How to Identify anomaly patterns for my binary value dataset using Python?

I want to identify the anomaly patterns of a fan which we used at boiler plant. So, by the fan only generate True and False only. Hence it is a binary dataset.

I tried plot deviations between the actual data and the rolling mean. But it doesn't make any sense. Hence, I want to identify those pattern which are not align with the normal behavior of the fan. Now I'm blank of a solution. If someone can give me a solution using machine-learning, that's much better.

As an example my dataset as like this.

TimeStamp	Fan
2023-01-10 17:50:55	False
2023-01-10 17:50:59	False
2023-01-10 17:51:15	False
2023-01-10 17:51:39	False
2023-01-10 17:51:55	True
2023-01-10 17:52:19	True
2023-01-10 17:52:35	True
2023-01-10 17:52:59	True
2023-01-10 17:53:05	False
2023-01-10 17:53:29	False
2023-01-10 17:53:55	False
2023-01-10 17:54:01	False
2023-01-10 17:54:16	True
2023-01-10 17:54:39	True
2023-01-10 17:54:55	True