Zhanqun Shi

Stability Analysis of a Turbocharger Rotor System Supported on Floating Ring Bearings

The stability of a turbocharger rotor is governed by the coupling of rotor dynamics and fluid dynamics because the high speed rotor system is supported on a pair of hydrodynamic floating ring bearings which comprise of inner and outer fluid films in series. In order to investigate the stability, this paper has developed a finite element model of the rotor system with consideration of such exciting forces as rotor imbalance, hydrodynamic fluid forces, lubricant feed pressure and dead weight. The dimensionless analytical expression of nonlinear oil film forces in floating ring bearings have been derived on the basis of short bearing theory. Based on numerical simulation, the effects of rotor imbalance, lubricant viscosity, lubricant feed pressure and bearing clearances on the stability of turbocharger rotor system have been studied. The disciplines of the stability of two films and dynamic performances of rotor system have been provided.

Modelling of Outer and Inner Film Oil Pressure for Floating Ring Bearing Clearance in Turbochargers

Floating ring bearing is widely used in turbochargers to undertake the extreme condition of high rotating speed and high operating temperature. It is also the most concerned by the designers and users alike due to its high failure rate and high maintenance cost. Any little clearance change may result in oil leakage, which in turn cause blue smoke or black smoke according to leakage types. However, there is no condition monitoring of this bearing because it is almost impossible to measure the clearance especially the inner clearance, in which the inner oil film directly bears the high speed rotation. In stead of measuring clearance directly, this paper has proposed a method that uses film pressure as a measure to monitor the bearing clearance and its variation. A non-linear mathematical model is developed by using Reynolds equations with non-linear oil film pressure. A full description of the outer and inner film is provided along both axial and radial directions. A numerical simulation is immediately carried out. Variable clearance changes are investigated using the mathematical model. Results show the relationship between clearance and film pressure.

Early Fault Diagnosis for Planetary Gearbox Based Wavelet Packet Energy and Modulation Signal Bispectrum Analysis

The planetary gearbox is at the heart of most rotating machinery. The premature failure and subsequent downtime of a planetary gearbox not only seriously affects the reliability and safety of the entire rotating machinery but also results in severe accidents and economic losses in industrial applications. It is an important and challenging task to accurately detect failures in a planetary gearbox at an early stage to ensure the safety and reliability of the mechanical transmission system. In this paper, a novel method based on wavelet packet energy (WPE) and modulation signal bispectrum (MSB) analysis is proposed for planetary gearbox early fault diagnostics. First, the vibration signal is decomposed into different time-frequency subspaces using wavelet packet decomposition (WPD). The WPE is calculated in each time-frequency subspace. Secondly, the relatively high energy vectors are selected from a WPE matrix to obtain a reconstructed signal. The reconstructed signal is then subjected to MSB analysis to obtain the fault characteristic frequency for fault diagnosis of the planetary gearbox. The validity of the proposed method is carried out through analyzing the vibration signals of the test planetary gearbox in two fault cases. One fault is a chipped sun gear tooth and the other is an inner-race fault in the planet gear bearing. The results show that the proposed method is feasible and effective for early fault diagnosis in planetary gearboxes.

Combustion Noise Analysis for Combustion and Fuels Diagnosis of a Compression Ignition Diesel Engine Operating with Biodiesels

In this article, the combustion noise of a compression ignition diesel engine operating with biodiesels has been investigated experimentally. It aims to explore an effective method for combustion process monitoring and fuel quality evaluation through analyzing the characteristics of the engine combustion noise. The experiments were conducted on a four-cylinder, four-stroke, direct injection and turbocharged diesel engine fueled with biodiesels (B50 and B100) and normal pure diesel, and operating under different loads and speeds. The signals of cylinder head vibration, engine noise, and in-cylinder pressure were measured during the tests. A coherent power spectrum analysis method was used to investigate the vibration and noise signals that related to the combustion process. The results showed that the noise components at the frequency band of 2–3 kHz are closely related to the combustion process. Subsequently, the Wigner–Ville distribution is employed to present the energy distribution of engine noise in the time-frequency domain. Then a band-pass filter based on fractional Fourier transform is developed to extract the main component of the combustion noise for feature extraction. The results show that the sound pressure levels of the extracted combustion noise of the test diesel engine fueled with biodiesels are higher than that fueled with diesel. This is also identical to the variation of in-cylinder pressure. The results demonstrate that the features of the extracted combustion noise can indicate the combustion characteristics and provide useful information for monitoring the combustion process and evaluating the fuel quality of diesel engines.

Application of EKF for an Electro-Hydraulic Servo System Using Model-Based Approach

This paper works on extended Kalman filter (EKF) for model-based fault detection of an electro-hydraulic system to deal with stochastic behaviour during control. A mathematical model of an electro-hydraulic system is developed. Some faults are introduced to evaluate the EKF fault detection method. Comparison of the EKF estimation accuracy and a linearised model-based accuracy shows the advantage of the EKF.

Cabin noise fault detection using deviation energy method in cross time and frequency domain

This paper proposes a new method to detect annoying sounds in-car cabins. Based on the fact that annoying sounds are usually triggered off by harsh road conditions and hence occur occasionally and instantly. The signals for such sounds would have a broadband spectrum in the frequency domain. From this understanding, a detection scheme is developed to find the instants of the abnormal sounds in the lengthy signal. This will be is a primary step in diagnosing the source of the sound. In particular, a new method, referenced as deviation energy (DE) method is developed to highlight the small changes in the high frequency range (above 500Hz) in the abnormal sounds. In addition, the detection parameters including average spectrum and detection thresholds are adaptive to different data frames corresponding to different driving conditions. This makes the detection scheme more generic and reliable. Case study shows that this DE-based detection scheme produces more accurate and reliable results, benchmarked with commonly used statistical measures such as RMS, Kurtosis and Euclidean distance.

Automatic Fault Detection Using a Model-Based Approach in the Frequency Domain

In this paper, the model-based approach is introduced into rotation machinery fault detection to achieve an automatic feature extraction. The paper starts with a brief review of the model-based approach, including model development, residual generation and fault detection and diagnosis. The applicability of this approach to rotation machinery is then considered. In order to overcome difficulties arising from phase shift and random measurements, the statistical performance of the vibration of rotation machinery is analysed in both time and frequency domains. A consistence model is developed using stochastic process theory. After model validation, the model-based approach is implemented in AC motor fault detection. The residual is generated by comparing the new measurement and the model prediction, by both subtraction and division. Fault detection results prove that the model-based approach is applicable to fault feature extraction for rotation machinery in the frequency domain.Copyright

Neural Network Modelling Applied for Model-Based Fault Detection

This paper aims to combine neural network modelling with model-based fault detection. An accurate and robust model is critical in model-based fault detection. However, the development of such a model is the most difficult task especially when a non-linear system is involved. The problem comes not only from the lack of concerned information about model parameters, but also from the inevitable linearization. In order to solve this problem, neural networks are introduced in this paper. Instead of using conventional neural network modelling, the neural network is only used to approximate the non-linear part of the system, leaving the linear part to be represented by a mathematical model. This new scheme of integration between neural network and mathematical model (NNMM) allows the compensation of the error from conventional modelling methods. Simultaneously, it keeps the residual signatures physically interpretable.Copyright