The Application of Radial Basis Function (RBF) Neural Network for Mechanical Fault Diagnosis of Gearbox

Abstract

Abstract. In this paper, the radial basis function (RBF) neural network is used for the mechanical fault diagnosis of a gearbox. We introduce the basic principles of the RBF neural network which is used for pattern classification and features a fast learning pace and strong nonlinear mapping capability; thus, it can be employed for fault diagnosis. The gearbox is a widely-used piece of equipment in engineering, and diagnosing mechanical faults is of great significance for engineers. A numerical example is presented to demonstrate the capability of the proposed method. The results indicate that the mechanical faults of a gearbox can be correctly diagnosed with a trained RBF neural network.

An Improved Artificial Bee Colony Algorithm in LoRa Wireless Communication System for Efficient Multimedia Transmission

“…Moreover, the number of nodes is the least. On the one hand, the LoRa wireless network can achieve good communication in a certain area, and, on the other hand, it can meet the requirements of real time and lower power consumption [8,20,23]. In order to show the effect of wireless network nodes optimized by the improved artificial colony algorithm, we select different scene areas to test the two kinds of algorithm.…”

“…The observation bees are responsible for searching nectar around the hive, and the leader bees search for honey sources [27,28]. We define some variables to represent them [16,17,23]. Firstly, a series of original honey sources are initialized randomly.…”

“…During the updating process [22,23,26], ( + 1), ( + 1), and ( + 1) will be updated according to the network correction equation. If these honey sources are not replaced, the position will be reserved.…”

“…The contributions of this paper are listed as follows: an artificial bee colony algorithm based on RBF radical basis neural network (RBFABC) is designed to resolve this problem, and this algorithm can solve the contradiction between power consumption and real-timeliness [14,17,23]. Furthermore, this design not only improves the traditional way of selecting and updating the honey sources in basic artificial swarm optimization algorithm, but also enables the algorithm to converge towards the optimal solution.…”

Study on the Predictive of Dynamic Milling Force of Milling Process Based on Data Mining

“…The current experimental formula of milling force is usually expressed in exponential form, which consists of correction factor, cutting process parameter and exponential factor [17] . The available process parameters are cutting width, cutting depth, cutting speed, feed per tooth, feed, spindle speed, milling cutter diameter and number of teeth.…”