@article{MAKHILLJEAS2019141117905,
    title = {Advisory System for Operators of Complex Industrial Processes Extended by Diagnostic Functions},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {11},
    pages = {3506-3513},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.3506.3513},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.3506.3513},
    author = {Ivan and},
    keywords = {Advisory system,Bayesian statistics,mixture of probability density functions,Kullback-Leibler divergence,diagnostics,rolling mills},
    abstract = {Operators of industrial processes face the complexity of the process usually. They have to set all
appropriate parameters correctly to ensure requested performance and quality of production. This demanding
task is additionally complicated by permanent changing of ambient conditions. A probabilistic advisory system
was developed as a support tool for operators. The advisory system processes historical data and concentrates
the information about process behavior into a mixture of probability density functions. With the help of
bayesian statistics, the advisory system compares historical information to the actual working point of the
process and generates advisory information on how to change current process settings in order to reach
requested performance and quality of production. Large mathematical and computational potential of developed
advisory system encouraged an extension of the system by advanced diagnostic functions. The aim of this
diagnostics is to recognize sensor and signal malfunctions that cannot be detected by standard diagnostic
tools.}
    }