@article{scholars10923,
           pages = {1--10},
       publisher = {Springer Verlag},
         journal = {Studies in Computational Intelligence},
            year = {2018},
           title = {Introduction},
             doi = {10.1007/978-3-319-71871-2{$_1$}},
            note = {cited By 0},
          volume = {743},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85040002653&doi=10.1007\%2f978-3-319-71871-2\%5f1&partnerID=40&md5=14130eb2d95d3253e2efc8b85678c99a},
        abstract = {In general, fossil fuel based power producing plants may not stay longer at a performance level envisaged at the design stage due to aging, malfunction (e.g. guide vane drift, fouling, erosion, variable bleed valve failure), changing fuel composition, and changing operating conditions. Because of these, they often require preventive maintenance interventions. If the onset of abnormal conditions is not dealt with at early stage, it can lead to reduced performance, high green house gas emissions and system outage. With excessive NOx, CO, UHCs and CO2 emissions, it will be difficult to comply with the regulatory bodys requirements on emission control. The loss of productivity because of breakdown creates high financial penalties depending upon the duration of plant downtime. Failure to have a monitoring system also causes increased cognitive load on operators and inefficient use of maintenance staff time. Since the plant is featured by high energy throughput, failure to detect even small reduction in efficiency may lead to high financial loss. {\^A}{\copyright} 2018, Springer International Publishing AG.},
            issn = {1860949X},
          author = {Lemma, T. A.}
}