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This paper aims to fabricate pineapple leaf fibre epoxy reinforced with three different orientations which are unidirectional, bidirectional and inclined 45°. E-glass fibre is also introduced in every orientation to observe the change in strength. Fabrication of all these epoxies reinforced pine apple fibre is being done by Hand Layup technique. Two tests are done on these composites that are Tensile Test and Flexural Test. The tensile strength of unidirectional oriented fibre is increased when the E-glass is introduced with composites. The tensile property of unidirectional fibre is reinforced when the E-glass is brought together with composites. The maximum tensile strength was observed in the E-glass unidirectional pineapple fibre composite. Optimum Flexural strength is observed for inclined 45° specimen without E-glass reinforcement, whereas with the introduction of E-glass unidirectional composite maximum flexural strength is yielded. From the results, it can be proposed that Pineapple Leaf Fibre (PALF) reinforced with epoxy and E-glass showed best characteristics for unidirectional orientation of fibre for tensile and in flexural loading. Based on the results obtained, this pineapple composite material is recommended to use for lightweight packaging applications like food industries. [ABSTRACT FROM AUTHOR]

Condition monitoring plays a vital role in predictive maintenance of machinery in today's world. It is very important to detect the faults in the machinery as early as possible in order to stop the propagation of faults which may lead to heavy damages. The objective of our study is to investigate the vibration analysis of bevel gearbox to detect the faults. An experimental setup was developed to carry out the vibration analysis. Various types of faults were induced in the gearbox. The vibration analysis was carried out for different types of faults with different lubrication levels in the gearbox. MATLAB toolbox was used for signal processing in which Coiflet wavelet was used for denoising the signal. An artificial neural network (ANN) and a deep neural network (DNN) were used to detect the faults in the bevel gearbox automatically. The results were promising in detecting the faults with high accuracy. [ABSTRACT FROM AUTHOR]

This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %. [ABSTRACT FROM AUTHOR]

This research highlights the vibration analysis on worm gears at various conditions of oil using the experimental set up. An experimental rig was developed to facilitate the collection of the vibration signals which consisted of a worm gear box coupled to an AC motor. The four faults were induced in the gear box and the vibration data were collected under full, half and quarter oil conditions. An accelerometer was used to collect the signals and for further analysis of the vibration signals, MATLAB software was used to process the data. Symlet wavelet transform was applied to the raw FFT to compare the features of the data. ANN was implemented to classify various faults and the accuracy is 93.3%. [ABSTRACT FROM AUTHOR]