Impact detection using a machine learning approach and experimental road roughness classification

Abstract

First, this publication presents the experimental validation of a road roughness classification method. Second, an impact detection strategy for twowheeled vehicles is proposed including a classification of service loads, mild special events, and severe special events. The methods presented utilise the vehicle's onboard signals to gather field data. The modular road roughness classification system operates with the vehicle's transfer functions, and continuously classifies the road profile, according to ISO 8608. The method was successfully validated on test tracks with known road profiles. The impact detection strategy was developed using a supervised machine learning technique. Six road obstacles were ridden over using different velocities to invoke mild and severe special events. The most popular classifiers were trained for comparison and prediction of future observations. The developed impact detection strategy shows a high accuracy and was successfully validated using a k-fold cross-validation. The combination of the road roughness classification system and the impact detection strategy, enables a holistic field data acquisition of customer usage profiles, in the context of durability engineering. The collection of customer usage profiles improves vehicle design targets and enables a virtual load acquisition.

Engine valve clearance diagnostics based on vibration signals and machine learning methods

“…It is impossible to fully review the applications of these algorithms, or even their fields of application. For example, we can only mention such different areas as: prediction of students' exam results [6], monitoring of urban changes [11], classification of road roughness [15], segmentation of apple defects [19], intelligent system of rotor machine damage detection [20], classification of network traffic [24], or image processing and analysis [35]. There are many known attempts to use machine learning in machine and component diagnostics to determine the technical condition as well as the characteristics of the condition.…”

Response-based methods to measure road surface irregularity: a state-of-the-art review

“…Random forest classifier (RF) was used to combine information from both time and frequency domains for a controllable suspension system in [2], while the RF was combined with transfer function to develop a speed independent road classification strategy in [47]. Most recently, independent component analysis as a simple and fast method was developed in [48], and various MLs were compared in [49].…”

Spatial Factor—Using a Random Forest Classification Model to Measure an Internationally Comparable Urbanity Index

“…For example, Gorges at al. [29] used a similar approach in a classification problem to develop a motorcycle impact detection strategy. As part of that study, test riders categorized road maneuvers (special characteristics like crossing railroads, potholes, or kerbs) into two groups, "mild" or "severe".…”

“…Based on the idea of Gorges et al [29] and Short Gianotti et al [7], the experts defined and divided well-known zip codes into four categories: super-urban, urban, suburban/small town, and rural. On the one hand, the use of four categories for labeling is based on the literature.…”