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Anomaly Analysis in Machine Sensors

In Industries, cutting blades is considered the main role in manufacturing the products. The cutting gathering is also a significant part of the machine to meet the high accessibility target. Along these lines, the edge should be set-up and kept up with appropriately. In Industries, the repairing of cutting blades is a major disadvantage. During, the time of heavy workload of machines, failure of blades may easily happen. Those incidents may happen due to damage to machine parts, blade stroking, and reducing the quality of blades. This leads to low costs, productivity will be increased and it is more safety. In this paper, our main aim is to find the machine anomalies. In this, we used a few algorithms to find anomalies. The approaches are One-class SVM, K-Means, and Autoencoder. We proposed an approach to find machine degradation and anomalies.