Using one-class classification first to find anomalies then apply multi-class classification

Question

I'm new to machine learning and trying to apply it for fault detection, an idea came to mind which is using only anomaly detection after which if the results after a while come up as positive, a multi-class classification algorithm (using 7 different classes) is used to classify the fault type. would that be efficient and saves on resources power?