ArticleSensors (Basel, Switzerland)2022
Misfire Detection in Automotive Engines Using a Smartphone through Wavelet and Chaos Analysis.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Symmetrized Dot Patterns and CNN-Based Acoustic Signal Analysis for Fault Diagnosis in Internal Combustion Engines.Sensors (Basel, Switzerland) · 2026Article
- Sensorless Speed Estimation of Induction Motors through Signal Analysis Based on Chaos Using Density of Maxima.Entropy (Basel, Switzerland) · 2024Article
- Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization.Sensors (Basel, Switzerland) · 2022Article
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Authors and funding
6 authors.
Funding
Abstract
Besides the failures that cause accidents, there are the ones responsible for preventing the car's motion capacity. These failures cause inconveniences to the passengers and expose them to the dangers of the road. Although modern vehicles are equipped with a failure detection system, it does not provide an online approach to the drivers. Third-party devices and skilled labor are necessary to manage the data for failure characterization. One of the most common failures in engines is called misfire, and it happens when the spark is weak or inexistent, compromising the whole set. In this work, two algorithms are compared, based on Wavelet Multiresolution Analysis (WMA) and another using an approach performing signal analysis based on Chaos using the density of maxima (SAC-DM) to identify misfare in a combustion engine of a working automotive vehicle. Experimental tests were carried out in a car to validate the techniques for the engine without failure, with failure in one piston, and with two failed pistons. The results made it possible to obtain the failure diagnosis for 100% of the cases for both WMA and SAC-DM methods, but a shorter time window when using the last one.
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Registered trials
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