Fault Detection and Diagnosis of an Internal Combustion Engine

ion Hierarchy Structural Functional Process HistoryBased Qualitative Expert System Qualitative Trend Analysis Quantitative Statistical Principal Component Analysis and Partial Least Squares Statistical Classifiers Neural Network Master’s Thesis – Y. Feng McMaster University – Mechanical Engineering 3 al., 2012). Knowing that an internal combustion engine is a complex system with many rotary and reciprocating components which makes accurate modeling very difficult, this thesis turns to the process history-based method for the misfire FDD. 1.2 Research Objective This thesis is based on previous work by Haqshenas (2012) and Ahmed (2011). Haqshenas proposed a powerful data analysis tool based on the concept of multi-scale principal component analysis (MSPCA). MSPCA combines discrete wavelet analysis and principal component analysis (PCA). This tool was able to detect and localize ICE fault conditions such as a lash adjuster fault in both crank angle domain and frequency domain. Another contribution of Haqshenas' research was the covariance-based indices that indicate the faulty variable and faulty scale. Ahmed proposed a multi-layer perceptron (MLP) neural network training technique based on the smooth variable structure filter (SVSF), which was found to have good generalization capability and the ability to avoid premature convergence to local minima. Ahmed also applied this technique to a fault detection and isolation system which classified two faulty conditions (piston chirp and missing bearing faults) plus one baseline condition with high accuracy. As an extension to Haqshenas' and Ahmed's work, this research aims to achieve the following objectives: 1. Build an FDD experimental setup. 2. Propose novel FDD methods based on data processing techniques and artificial neural networks. 3. Apply these FDD methods to the ICE misfire fault. Master’s Thesis – Y. Feng McMaster University – Mechanical Engineering 4 1.3 Thesis Organization This thesis is organized as follows. Chapter 2 reviews the relevant literature on data processing techniques and artificial neural networks for FDD. This chapter also reviews the prior work on FDD of ICE misfire. Chapter 3 describes fault diagnosis algorithms obtained using data processing techniques, including one new statistical index and two kernel MSPCA algorithms based on wavelet transform and variational mode decomposition respectively. Chapter 4 presents an SVSFbased training algorithm for RBF neural networks. It also describes the method for generating the feature vector used as the networks' input. Chapter 5 first describes the experimental setup and the seeding of the ICE misfire faults. Next it presents experimental results and a comparison of the performance of the FDD methods. Chapter 6 draws the conclusions of the research and makes recommendations for future work. Master’s Thesis – Y. Feng McMaster University – Mechanical Engineering 5 Chapter 2 Literature Review This chapter reviews the relevant literature on FDD methods for mechanical systems. Signal processing techniques and their application to FDD are covered first, followed by artificial neural networks and their application to FDD. Second, FDD methods for detecting misfire faults with ICE are reviewed. The chapter ends with a summary of the findings. 2.1 Signal Processing Techniques for Fault Detection and Diagnosis Signal processing techniques are the basis for many FDD methods. This section presents the application of the Fourier transform, wavelet transform, empirical mode decomposition, variational mode decomposition and principal component analysis to the FDD problem. 2.1. 1 Fourier Transform The Fourier transform (FT) provides an alternative representation of a signal in the frequency domain rather than the time domain. Because of its usefulness in practical applications, FT has become a key tool in numerous engineering branches (Prandoni et al., 2008). The following are examples of its application to FDD. Shim et al. (2010) proposed an FDD system for an uninterruptible power supply (UPS). The system performed real time fast Fourier transform (FFT) on the output current of the UPS' inverter side and analyzed its harmonics. Four cases that were studied in the paper: normal linear load case, normal rectifier load case, turn-on resistance increase fault, and turn-on delay misfiring fault. The faulty cases could be distinguished by comparing the even harmonics in the current. The even harmonics only took 5.3% and 5.6% of the current for the normal linear load and normal rectifier Master’s Thesis – Y. Feng McMaster University – Mechanical Engineering 6 load respectively. However, when the turn-on resistance increased a fault condition occurred, and the even harmonics increased to 18.8%. Also, the even harmonics increased sharply to 40.2% when there existed the turn-on delay misfiring fault. Their experimental results showed that the FFT was a simple and practical tool for detecting fault conditions in UPS. Mamis et al. (2011) developed a method for fault isolation for power transmission lines. Transient voltage and current waveforms of the transmission lines were first obtained and then transformed into the frequency domain using the FFT. The first harmonic of the spectrum was utilized for determination of the fault location according to the travelling wave theory. The percentage error for the fault distance was within 0.47% to 3.20% when the fault distance changed from 40 km to 320 km. Other experiments also found that the measured fault distance were not affected by resistance, phase angle and source inductance. 