The assets in Industry 4.0 are categorised into
physical, virtual and human. The innovation and popularisation
of ubiquitous computing enhance the usage of smart devices:
RFID tags, QR codes, LoRa tags, etc. for assets identification and
tracking. The generated data from Industrial Internet of Things
(IIoT) eases information visibility and process automation in
Industry 4.0. Virtual assets include the data produced from IIoT.
One of the applications of the industrial big data is to predict the
failure of manufacturing equipment. Predictive maintenance
enables the business owner to decide such as repairing or replacing
the component before an actual failure which affects the whole
production line. Therefore, Industry 4.0 requires an effective asset
management to optimise the tasks distributions and predictive
maintenance model. This paper presents the Genetic Algorithm
(GA) based resource management integrating with machine
learning for predictive maintenance in fog computing. The time,
cost and energy performance of GA along with MinMin, MaxMin,
FCFS, RoundRobin are simulated in the FogWorkflowsim. The
predictive maintenance model is built in two-class logistic
regression using real-time datasets. The results demonstrate that
the proposed technique outperforms MinMin, MaxMin, FCFS,
RoundRobin in execution time, cost and energy usage. The
execution time is 0.48%faster, 5.43% lower cost and energy usage
is 28.10% lower in comparison with second-best results. The
training and testing accuracy of the prediction model is 95.1% and
94.5%, respectively