A New Measure for Analysing Accelerometer Data towards Developing Efficient Road Defect Profiling Systems

Abstract

Aims: In this paper, we propose a new measure for analysing data obtained from an accelerometer with the aim of improving road surface condition monitoring and defect detection systems. Study Design: The study consisted of an experimental setup involving the use of an accelerometer embedded device connected to a laptop, all mounted in a vehicle for data acquisition and storage. Place and Duration of Study: Data gathering was conducted within the campus of the Federal University of Technology, Minna, for a period of two months. Methodology: The accelerometer was programmed to capture vibration signals along the x, y and z-axis with special interest in the z-axis because it monitors the up/down motion of the vehicle. Our algorithm uses what we call the “z-difference square” measure to analyse raw accelerometer data towards improving road defect detection. LABVIEW was used to configure the accelerometer device, while the algorithm for post data processing and statistical analyses were implemented in MATLAB. Results: Inferences drawn from the raw data and other statistical measures indicate that the proposed measure provides the advantage of using single threshold values for detection, inherent averaging, and potential for spatial localization of potholes, as compared to other statistical measures. Conclusion: The use of our proposed “z-difference square” measure for analysing accelerometer data will provide a simple yet efficient and effective statistical measure for improving road defect detection systems.