As society becomes increasingly reliant on electricity, the reliability requirements for electricity supply continue to rise. In response, transmission/distribution system operators (T/DSOs) must improve their networks and operational practices to reduce the number of interruptions and enhance their fault localization, isolation, and supply restoration processes to minimize fault duration. This paper proposes a machine learning based fault prediction method that aims to predict incipient faults, allowing T/DSOs to take action before the fault occurs and prevent customer outages.