Noise and vibration of automotive vehicles are an increasingly important issue in the automobile industry, for implications on both environmental noise pollution and comfort perceived by driver and passengers. Since noise and vibration performances affect the overall image of a vehicle, they are now considered important factors in the entire vehicle design process. In this regard, an accurate experimental evaluation of vehicle noise and vibration levels in both stationary conditions and urban driving conditions are often necessary for undertaking a refinement process of vehicle sound quality, satisfying the development targets. This paper provides a review of the main experimental techniques adopted for the measurement and analysis of noise and vibration in a commercial vehicle. In order to monitor sound generation in terms of both air-borne and structure-borne noise, the source, transfer path and receiver have to be investigated by noise measurement. For this purpose, different techniques can be employed. Some of them make use of instrumentation (i.e. condenser microphone, sound level meter, sound intensity probe, acoustic holography) which requires particular measurement environments, as it ensures sufficient measuring accuracy only in free field or anechoic conditions. On the contrary, the more innovative pressure-velocity sound intensity method can be used for in-situ tests, without the need to create acoustically treated environments. At the same time, vibration measurements by using proper transducers allow to minimize the structure-borne vibration transmitted from vehicle power plant to chassis and body structure. Experimental modal analysis is another standard tool in vehicle NVH development for determining the dynamic characteristics of a system, and therefore for reducing the risk of failure or excessively high structural vibration or sound pressure levels.Hence, in present work, noise and vibration frequency spectra of experimentally analyzed case studies are given, to illustrate how accurate experimental tests are fundamental for identifying the generation causes of typical noise and vibration problems

Anomaly detection (AD) algorithms can be instrumental in industrial scenarios to enhance the detection of potentially serious problems at a very early stage. Of course, the “Industry 4.0” revolution is fostering the implementation of intelligent data-driven decisions in industry based on increasingly efficient machine learning (ML) algorithms. Most well-known AD methods use a supervised learning approach focusing on fault classification. They assume the availability of labeled data for both normal and anomalous classes. However, in many industrial environments, a labeled set of anomalous data instances is more challenging to obtain than a labeled set of normal data. Hence, this work implements an unsupervised approach based on two different methods using a typical benchmark bearing-fault dataset. The first method relies on the manual extraction of typical vibration metrics provided as input to an ML algorithm. The second one is based on a deep learning (DL) approach, automatically learning latent representation from raw data. The performance metrics demonstrate that both approaches can distinguish the state of a bearing from normal to faulty. DL methodology proves a higher accuracy rate in recognizing faults and a better ability to provide information about the fault size.

The easiest way to identify knock conditions during the operation of a SI engine is represented by the knowledge of the in-cylinder pressure. Traditional techniques like MAPO (Maximum Amplitude Pressure Oscillation) based method rely on the frequency domain processing of the pressure data. This technique may present uncertainties due to the correct specification of some model parameters, like the band-pass frequency range and the crank angle window of interest. In this paper two innovative techniques for knock detection, which make use of the in-cylinder pressure, are explained in detail, and the results are compared with those coming from the MAPO method. The first procedure is based on the use of statistical analysis by applying an Auto Regressive (AR) technique, while the second technique makes use of the Discrete Wavelet Transform (DWT). The data useful for the analysis have been acquired on a high compression ratio four cylinder, spark ignition engine. Results demonstrate that the analyzed methods give quite similar outcomes but they also highlight that AR and DWT techniques present an higher sensitivity for soft knock detection. Moreover, both AR model and DWT based method may be applied to the engine block vibrational signals, acquired by low-cost accelerometers. Therefore, they can be implemented within modern on-board control units, in order to execute a real time knock detection allowing to realize more sophisticated spark advance control strategies.

In this work an extensively experimental analysis aimed to verify the sound insulation properties of the engine bay of a commercial passenger car is carried out, evaluating the possibility to adopt different sound absorbing materials, to be applied under engine cover nylon skin, in the place of commonly used polyurethane foams. Experimental tests were performed on the vehicle at different stationary operating conditions, employing typical pressure microphones for far field measurements, according to the related prescribed standards. A limited number of materials has been initially selected through a preliminary analysis, and then employed for creating different engine cover configurations, which were subsequently tested in real engine operating conditions. For a good understanding of the obtained results, an experimental investigation through an innovative in situ impedance method aimed to assess acoustic properties of each considered material has been also performed. Among all the tested materials, only one able to ensure better acoustic performance at mid and high frequencies with respect to the already existing cover configuration, has been finally identified, after considering other selection criteria such as an adequate high temperature resistance and the most cost-effective solution. Future analyses will regard investigations on the use of additional materials, for solving problem in attenuating engine noise also at low frequencies

In the present work, an Auto Regressive Moving Average (ARMA) model and a Discrete Wavelet Transform (DWT) are applied on vibrational signals, acquired by an accelerometer placed on the cylinder block of a Spark Ignition (SI) engine, for knock detection purposes. To the aim of tuning such procedures, the same analysis has been carried out by using the traditional MAPO (Maximum Amplitude of Pressure Oscillations) index and an Inverse Kinetic Model (IKM), both applied on the in-cylinder pressure signals. Vibrational and in-cylinder pressure signals have been collected on a four cylinder, four stroke engine, for different engine speeds, load conditions and spark advances. The results of the two vibrational based methods are compared and in depth discussed to the aim of highlighting the pros and cons of each methodology. The presented outcomes show the capability of the vibration based detection algorithms in accurately monitor the presence of knock phenomena, and to determine its intensity according to the IKM and MAPO based methods. The comparison with a standard processing of the knock sensor output reveals the higher sensitivity of the proposed methodologies. Therefore, the possibility of implementation in modern on-board control units is foreseen, as well.