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The Look-Compute-Move model (LCM) is adopted to study swarms of mobile robots that have to solve a given problem. Robots are generally assumed to be autonomous, indistinguishable, anonymous, homogeneous and to move on the Euclidean plane. Different LCM sub-models have been theorized to study different settings and their computational power. Notably, the literature has focused on four base models (i.e., OBLOT, FSTA, FCOM, LUMI) that differ in memory and communication capabilities, and in different synchronization modes (e.g., fully synchronous FSYNCH, semi-synchronous SSYNCH). In this paper, we consider fault-prone models where robots can suffer from crash faults: each robot may irremediably stop working after an unpredictable time. We study the general Fault Detection (FD) problem which is solved by a swarm if it correctly detects whether a faulty robot exists in the swarm. The Fault Identification (FI) problem additionally requires identifying which robots are faulty. We consider 12 LCM sub-models (OBLOT, FSTA, FCOM, LUMI, combined with FSYNCH, SSYNCH, and the round-robin RROBIN) and we study the (im)possibility of designing reliable procedures to solve FD or FI. In particular, we propose three distributed algorithms so that a swarm can collectively solve FD under the models LUMI^FSYNCH, FCOM^FSYNCH, and LUMI^RROBIN.