Noninvasive Methods for Fault Detection and Isolation in Internal Combustion Engines Based on Chaos Analysis

The classic monitoring methods for detecting faults in automotive vehicles based on on-board diagnostics (OBD) are insufficient when diagnosing several mechanical failures. Other sensing techniques present drawbacks such as high invasiveness and limited physical range. The present work presents a fu...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-10, Vol.21 (20), p.6925
Main Authors: Lima, Thyago L., Filho, Abel C. L., Belo, Francisco A., Souto, Filipe V., Silva, Thaís C. B., Mishina, Koje V., Rodrigues, Marcelo C.
Format: Article
Language:English
Subjects:
Algorithms
Alternators
Automobiles
Automotive engines
chaos analysis
Chaos theory
Determinism
Fatalities
Fault detection
Fault diagnosis
Fractal geometry
Fractals
Internal combustion engines
Medical diagnosis
Methods
misfire
Multiresolution analysis
Signal processing
Smartphones
Sound
sound analysis
Spectrum allocation
Time series
Vehicles
Wavelet analysis
Wavelet transforms
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Summary:The classic monitoring methods for detecting faults in automotive vehicles based on on-board diagnostics (OBD) are insufficient when diagnosing several mechanical failures. Other sensing techniques present drawbacks such as high invasiveness and limited physical range. The present work presents a fully noninvasive system for fault detection and isolation in internal combustion engines through sound signals processing. An acquisition system was developed, whose data are transmitted to a smartphone in which the signal is processed, and the user has access to the information. A study of the chaotic behavior of the vehicle was carried out, and the feasibility of using fractal dimensions as a tool to diagnose engine misfire and problems in the alternator belt was verified. An artificial neural network was used for fault classification using the fractal dimension data extracted from the sound of the engine. For comparison purposes, a strategy based on wavelet multiresolution analysis was also implemented. The proposed solution allows a diagnosis without having any contact with the vehicle, with low computational cost, without the need for installing sensors, and in real time. The system and method were validated through experimental tests, with a success rate of 99% for the faults under consideration.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21206925