  Reliability is one of the major design criteria in Cyber-Physical Systems
(CPSs). This is because of the existence of some critical applications in CPSs
and their failure is catastrophic. Therefore, employing strong error detection
and correction mechanisms in CPSs is inevitable. CPSs are composed of a variety
of units, including sensors, networks, and microcontrollers. Each of these
units is probable to be in a faulty state at any time and the occurred fault
can result in erroneous output. The fault may cause the units of CPS to
malfunction and eventually crash. Traditional fault-tolerant approaches include
redundancy time, hardware, information, and/or software. However, these
approaches impose significant overheads besides their low error coverage, which
limits their applicability. In addition, the interval between error occurrence
and detection is too long in these approaches. In this paper, based on Deep
Reinforcement Learning (DRL), a new error detection approach is proposed that
not only detects errors with high accuracy but also can perform error detection
at the moment due to very low inference time. The proposed approach can
categorize different types of errors from normal data and predict whether the
system will fail. The evaluation results illustrate that the proposed approach
has improved more than 2x in terms of accuracy and more than 5x in terms of
inference time compared to other approaches.
