Frequency Of Machine Research Articles

The effect of high-frequency stochastic actions on the low-frequency behaviour of dynamic systems.

The dynamic system described by a finite number of first-order differential equations is considered. The right side of each equation is a sum of a slow, deterministic and, in the general case, nonlinear function of dynamic variables and a stochastic excitation. The stochastic action is a superposition of a finite number of independent random processes with coefficients depending on dynamic variables and slow time. The problem statement is oriented to applications in the field of driven systems. The analysis is based on the concept of vibration mechanics proposed by I. I. Blekhman. The modified method of direct separation of slow and fast motions uses the explicit introduction of a small parameter and some ideas of the two-scale technique. The general formulas for vibrational forces (or fluxes) are obtained. These additional terms appear in the resulting system for averaged motion instead of the stochastic terms to make the averaged system equivalent to the initial stochastic system with respect to slow motions and, in particular, to low-frequency resonances. As an example, the model of a vibration machine for bulk material processing is considered. The stochastic effect is caused by random oscillations of the bulk material mass. It is transformed into a modification of the machine's frequency characteristics leading to a specific stochastic resonance. This article is part of the theme issue 'Vibrational and stochastic resonance in driven nonlinear systems (part 1)'.

Prognosis and Diagnosis of Farm Tractors Reliability and Availability for Maintenance Policies Using Markov – Chain Model

A sound maintenance planning is of crucial importance for farm power systems. There is a large potential in cost savings by optimizing maintenance decisions to make utilization of farm tractors more cost-efficient. Reliability and availability are fundamental attributes of organization, scheduling and operation of fleet of tractors in agricultural project of multi-farms. This paper utilizes recursive Markov chain closed-form analytical solution and condition-based maintenance model to evaluate performance of degraded multi-state system. First state of system failure is inspected, analyzed, and classified into partial, or combined or complete failure of estimating the transition matrix for the failure state. At each inspection of failure status a preventive maintenance (minor repair by replacement of parts) or corrective maintenance (major replacement of parts by complete overhaul) is performed to restore the system to good as new. The development of condition-based maintenance is used to signify the monitoring of machines for the purpose of diagnostics and prognostics. Diagnostics are used to determine the current status of a machine's frequency of failure (useful life) and prognostics are used to predict its dependability, availability (utility).Hence, the system of evaluation is quantified by six distinct indicators (maximum time before failure, tractor dependability, availability, frequency of failure and operating time between preventive and corrective maintenance) such that appropriate actions can be planned and taken in order to minimize the impact of equipment failure to operation. Simulation results for a dataset of three tractors (T120, C225 and B250) from two workshops of sugar plantation (Gunied and Sennar factories in Sudan) is investigated to assert the magnitude of variation between the tested variables that justify changing current maintenance policy using analysis of variation. The results indicate the applicability of Markov where comparison with condition-based maintenance is the optimal maintenance strategy for tractor high failure rate.

An Amplitude Modulation Detector for Fault Diagnosis in Rolling Element Bearings

The purpose of this research is to identify single-point defects in rolling element bearings. These defects produce characteristic fault frequencies that appear in the machine vibration and tend to modulate the machine's frequencies of mechanical resonance. An amplitude modulation (AM) detector is developed to identify these interactions and detect the bearing fault while it is still in an incipient stage of development (i.e., to detect the instances of AM when the magnitude of the characteristic fault frequency itself is not significant). Use of this detector only requires machine vibration from one sensor and knowledge of the bearing characteristic fault frequencies. Computer simulations as well as machine vibration data from bearings containing outer race faults are used to confirm the proficiency of this proposed technique.