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Filtration of Diagnostic Data for Retrospective Analysis in Health Monitoring Systems of Engineering Structures

Authors of this publication being discussed the task of diagnostic data filtration received from health monitoring systems of engineering structures. During operational period of conventional monitoring systems, initial diagnostic matrix contains as usual some of foreseen errors. Existence of those errors has various reasons, such as malfunction of data transmission form measuring devices, extraordinary data spikes, equipment failure, faults accumulated from extraneous signals, loss of data etc. Persistence of such kind of errors not just interdicts further expert analysis, but it blocks further solution regarding diagnostic with afterward forecast. Afore-said circumstances drastically diminish the effectiveness of monitoring technology as well as spoils certainty result. In connection therewith, authors set a challenge concerning algorithms search (either combination of algorithms) to actualize initial data filtration operation. Russky Island Bridge in Vladivostok, Russia was designated as a sample of monitoring site, where started from 2012 permanent monitoring system was in-service. The entire data from the abovementioned system should be accumulated within temporary storage with after-ward retrospective analysis. We applied such ways of static data filtration like time-series data, machine learning method plus implementation of embedded functions of information filtration. Be means of static methods completion we achieved the result of clear up to 50% of being diagnosed files. As for machine learning method it helped us to improve filtration function up to 60%. Passing through time-series method we made it about 70% of filtration quality. The best ever filtration outcome was shown by in Forecast Library via Artificial Programming Language (APL R) up to 98% of effectiveness, consequently that was the cause, why we apply this particular function for filtration performance of the initial diagnostic data nowadays. Filtered information further may be processed by machine analysis Artificial Intelligence (AI), which should ensure more precise estimation regarding service capability of unique structures.