Pattern recognition for HEV engine diagnostic using an improved statistical analysis

Detecting early symptoms of engine failure is a crucial phase in an engine management system to prevent poor driving performance and experience. This paper proposes a Hybrid Electric Vehicle (HEV) engine diagnostics using a low-cost piezo-film sensor, an analysis with improved statistical method and verification by a Support Vector Machine (SVM). The current engine management system is unable to evaluate the performance of each cylinder operation. Eventually, it affects the whole hybrid vehicle system, particularly in the mode of charging and accelerating. This research aims to classify the combustion to monitor the condition of sparking activity of the engine by using the Z-freq statistical method. Piezo-film sensors were mounted on the Internal Combustion Engine (ICE) wall of each hybrid vehicle for vibration signal measurements. The engine runs at different speeds, the vibration signals were then recorded and analysed using the Z-freq technique. A machine learning tool referred to as Support Vector Machine was used to verify the classifications made by the Z-freq technique. A significant correlation was found between the voltage signal and calculated Z-freq coefficient value. Moreover, a good pattern was produced within a consistent value of the engine speed. This technique is useful for the hybrid engine to identify different stages of combustion and enable pattern categorisation of the measured parameters. These improved techniques provide strong evidence based on pattern representation and facilitate the investigator to categorise the measured parameters

The Application of I-kazTM-based Method for Tool Wear Monitoring Using Cutting Force Signal

AbstractTool wear monitoring is important in machining industries for controlling the quality of machined parts that helps to improve the productivity. To date, many monitoring system methods are developed by utilizing various signals, and cutting force is one of the signals in machining process that has been widely used for tool wear monitoring. This paper presents the application of I-kaz based method to analyze the cutting force signal for monitoring the status of tool wear in turning process. Experiments were carried out by turning hardened carbon steel, and cutting force signals were measured by two channels of strain gauges that were mounted on the surface of tool holder. In this study, I-kaz 2D is one of the derivatives of I-kazTM method that has been utlized to integrate two components of cutting force signals. It differs from the I-kaz method, whereby the signal does not need to be decomposed to three different frequency ranges. The analysis of results using I-kaz 2D and I-kazTM methods, revealed that both methods can be used to determine tool wear progression during turning process and feed force is very significant due to flank wear

Strain Signal Characterisation Using the 4th Order of Daubechies Wavelet Transform for Fatigue Life Determination

This paper presents the significance of Discrete Wavelet Transform to provide more accuracy by using the Wavelet (Db4) Daubechies approach to analyse original signals obtained from the actual responses of an automotive suspension system. The time-frequency domain considers both time and frequency parameters, making this approach more efficient compared to the time domain and frequency domain approaches. An original signal was obtained from three road types:  highway road, rural road and residential road. These signals were classified into 12 levels of decomposition where each level contained its own frequency range. The decomposed signals were then analysed using fatigue analysis to obtain the fatigue damage at each interval, which was then compared to the original signal. Results show that the decomposition signals from levels 1 to level 2 for highway and residential roads and level 1 to level 3 for rural roads gave a significant value of fatigue life located in the range of 2:1 and 1:2 in the fatigue life prediction graph. In summary, the Daubechies (Db4) Wavelet approach is capable of correlating the fatigue life of those components that contribute to the failure of a suspension system

Spark plug failure detection using Z-freq and machine learning

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

The study of polymer material characterisation using M-Z-N statistical analysis method

This paper proposes an implementation of alternative statistical signal analysis method in characterising material properties of polymer using impulse excitation technique (IET) in accordance with ASTM E1876 standard. Five types of cylindrical shape polymer specimens are used, namely acrylics (AC), poly vinyl chloride (PVC), polyethylene (PE), cast nylon (MC), and polyoxymethylene (POM). Experimental procedure is done based on non-destructive testing (NDT) concept by tapping the specimens using an impact hammer within a specific range of impact force, while accelerometer sensor Endevco 751-100 is used to detect the vibration produced. The detected vibration and the impact force signal which is triggered by impact hammer are captured using NI 9234 data acquisition device and computer. The signal is interpreted and statistically analysed using Mesokurtosis Zonal Non-parametric (M-Z-N) analysis method. As a result, mathematical model equations for determining two material properties which are tensile strength and thermal conductivity have been successfully developed. They are derived through correlation between the signal data and M-Z-N coefficient. Verification of the equation is done and the calculated errors to be in the range of 5.55% to 9.74%. Hence, this proves that the correlation can be used as a standard for determining these material properties through M-Z-N method, which is efficient and low cost

