  There is growing importance to detecting faults and implementing the best
methods in industrial and real-world systems. We are searching for the most
trustworthy and practical data-based fault detection methods proposed by
artificial intelligence applications. In this paper, we propose a framework for
fault detection based on reinforcement learning and a policy known as proximal
policy optimization. As a result of the lack of fault data, one of the
significant problems with the traditional policy is its weakness in detecting
fault classes, which was addressed by changing the cost function. Using
modified Proximal Policy Optimization, we can increase performance, overcome
data imbalance, and better predict future faults. When our modified policy is
implemented, all evaluation metrics will increase by $3\%$ to $4\%$ as compared
to the traditional policy in the first benchmark, between $20\%$ and $55\%$ in
the second benchmark, and between $6\%$ and $14\%$ in the third benchmark, as
well as an improvement in performance and prediction speed compared to previous
methods.
