Electrical connectors are widely utilized for signal communications in automotive electronic systems whose performance is related to the reliability of the entire system. Electrical connectors are frequently affected by the engine vibration, resulting in fretting damages on electrical connector. In this thesis, the main propose is to find a signal analysis method to predict the fretting damages on fuel pump connector induced by engine vibration. The data of the fuel pump connector is studied from a vibration test of the four-cylinder engine and the dominating frequencies are used in the fretting test to verify the analysis method. The fretting damage is identified through visual inspection by microscope. The model of the connector is built in COMSOL to explain the fretting on the contact surfaces. The results present the signal analysis method can be directly used to predict the risk of fretting damages during the engine vibration. Some significant frequencies are pointed out as guidelines for future tests and optimization.

The main aim of this thesis is to design the cranckshaft for aircraft diesel engine with ordered basic parameters. The engine is flat with four-cylinder and opposed pistons. The thesis is focused on balancing of centrifugal and reciprocating forces and their moments, conceptual design of crankshaft and its stress calculation. The thesis is describing calculation of torsional vibrations as well.

Aircraft engines are one of the most heavily instrumented parts of an aircraft, and the data from various types of instrumentation across these engines are continuously monitored both offline and online for potential anomalies. Vibration monitoring in aircraft engines is traditionally performed using an order tracking methodology. Currently, there are no representative and efficient physics-based models with the adequate fidelity to perform vibration predictions in aircraft engines, given various parametric dependencies existing among different attributes such as temperature, pressure, and external conditions. This gap in research is primarily attributed to the limited understanding of mutual interactions of different variables and the nonlinear nature of engine vibrations. The objective of the current study is three-fold: (i) to present a preliminary investigation of tracked order vibrations in aircraft engines and statistically analyze them in the context of their operating environment, (ii) to develop data-driven modeling methodology to approximate a dynamical system from input-output data, and (iii) to leverage these data-driven modeling methodologies to develop highly accurate models for tracked order vibration in a turbo-fan engine valid over a wide range of operating conditions. Off-the-shelf data-driven modeling techniques, such as machine learning methods (eg., regression, neural networks), have several drawbacks including lack of interpretability and limited scope, when applying them to a complex multiscale multi-physical dynamical system. Moreover, for dynamical systems with external forcing, the identified model should not only be suitable for a specific forcing function, but should also generally approximate the input-output behavior of the data source. The author proposes a novel methodology known as Wavelet-based Dynamic Mode Decomposition (WDMD). The methodology entails using wavelets in conjunction with input-output dynamic mode decomposition (ioDMD). Similar to time-delay embedded DMD (Delay-DMD), WDMD builds on the ioDMD framework without the restrictive assumption of full state measurements. The author demonstrates the present methodology's applicability by modeling the input-output response of an Euler-Bernoulli finite element beam model, followed by an experimental investigation. As a first step towards modeling the tracked order vibration amplitudes of turbofan engines, the interdependencies and cross-correlation structure between various thermo-mechanical variables and tracked order vibration are analyzed. The order amplitudes are further contextualized in terms of their operating regime, and exploratory data analyses are performed to quantify the variability within each operating condition (OC). The understanding of complex correlation structures is leveraged and subsequently utilized to model tracked order vibrations. Switching linear dynamical system (SLDS) models are developed using individual data-driven models constructed using WDMD, and its performance in approximating the dynamics of the $1^{st}$ order amplitudes are compared with the state-of-the-art time-delay embedded dynamic mode decomposition (Delay-DMD) and Lasso regression. A parametric approach is proposed to improve the model further by leveraging previously developed WDMD and Delay-DMD methods and a parametric interpolation scheme. In particular, a recently developed pole-residue interpolation scheme is adopted to interpolate between several linear, data-driven reduced-order models (ROMs), constructed using WDMD and Delay-DMD surrogates, at known parameter samples. The parametric modeling approach is demonstrated by modeling the transverse vibration of an axially loaded finite element (FE) beam, where the axial loading is the parameter. Finally, a parametric modeling strategy for tracked order amplitudes is presented by constructing locally valid ROMs at different parametric samples corresponding to each pass-off test. The performance of the parametric-ROM is quantified and compared with the previous frameworks. This work was supported by the Rolls-Royce Fellowship, sponsored by the College of Engineering, Virginia Tech. / Doctor of Philosophy / Vibrations in commercial aircraft engines are of utmost importance as they directly translate to aviation health and safety, and hence are continuously monitored both online and offline for potential abnormalities. Notably, this is of increased interest with the abundance of air transportation in today's world. However, there is limited understanding of the complex higher vibration in aircraft engines. Vibration engineers often face ambiguity when interpreting higher vibrations. This can often lead to a lengthy investigative process resulting in longer downtime and increased testbed occupancy, ultimately leading to revenue loss. It is often hypothesized that prior engine running conditions such as shutdown/cooling time between one engine run to another engine run affect the vibration profile. Nonetheless, there exists a gap in understanding tying together various historical operational conditions, temperature, pressures, and current operational conditions with the expected vibration in the engine. This study aims to fill some of these gaps in our understanding by proposing a data-driven strategy to model the vibrations in commercial aircraft engines. Subsequently, this data-driven model can serve as a baseline model to compare the observed vibrations with the model predicted vibration and supplement physics-based models. The data for the present study is generated by operating a commercial turbofan engine in a testbed. With the advent of machine learning and data fusion, various data-driven techniques exist to model dynamical systems. However, the complexity of the turbofan engine vibrations calls for developing new techniques applicable towards modeling the vibration characteristics of a turbofan engine. Specifically, this dissertation details the development of a novel methodology called Wavelet-based Dynamic Mode Decomposition (WDMD) and applies the technique to model input-output characteristics of various dynamical systems ranging from a numerical finite element (FE) beam to an experimental free-free beam to shaft vibrations in a turbofan engine. The study finally presents an improved modeling framework by incorporating the existing techniques with parametric dependencies. This enables the existing method to consider slight differences existing from one engine run to another, such as the history of the engine, the shutdown time, and the outside environmental parameters.

