Spark plug failure detection using Z-freq and machine learning

Abstract

Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a novel statistical signal-based analysis called Z-freq to improve the exploration. This study is an exploration of the time and frequency content attained from the engine after it goes under a specific situation. Throughout the trial, the misfire was formed by cutting the voltage supplied to simulate the actual outcome of the worn-out spark plug. The failure produced by fault signals from the spark plug misfire were collected using great sensitivity, space-saving and a robust piezo-based sensor named accelerometer. The achieved result and analysis indicated a significant pattern in the coefficient value and scattering of Z-freq data for spark plug misfire. Lastly, the simulation and experimental output were proved and endorsed in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. Finally, it confirmed that the proposed technique capably to make a diagnosis: fault detection, fault localization, and fault severity classification.

Z-Freq Hybrid: Signal Analysis Based Car Air Conditioning Compressor Monitoring Technique Using Accelerometer and Piezo-Film Sensor

“…Figure 3 shows a green scattered graph voltage (V) versus acceleration (m/s 2 ) at varying engine revolution speeds for 320g R134a with the compressor on. The scattered graph ascends as the data points increase in value as the RPM increases [12,21,22]. The data points are also tightly clustered as they are very close to each other.…”

“…Ngatiman et. al in year 2021 [22]. Z-freq Hybrid is a new statistical signal analysis from two different types of sensors based on frequency domain.…”

Wireless Hybrid Vehicle Three-Phase Motor Diagnosis Using Z-Freq Due to Unbalance Fault

“…From the tabulated data, an initial objective has been met, which this new method named Z-freq can differentiate the fault pattern. [12], [13] From this observation, there is a little increment for static fault, about 12% from the normal condition for Z-freq value, a significant increment of about 43% for coupled fault and 51% for dynamic fault. This little percentage difference between coupled and dynamic also occurred in I-kaz and RMS.…”