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Noise source identification and separation of internal combustion engines is an effective tool for engine NVH (noise, vibration and harshness) development. Among various experimental approaches, noise source identification using signal processing has attracted extensive attention because of that the signal can be easily acquired and the requirements for equipment is relatively low. In this paper, variational mode decomposition (VMD) combined with independent component analysis (ICA) is used for noise source identification of a turbo-charged gasoline engine. Existing algorithms have been proved to be effective to extract signal features but also have disadvantages. One of the key problems in presently used method is that the main components of the signal, i.e. the main source of the noise, are unknown in advance. Thus the parameters selection of signal processing algorithms, which has a significance influence on the identification result, has no uniform criterion.

As a major power source, diesel engines are being widely used in a variety of fields. However, because of complex structure, some faults which cannot be detected by direct signals would occur on engines and even lead to accidents. Among all kinds of indirect signals, vibration signal is the most common choice for faults detection without disassemble because of its convenience and stability. This paper proposed a novel approach for detecting multiple engine faults based on block vibration signals using variational mode decomposition (VMD) and echo state network (ESN). Since the quadratic penalty has a great influence on adaptable VMD that may make expected component signals cannot be extracted exactly, this paper proposed a dynamic quadratic penalty value, which will change with decomposing level. This paper selected a best dynamic quadratic penalty value by analyzing a large amount of data and results showed that this approach can decompose signals more exactly.

The inverse boundary element method (IBEM) is presented to accurately identify the noise sources of a diesel engine in this study. The sound pressures on four near-field planes were measured as inputs for the method. Then, the acoustic model of the full diesel engine was established using the boundary element method, and the acoustic transfer vectors (ATV) between the surface normal velocity and acoustic pressure at field points were calculated over the frequency range of interest. Based on the measured sound pressure and the ATVs, the surface normal velocity distribution of the diesel engine was reconstructed by the IBEM. The reconstructed pressures at two reference field points were compared with the measured ones. Furthermore, the panel contribution of each engine component was analyzed through the reconstructed surface velocity.

As the intake noise is a major contributing factor to automotive passenger compartment noise levels, it has received much more attention than before. Because the plastic manifolds could induce and transmit more noise owing to their lighter weight, aerodynamic noise has become a more serious problem in plastic manifolds than in conventional aluminum-made manifolds. Due to the complexity of aerodynamic noise of the intake system, it is difficult to predict the noise precisely, especially for the part whose frequency is higher than 1000 Hz. This paper introduces a new co-simulation method to simulate the aerodynamic noise at the engine inlet. With the coupled simulation between two programs, GT-Power and Fluent, it could simulate the gas flow inside the engine intake system, under the actual running condition of engine.

As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of extracting signal components. Therefore, a method based on spectral energy distribution is proposed to determine the parameter, and the quadratic penalty term is optimized according to SNR. The results show that the optimized VMD can adaptively extract the vibration signal components of the diesel engine. In the actual fault diagnosis case, it is difficult to obtain the data with labels.

The vehicle axle gear whine noise and vibration are key issues for the automotive industry to design a quiet, reliable driveline system. The main source of excitation for this vibration energy comes from hypoid gear transmission error (TE). The vibration transmits through the flexible axle components, then radiates off from the surface of the housing structure. Thus, the design of hypoid gear pair with minimization of TE is one way to control the dynamic behavior of the vehicle axle system. In this paper, an approach to obtain minimum TE and improved dynamic response with optimal tooth profile modification parameters is discussed. A neural network algorithm, named Back Propagation (BP) algorithm, with improved Particle Swarm Optimization (PSO) is used to predict the TE if some tooth profile modification parameters are given to train the model.

In-vehicle noise is composed of a variety of tonal (frequency-related) components and the tonal components play an important role in the improvement of interior vehicle sound quality. Much research has been focused on the suppression of sound pressure level and achieved certain positive effects. However, in some operating conditions, customers still perceive the tonal components and complain about the vehicle quality even the sound pressure level is relatively low. Therefore, a better understanding of how tonal components are perceived is necessary for automotive designers. To do so, psychoacoustics results about human hearing mechanism to tonal components are comprehensively summed in this study: human hearing response to pure tone, two tones and multiple tones. Then, well-controlled testing stimuli were generated and subjective annoyance testing was conducted. The results show agreement with former researchers' findings.

Subband adaptive filtering (SAF) techniques have been increasingly used in active noise control (ANC), especially for acoustic broadband noise signal and system models with long impulse responses. In ANC, the closed-loop delayless SAF schemes improve the convergence rate of the widely adopted conventional filtered-x LMS (FxLMS) algorithm in a more computationally efficient manner under wideband noises like colored signals or even nonstationary signals. In most real-world environment like vehicle interior cabin, however, the performance of ANC can be degraded by various outliers, including non-Gaussian impulsive signal such as transient impact acoustic response for road noise.

The meshing noise of hypoid gear has a significant influence on driving axle system. It should be strictly controlled in order to reduce the whole vehicle noise. Meshing internal excitation of hypoid gear is a main source of vibration noise, closely connected with geometrical shape and meshing status. There is no comprehensive analysis on the impact of various contact patterns on vibration noise in previous studies. Therefore, the method for controlling contact characteristics of hypoid gears is studied in this paper, which includes adjusting the position and length of contact pattern, direction of contact trace and the theoretical transmission error. Also, a non-linear dynamic model with multi-freedom for the hypoid gear pair of the driving axle is established to evaluate the dynamic response of the gear pair. Then an example was carried out to improve the dynamic characteristic of hypoid gears by tooth profile modification.

Gear drives are widely used in the transmission of many types of vehicles and various gear faults were reported to have different effects on the performance of transmission systems. The psychoacoustics metrics, which are used to represent the human hearing property, are objective indicators of product sound quality performance. Therefore, psychoacoustic analysis of gear noise with gear faults needs to be conducted. In this paper, different types of gear faults are summarized, and two of them, including wear and misalignment, are studied separately in the psychoacoustic analysis of the synthesized noise signal of an example gearbox. The gear noise spectra for the cases with different gear faults are synthesized based on the findings of previous publications, where it shows that the two gear faults can either increase the amplitude at the harmonics of the gear mesh frequency or cause the sideband responses.