Nonlinear phase characteristics of ultrasonic transducers introduce a frequency deviation in ultrasonic signals. In this paper, a new concept for ultrasonic indoor positioning based on instantaneous frequency of ultrasonic signals is presented. In validation experiments using RH measured by a thermo-hygrometer as a comparison, the relative errors of the acoustically measured RH are within 9.9% in the RH range of 40.7–87.1%, and the standard deviation is within 4.8%. After obtaining these two parameter values, the sound speed measured by the system is closer to the theoretical value at the same RH point. Then, equivalent sound path length and systematic delay are estimated using the least squares method. To improve the accuracy of the sound speed measurement, sound speed under different RH points is obtained through reference RH experiments and substituted into the calibration equation. Sound travel time is calculated by cross-correlating the sound signals received by the two acoustic sensors. A 0.1 s linear chirp signal in the frequency range of 200–500 Hz is selected as the sound source. The influence of air temperature, atmospheric pressure, and air constituent concentrations on the RH measurement is analyzed theoretically. Sound speed mainly depends on air temperature, humidity, atmospheric pressure, and air composition. Air humidity is acquired by measuring sound speed in the air. In this paper, a novel air humidity measurement system using low-frequency sound waves as a sound source and two acoustic sensors is proposed. Existing methods use ultrasonic waves as a sound source and air humidity is measured by measuring the sound attenuation. The acoustic method is a novel technique to measure air humidity non-intrusively. Therefore, the objective of the current study was to identify sensor-based PHM, emphasizing different fault identification and isolation (FDI) techniques with challenges and gaps existing in this field.Īir relative humidity (RH) is an important control parameter in many industrial processes. There is a lack of surveys available related to sensor-based PHM of AVs in the literature.
The PHM of AVs has recently been introduced and is still progressing. Thus, the prognostic health management (PHM) of ADAS is important for the smooth and continuous operation of AVs. However, the continual use of multiple sensors and actuators of the ADAS can lead to the failure of AV sensors. An ADAS is composed of various sensors: radio detection and ranging (RADAR), cameras, ultrasonic sensors, and LiDAR. With an increasing number of electronic control units and a combination of multiple sensors, there are now sufficient computing aptitudes in the car to support ADAS deployment. An automated system provided by the ADAS in autonomous vehicles is a salient feature for passenger safety in modern vehicles. Improvement of passenger safety is one of the key factors for evolving AVs.
#Speed of sound vs temperature driver#
Recently, the advanced driver assistance system (ADAS) of autonomous vehicles (AVs) has offered substantial benefits to drivers.
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