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This paper presents a sensor analysis based fault detection approach (which we call SAFDetection) that is used to monitor tightly-coupled multi-robot team tasks. Our approach aims at detecting both physical and logic faults of a robot system with little prior knowledge on the system. We do not need the motion model or a priori knowledge of the possible fault types of the monitored system. Our approach treats the monitored robot system as a black box, with only sensor data available. Thus, we believe the approach is general, and can be used in a wide variety of robot systems performing many different kinds of tasks. Our approach combines data clustering techniques with the generation of a probabilistic state diagram to model the normal operation of the multi-robot system. We have implemented this approach on a physical robot team. This paper presents the results of these experiments, which show that sensor data analyzed from a training phase of normal operation can be used to generate a model of normal robot team operation. This model can then be used to detect many types of abnormal behavior of the system, based purely on monitoring the sensor data of the system.