In this paper authors are going to present a Markov Decision Process (MDP)
based algorithm in Industrial Internet of Things (IIoT) as a safety compliance
layer for human in loop system. Though some industries are moving towards
Industry 4.0 and attempting to automate the systems as much as possible by
using robots, still human in loop systems are very common in developing
countries like India. When ever there is a need for human machine interaction,
there is a scope of health hazard. In this work we have developed a system for
one such industry using MDP. The proposed algorithm used in this system learned
the probability of state transition from experience as well as the system is
adaptable to new changes by incorporating the concept of transfer learning. The
system was evaluated on the data set obtained from 39 sensors connected to a
computer numerically controlled (CNC) machine pushing data every second in a
24x7 scenario. The state changes are typically instructed by a human which
subsequently lead to some intentional or unintentional mistakes and errors. The
proposed system raises an alarm for the operator to warn which he may or may
not overlook depending on his own perception about the present condition of the
system. Repeated ignorance of the operator for a particular state transition
warning guides the system to retrain the model. We observed 95.61% alarms
raised by the said system are taken care of by the operator. 3.2% alarms are
coming from the changes in the system which in turn used to retrain the model
and 1.19% alarms are false alarms. We could not compute the error coming from
the mistake performed by the human operator as there is no ground truth
available for that.

使用基于马尔可夫决策过程（MDP）的算法作为工业物联网（IIoT）中的安全合规层，以应对人机交互的健康危害问题，并使用迁移学习的概念适应新变化的系统。