Pradeep Katta

Numerous industrial applications depend heavily on induction motors and their malfunction causes considerable financial losses. Induction motors in industrial processes have recently expanded dramatically in size, and complexity of defect identification and diagnostics for such systems has increased as well. As a result, research has concentrated on developing novel methods for the quick and accurate identification of induction motor problems.In response to these needs, this paper provides an optimised algorithm for analysing the performance of an induction motor. To analyse the operation of induction motors, an enhanced methodology on Deep Belief Networks (DBN) is introduced for recovering properties from the sensor identified vibration signals. Restricted Boltzmann Machine (RBM) is stacked utilizing multiple units of DBN model, which is then trained adopting Ant colony algorithm.An innovative method of feature extraction for autonomous fault analysis in manufacturing is provided by experimental investigations utilising vibration signals and overall accuracy of 99.8% is obtained, which therefore confirms the efficiency of DBN architecture for features extraction.

The recent improvements related to the area of electric locomotive, power electronics, assembly processes and manufacturing of machines have increased the robustness and reliability of induction motors. Regardless of the increased availability, the application of induction motors in many fields alleges the need for operating state supervision and condition monitoring. In other words, fault identification at the initial stage helps make appropriate control decisions, influencing product quality as well as providing safety. Inspired by these demands, this work proposes a regression based modeling for the analysis of performance in induction motors. In this approach, the feature extraction process is combined with classification for efficient fault detection. Deep Belief Network (DBN) stacked with multiple Restricted Boltzmann Machine (RBM) is exploited for the robust diagnosis of faults with the adoption of training process. The influences of harmonics over induction motors are identified and the losses are mitigated. The simulation of the suggested approach and its comparison with traditional approaches are executed. An overall accuracy of 99.5% is obtained which in turn proves the efficiency of DBN in detecting faults.

Once a patient enters the comatose stage, it is really difficult to predict when he/she will be out of it. It may be within days, weeks, or may even take months and years together. Due to this situation, it becomes difficult for the hospital staff to monitor and keep watch over the patient at all times and thus slight body movements and life-like indications or abnormal activities may go unnoticed. To prevent such a situation, the proposed system has a wide range of wearable sensors fitted to the patient’s body. Sensors used are flex sensors, MEMS Accelerometer, heartbeat sensor, SPO2 oximeter sensor, temperature sensor, IR sensor in the form of goggles. These sensors help monitor the patient’s vitals and these data are stored in the cloud server and can be accessed when the need arises through a PC or smart-phone. The parameters are then upon plotted on a graph and hence analysis can be done on it to predict the approximate chances of recovery of the patient.