Is it feasible to use decision tree algorithms for sensor fault detection?
You need to determine how to formulate your problem. I see it as having two aspects:
1. Detect an abnormal value (in temperature)
2. Determine whether abnormal value is due to sensor or system problem

The first is an anomaly detection problem and there is lots of literature on the topic, including tree-based methods on sensor data. One prominent approach is Isolation Forests, which is implemented in scikit-learn. http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html

The second is fault localization. This is an area also well described in literature, and tree-based models were used even before machine learning became a thing.
A key here is to only train on anomalous data, then this should become a relatively simple classification problem assuming your have relevant features.