2.1.2 Wavelet Transform The wavelet transform is a useful tool for analysing aperiodic, noisy, intermittent and transient signals. Compared with the Fourier transform, the wavelet transform not only finds the frequency components in the signal but also tell at what instant a particular frequency arises. Due to its advantages, the wavelet transform has been widely applied in different fields, such as fault detection, audio processing and image processing (Sifuzzaman et al., 2009). The following are a few examples of its application to FDD. Lin and Qu (2000) applied the wavelet transform in gear fault feature extraction. They first proposed a denoising method based on Morlet wavelet whose optimal parameter was obtained using minimal wavelet entropy method. Then this denoising method was used to filter the Master’s Thesis – Y. Feng McMaster University – Mechanical Engineering 7 vibrational signal of a gear box where a tooth crack failure resulted in a very clear periodic impulse with the feature period of 0.24s. Sun and Tang (2002) used the wavelet transform in fault detection for bearings in freight cars. First the wavelet transform modulus maxima and their locations were obtained from the vibration signal. Then thresholding and signal reconstruction were performed for denoising. The reconstructed signal consisted of only defect-induced impulse clusters. The errors between the estimated and measured faulty frequencies were only 0.8%. Kang et al. (2001) developed an FDD method for power transformers based on the wavelet transform. The vibration of the equipment was measured by one accelerometer. The envelope of the vibration was extracted using the Hilbert transform and then the continuous wavelet transform (CWT) was performed on the envelope signal to obtain the wavelet coefficients. Next, local maxima of these wavelet coefficients were found and selected to get the CWT vertical ridges. Finally the dominant ridgelines were determined according to the number of expected transient events. This method detected that the weak spring condition caused longer delay time between the dominant ridgelines. For example the delay time between the first two dominant ridgelines was 25.4 ms for the weak spring condition while it was 19.0 ms for the normal condition. This method also found that when the equipment had worn contacts, the strength of the dominant ridgelines decreased significantly, e.g. the strength of the 4 dominant ridgeline decreased from 11.28 to 6.30. 2.1.3 Empirical Mode Decomposition Empirical Mode Decomposition (EMD) proposed by Huang et al. is a time-frequency signal processing method and is useful for analyzing natural signals, which are most often non-linear Master’s Thesis – Y. Feng McMaster University – Mechanical Engineering 8 and aperiodic. The method is fully adaptive and generates intrinsic mode functions (IMFs) to represent the data. The IMFs represent a complete set of locally orthogonal basis functions whose amplitude and frequency may vary over time. (Zeiler et al., 2010). EMD has been applied to many fields in engineering and science. Loutridis (2004) proposed a damage detection method for pairs of gears. In this method, the vibration signal resulting from the gears were decomposed into nine intrinsic mode functions (IMF), namely C1, C2 to C9. Since C2 best represented the time scale of the transient caused by gear damage, the energy of mode C2 was selected as the diagnostic feature. The energy of a healthy gear pair was relatively low at C2. It increased when damage happened. When a gear pair had a 75% crack, the peak energy was more than three times that of a pair with only a 15% crack. Yu et al. (2005) proposed a method for the fault diagnosis of roller bearings. The vibration signal of a roller bearing was first translated into time-scale representation by orthogonal wavelet bases. Then the envelope signal was obtained by envelope spectrum analysis of the wavelet coefficients that represent the high frequency components. After applying EMD and Hilbert transform, the local Hilbert marginal spectrum of the envelope signal was obtained, from which the fault pattern can be identified. In the paper, an outer-race fault was introduced in the roller bearing with a characteristic frequency of 76 Hz. The local marginal spectrum shows the frequency peaks of 76 Hz and its double frequency of 15