Abstract
Smartphone MEMS (Micro Electrical Mechanical System) accelerometers have relatively low sensitivity and high output noise density. Therefore, it cannot be directly used to track feeble vibrations such as structural vibrations. This article proposes an effective increase in the sensitivity of the smartphone accelerometer utilizing the stochastic resonance (SR) phenomenon. SR is an approach where, counter-intuitively, feeble signals are amplified rather than overwhelmed by the addition of noise. This study introduces the 2D-frequency independent underdamped pinning stochastic resonance (2D-FI-UPSR) technique, which is a customized SR filter that enables identifying the frequencies of weak signals. To validate the feasibility of the proposed SR filter, an iPhone device is used to collect bridge acceleration data during normal traffic operation and the proposed 2D-FI-UPSR filter is used to process these data. The first four fundamental bridge frequencies are successfully identified from the iPhone data. In parallel to the iPhone, a highly sensitive wireless sensing network consists of 15 accelerometers (Silicon Designs accelerometers SDI-2012) is installed to validate the accuracy of the extracted frequencies. The measurement fidelity of the iPhone device is shown to be consistent with the wireless sensing network data with approximately 1% error in the first three bridge frequencies and 3% error in the fourth frequency.
Highlights
Smartphone technology has advanced rapidly over the last decade
Method in processing theutilizing iPhone signal significantly improves the device. Regardless of the latter point, the results show that the 2D-frequency-independent underdamped pinning stochastic resonance (FI-UPSR)
This article investigates the feasibility of utilizing smartphones for it investigates the potential for a low-grade iPhone accelerometer to extract the firstMore four
Summary
Smartphone technology has advanced rapidly over the last decade. These ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field detectors. For the same scanning frequency, the noise RMS is 10−4 g, as presented in Figure 2b (red straight lines) These two issues of sensitivity and noise limit the iPhone accelerometer’s capacity to extract frequencies for the bridge modes of due to the applied acceleration directly into a digital format. RMS is 10−4 g, as presented in Figure 2b (red straight lines) These two issues of sensitivity and noise limit the iPhone accelerometer’s capacity to extract frequencies for the bridge modes of vibration. If the input signal is strong the particle will strongly hop from one position to the pinning pints of the potential shape At this point, resonance is approached between the input signal and the extracted signal, as shown in.
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