The aim of this study was to make a method usable in an early detection of malfunction, e.g., abnormal vibration/fluctuation in recorded signals. We conducted experimentations of heart health and structural health monitoring. We collected natural world signals, e.g., heartbeat fluctuation and mechanical vibration. For the analysis, we used modified detrended fluctuation analysis (mDFA) method that we have made recently. mDFA calculated the scaling exponent (SI, the acronym SI is derived from the scaling indices) from the time series data, e.g., R-R interval time series obtained from electrocardiograms. In the present study, peaks were identified by our own method. In every single mDFA computation, we identified ∼2000 consecutive peaks from a data: “2000” was necessary number to conduct mDFA. mDFA was able to distinguish between normal and abnormal behaviors: Normal healthy hearts exhibited an SI around 1.0, which is a phenomena comparable to 1/f fluctuation. Job-related stressful hearts and extrasystolic hearts both exhibited a low SI such as 0.7. Normally running car’s vibration — recorded steering wheel vibration — exhibited an SI around 0.5, which is white noise like fluctuation. Normally spinning ball-bearings (BB) exhibited an SI around 0.1, which belongs to the anti-correlation phenomena. A malfunctioning BB showed an increased SI. At an SI value over 0.2, an inspector must check BB’s correct functioning. Here we propose that healthiness in various cyclic vibration behaviors can be quantitatively analyzed by mDFA.

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