It is well known that storage sensor data on storage systems can detect abnormal symptoms that can lead to failures. With the abnormal sensor data and machine learning techniques, we can predict a storage component failure ahead of time and proactively remove it, before it can impact the remaining system or interrupt customer’s operations. A successful predictive maintenance model must make trade off in detection rate, false positive and lead time.