Time-Frequency Analysis of Single-Point Engine-Block Vibration Measurements for Multiple Excitation-Event Identification

Display Omitted ANNs-solved vibration based parametric identification studies are reviewed.Factors which affect identification result are discussed.Pros and cons of ANN approaches are mentioned.Suggestions are given to potential researchers based on the discussion.Analysis with experimental results is provided to justify some point of view. Vibration behavior of any solid structure reveals certain dynamic characteristics and property parameters of that structure. Inverse problems dealing with vibration response utilize the response signals to find out input factors and/or certain structural properties. Due to certain drawbacks of traditional solutions to inverse problems, ANNs have gained a major popularity in this field. This paper reviews some earlier researches where ANNs were applied to solve different vibration-based inverse parametric identification problems. The adoption of different ANN algorithms, input-output schemes and required signal processing were denoted in considerable detail. In addition, a number of issues have been reported, including the factors that affect ANNs prediction, as well as the advantage and disadvantage of ANN approaches with respect to general inverse methods Based on the critical analysis, suggestions to potential researchers have also been provided for future scopes.

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Artificial neural networks for vibration based inverse parametric identifications

Knowing the pavement condition is essential for efficiently deciding on maintenance programs. Current practice is predominantly manual with only 0.4% of inspections happening automatically. All methods in the literature aiming at automating condition assessment focus on two defects at most, or are too expensive for practical application. In this paper, the authors propose a low-cost method that automatically detects pavement defects simultaneously using parking camera video data. The types of defects addressed in this paper are two types of cracks (longitudinal and transverse), patches, and potholes. The method uses the semantic texton forests (STFs) algorithm as a supervised classifier on a calibrated region of interest (myROI), which is the area of the video frame depicting only the usable part of the pavement lane. It is validated using data collected from the local streets of Cambridge, U.K. Based on the results of multiple experiments, the overall accuracy of the method is above 82%, with a p...

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Automated Detection of Multiple Pavement Defects

The enhancement of passengers’ comfort and their safety are part of the constant concerns for car manufacturers. Semiactive damping control systems have emerged to adapt the suspension features, where the road profile is one of the most important factors determining the automotive vehicle performance. Because direct measurements of the road profile represent expensive solutions and are susceptible to contamination (e.g. using laser and other visual sensors), this paper proposes a novel road profile estimator that offers the essential information (road roughness and its frequency) for the adjustment of the vehicle dynamics using conventional sensors, such as accelerometers or displacement/velocity sensors easy to mount, cheap, and useful to estimate all suspension variables. Based on the ${Q}$ -parametrization approach, an adaptive observer estimates the dynamic road signal; afterward, a Fourier analysis is used to compute the road roughness condition online and to perform an International Organization for Standardization (ISO) 8608 classification. Experimental results on the rear-left corner of a 1:5 scale vehicle, equipped with electro-rheological (ER) dampers, have been used to validate the proposed road profile estimation method. Different ISO road classes evaluate the performance of the proposed algorithm, whose results show that any road can be identified successfully at least 70% of the time with a false alarm rate lower than 5%; the general accuracy of the road classifier is 95%. A second test with variable vehicle velocity shows the importance of the online frequency estimation to adapt the road estimation algorithm to any driving velocity; in this test, the road is correctly estimated in 868 of 1042 m (an error of 16.7%). Finally, the adaptability of the parametric road estimator to the semiactiveness property of the ER damper is tested at different damping coefficients.

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Adaptive Road Profile Estimation in Semiactive Car Suspensions

A methodology for combustion detection in diesel engines through in-cylinder pressure derivative signal

Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals

Road profile is one of the most important factors affecting vehicle performance and characteristics. Direct measurement of the road profile is either slow or need expensive profilometers. Indirect methods that are inexpensive and can be installed on a fleet of vehicles are of main interest. On-vehicle sound and vibration measurements to predict road texture was investigated in two pilot studies. In the first study, it was shown that both interior and exterior measurements of tyre noise and axle acceleration can be used to distinguish fresh laid asphalt from worn. Eight asphalt road sections were successfully classified at 50 and 70 km/h based on octave bands levels. In the second pilot study, axle acceleration measurements and an inverse quarter-car model was used to reconstruct a well-defined test track at 30 and 50 km/h. Although low frequency problems and tyre resonances the results showed that indirect methods can be used to estimate road texture from field measurements.

Roger Johnsson

Papers

Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals

Methods for road texture estimation using vehicle measurements

A new test track for automotive squeak and rattle (S&R) detection

Walking sound annoyance vs. impact sound insulation from 20 Hz

Assessment of the change in similarity judgements of auralized engine sounds caused by changes in frequency resolution of transfer functions