Researchers are trying to find a solution for reducing the vibration of the engine with minimum changes to the engine mounts. Several researches and main giant car companies have presented valuable effort in these areas but still new research is needed to improve the control system. The present research carried out a comprehensive study of the state of art methods to suppress unwanted vibration from the engine to the passenger cars. This research was designed based on the objective of the Trelleborg Company to investigate the influence of Active Vibration Control (AVC) on the real engine. Therefore, this thesis tried to challenge the vibration problem with practical engineering approach by implementing different types of controllers experimentally and applying them on the real petrol engine. Inversing controlling technique and PID controller tuned with different methods (Ziegler Nichols and tyreus-luyben) have been tested here on two separate platforms; unbalanced DC motor and petrol engine. In addition, as a requirement of the study, the resonance frequency and related mode shapes of the system was investigated experimentally. It is also shown that using suitable filters can help elimination of high frequency noises in the control signals. This study experimentally tests PID controller with mentioned tuned methods on a real engine with this specific setup for the first time. A new scheme was developed with "mode shapes specific controller system", according to which the shaker position and the controller parameters were specified according to the system mode shapes. The result of applying controllers shows that both control methods have a similar effect on vibration reduction. A 33% - 37% reduction on DC motor achieved in different frequencies (20Hz, 37.5Hz and 46.2Hz) with different control methods, and about 10% reduction on petrol engine at resonance frequency while the shaker IV40 (with max 30N force) was placed on the chassis. For reducing the vibration transmitted from the engine to the chassis, for the first time the shaker was placed on the engine (unlike in previous studies where the shaker was placed on the chassis). Using shaker IV40 placed on the engine results in a 20% reduction in vibration transmission, which is a significant improvement in comparison with having the shaker on the chassis. The optimum result was achieved using shaker IV45 (Max 50N force), which yielded a vibration reduction of 33%.