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Gearbox is an essential device employed in industries to vary speed and load conditions according to the requirements. More advancement in its design and operation leads to increase in industrial applications. The failure in any of the components of gearbox can lead to production loss and increase maintenance cost. The component failure has to be detected earlier to avoid unexpected breakdown. Vibration measurements are used to monitor the condition of the machine for predictive maintenance and to predict the gearbox faults successfully. This paper addresses the use of vibration signal for automated fault diagnosis of gearbox. In the experimental studies, good gears and face wear gears are used to collect vibration signals for good and faulty conditions of the gearbox. Each gear is tested with two different speeds and loading conditions. The statistical features are extracted from the acquired vibration signals. The extracted features are given as an input to the support vector machine (SVM) for fault identification. The Performance of the fault identification system using vibration signals are discussed and compared.

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Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains.

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