will reduce lateral vibrations.