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

Ti-6Al-4V is extensively used in aerospace and bio-medical applications. In an automated machining environment monitoring of tool conditions is imperative. In this study, Experiments were conducted to classify the tool conditions during High Speed Machining of Titanium alloy. During the machining process, vibration signals were monitored continuously using accelerometer. The features from the signal are extracted and a set of prominent features are selected using Dimensionality Reduction Technique. The selected features are given as an input to the classification algorithm to decide about the condition of the tool. Feature selection has been carried out using J48 Decision Tree Algorithm. Classifications of tool conditions were carried out using Machine Learning Algorithms namely J48 Decision Tree algorithm and Artificial Neural Network (ANN). From the analysis, it is found that ANN is producing comparatively better results. The methodology adopted in this study will be useful for online tool condition monitoring

It is important to develop an intelligent tool condition monitoring system to increase productivity and promoting automation in metal cutting process. Many attempts have been made in the past to develop such systems using signals from various sensors

In this paper, a finite element model has been developed to predict the effect of residual stress induced in the work material during multiple pass turning of AISI 4340 steel. Chip morphology and force variation during machining are also quantified using the FE model. Finite element model was developed using arbitrary Lagrangian-Eulerian formulation along with Johnson-Cook material model and Johnson-Cook damage model. The finite element model developed in this study was validated experimentally by studying the chip morphologogy and cutting force variation during the machining. Results indicate that there is good correlation existing between numerical results and experimental results.

The statistical relationship between sensor signature features and lubricant solid particle contamination conditions in a spherical roller bearing has been investigated in this study. The influence of particle size and concentration of solid contaminants in lubricant on the RMS parameter of time-domain acoustic emission, vibration, and sound sensor signals are examined. Machine learning algorithms are trained with time domain statistical features derived from sensor signatures to predict the lubricant conditions. Decision trees, bagging tree ensembles, and support vector machines are used to build ML models. Decision Tree models are built using classification and regression tree algorithms with three distinct split criteria, namely gini, towing, and maximum deviance. A bagged tree ensemble model is constructed using the decision tree as a base learner. In the support vector machine, kernel tricking is done to optimize the classification boundaries. Models built using Acoustic emission signature features predict lubricant conditions with better accuracy compared to models constructed using sound and vibration signature features. Feature-level fusion approach is implemented by combining the vibration, sound, and acoustic emission features at the feature level to improve the prediction power of machine learning models. The bagged tree ensemble and support vector machine models, which are trained using fused features, predict lubricant conditions in spherical roller bearings with an accuracy of around 99%.

This paper aims to predict the severity of solid particle contaminants present in the lubricant in a Spur Gearbox using Vibration, Acoustic Emission, and Sound Signature features. Sensor signatures are acquired at various contaminant conditions of lubricant with different speed and load conditions. Statistical Features are extracted in the time domain, and feature ranking is carried out using the analysis of variance approach. Statistical models are developed using the selected features of Sound, Acoustic Emission, and vibration separately and fusing the features in the feature level. Decision Trees and Support Vector Machine algorithms are used in this study to build statistical models. AE features have a good correlation with lubricant conditions compared to sound and vibration features. The feature-level fusion approach predicts the lubricant conditions with more than 99% accuracy. The feature-level fusion models built using dominant features are computationally efficient without compromising the prediction ability.