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The purpose of this paper was to perform an experimental investigation to analyze vibration and noise of unloaded gearbox with different oil quality. All motor-driven machinery used in the modern world can develop faults. The maintenance plans include analyzing the external relevant information of critical components, in order to evaluate its internal state. From the beginning of the twentieth century, different technologies have been used to process signals of dynamical systems.

A proposed neural network (NN) is also employed to predict vibration parameters of the experimental test rig. Moreover, four types of oils are used for gearbox to predict reliable oil. Vibration signals extracted from rotating parts of machineries carry lot many information within them about the condition of the operating machine. Further processing of these raw vibration signatures measured at a convenient location of the machine unravels the condition of the component or the assembly under study. The experimental stand for testing an unloaded gearbox is composed by actuated direct current (DC) driving system.

This paper deals with the effectiveness of wavelet-based features for fault diagnosis of a gearbox using two types of artificial neural networks (ANNs) and stress analyzed with computer-based software ANNs. The results improved that the proposed NN has superior performance to adapt experimental results.

This paper is one such attempt to apply machine learning methods like ANN. This work deals with extraction of wavelet features from the vibration data of a gearbox system and classification of gear faults using ANNs.

These kind of NN-based approaches are novel approaches to predict real-time vibration and acceleration parameters of unloaded gearbox with five types of oils. Also, the investigation contains new information about studied process, containing elements of novelty.

The purpose of this paper is to improve the application of neural networks on vehicle engine systems for fault detecting and analysing engine oils.

Three types of neural networks are employed to find exact neural network predictor of vehicle engine oil performance and quality. Nevertheless, two oil types are analysed for predicting performance in the engine. These oils are used and unused oils. In experimental work, two accelerometers are located at the bottom of the car engine to measure related vibrations for analysing oil quality of both cases.

The results of both computer simulation and experimental work show that the radial basis neural network predictor gives good performance at adapting different cases.

The results of the proposed neural network analyser follow the desired results of the vehicle engine's vibration variation. However, this kind of neural network scheme can be used to analyse oil quality of the car in experimental applications.

As theoretical and practical studies are evaluated together, it is hoped that oil analysers and interested researchers will obtain significant results in this application area.

This paper is an original contribution on vehicle oil quality analysis using a proposed artificial neural network and it should be helpful for industrial applications of vehicle oil quality analysis and fault detection.