| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Outline of Final Research Achievements |
As IoT (Internet of Things) and BigData have been emerging, a variety of datamining technologies have been applied to predictive maintenance of equipment and facility. While these technologies are greatly successful in pattern recognition and social data analysis, they are less successful in predictive maintenance. One of the reasons is that existing machine learning methods cannot handle well unsupervised learning for detecting gradually changing states of industry systems. Therefore I proposed "time series chain extraction for detecting the gradually changing patterns" and "fast parameter-free feature extraction of magnitudes of spikes in time series" I also verified the effectiveness of our proposed methods on real industry data.
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