Acoustic energy harvesting using flexible panel and PVDF films: a preliminary study

Acoustic energy harvesting from ambient noise utilizing flexural vibration of a flexible panel is investigated. A flexural vibration of a flexible panel is use to extract more energy from the ambient noise level where piezoelectric materials of PVDF films are attached at the plate edges. The energy harvesting can be obtained with a maximum output power of 120 pW at the sound pressure level of 97.3 dBA

Novel Statistical Clustering Method for Accurate Characterization of Word Pronunciation

This paper discusses the development method to determine the accuracy of pronunciation of the word using global statistical signal analysis parameters. An engineering word that has been chosen is ‘leaching’. The pronunciation of the word ‘leaching’ in the French language has been recorded from 1 native speaker and 4 students. The recording processes use a microphone-laptop system configuration and the signal analyzing processes use MATLAB software. Time and frequency domain plots show a variety of waveforms according to the recorded pronunciation. For data processing, statistical signal analysis parameters involved to extract the signal’s features are kurtosis, root mean square and skewness. The mapping process has been performed to cluster each data. The position of the samples from the students is referred to the samples from the native speaker. The result of the accuracy of the pronunciation of words for each student can be evaluated through the comparison of the position of all the samples. In conclusion, the development of mapping and clustering methods are able to characterize the accuracy of the pronunciation of words

Effect of disc brake squeal with respect to thickness variation: Experimental Modal Analysis

Disc brake or rotor squeal is an ongoing problem that occurs in the automotive industry. An undesirable disc brake noise problem can arise after a period of time of usage. The purpose of this paper is to investigate the structural dynamic behaviour of disc brakes with different wear thickness by using Experimental Modal Analysis. The wear thickness of disc brake rotors are 0.5 mm, 1.0 mm and 1.5 mm from the original thickness of 15.8 mm and 3.2234 kg weight. The modal parameters such as natural frequency, damping ratio, and mode shape are obtained in a free-free condition by using an impact hammer test. For original thickness of disc brake rotor, the first eight natural frequencies are 1256.4 Hz, 2486.9 Hz, 2654.9 Hz, 3092.1 Hz, 3348.7 Hz, 3407.0 Hz, 4130.0 Hz, and 5709.6 Hz. The results show that the natural frequency decreases when the thickness reduction increased at the same mode. It can be concluded that the wear effect of the disc brake rotor is one of the factors which may lead to the brake squeal problem due to the reduction of the natural frequency of the disc brake rotor

Investigation of Internal Gas Leakage on the Gate Valve using Acoustic Signal

The gate valve is primarily used for starting/stopping the flow of fluids. It is suitable for most fluids such as water and chemicals as well as air, steam and gas in petrochemical and refinery plants that require high temperature and low pressure. The aim of this study is to define the frequency domain using AE signals, such as RMS and ASL, to determine the internal gas leakage. The conducted experiment employed a 4-inch diameter gate valve installed in the middle of the pipe length. To simulate industrial applications, the AE signals were observed at low-frequency (between 18.6 kHz to 19.5 kHz), with inlet pressures between 100 to 800 kPa and leakage rates between 0.5 percent to 2 percent. The frequency domain between 18.6 to 19.5 kHz and the inlet pressure of 100 to 800 kPa were displayed as the Root Mean Square (RMS) and Average Signal Limit (ASL). The pressure difference between the inlet and outlet influences the AE signal. The frequency spectrum can be correlated with the pressure leakage, thus providing leakage conditions. Therefore, the obtained results can be employed in industrial applications

Research and modelling of surface roughness, cutting forces and I-kaz coefficients for S42C in turning using response surface methodology

This paper presents the optimization in machining processes on the cutting parameters for the S45C in turning process using the response surface method (RSM). The experimental work conducted investigates the influence of cutting parameters on statistical analysis of signals and surface quality. The paper also presents a statistical analysis of signal processing. The cutting force was measured during machining using the Kistler 9129AA dynamometer to monitor the force signals and the data was analyzed using the I-kazTM method of statistical analysis. This statistical analysis was used to assess the effect of force signals during the machining process. The RSM models for Ra and Rz, and I-kaz coefficients (Z) have been developed with ANOVA and multiple regression equations. The models also were compared and validated with the predicted and measured of Ra and Rz values, and I-kaz coefficients. The optimal configuration of cutting parameters was observed at 200 m/min, 0.1 mm/rev and 0.521 mm with desirability of 95.9%. It is observed that the models developed are suggested to be utilized for predicting surface roughness values and I-kaz coefficients for the machining of S45C steel