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A unique Matlab-based coded engineering software tool (Time-Frequency Analyzer Core®) was developed that allows users to process acquired time data to help in identifying sources and paths of noise and vibration (in the experience of the authors). The Time-Frequency Analyzer Core (TFAC) software does not replace commercial off the shelf software/hardware NV specific tools such as modal analysis, ODS, acoustic mapping, order tracking, etc., rather it aims at providing basic, yet powerful data inspection and comparison techniques in a single software tool that facilitates drawing conclusions and identifying most effective next steps. The features and advantages of using this software tool will be explained, along with a description of its application to a few different cases (automotive and off highway/agricultural).

Source-path-contribution (SPC) analysis, or transfer-path-analysis, is a test based method to characterize noise and vibration contributions of a complex system. The methodology allows for the user to gain insight into the structural forces and acoustic source strengths that are exciting a system, along with the effects of the structural and acoustic paths between each source and a receiver position. This information can be utilized to understand which sources and/or paths are dominating the noise and vibration performance of a system, allowing for focused target cascading and streamlined troubleshooting efforts. The SPC process is widely used for automotive applications, but is also applicable for a wide range of product types. For each unique application the basic SPC principles remain constant, however best practices can vary for both measurement and analysis depending on the type of system being evaluated.

As the demand for Sound Quality improvements in vehicles continues to grow, robust analysis methods must be established to clearly represent end-user perception. For vehicle sounds which are tonal by nature, such as transmission or axle whine, the common practice of many vehicle manufacturers and suppliers is to subjectively rate the performance of a given part for acceptance on a scale of one to ten. The polar opposite of this is to measure data and use the peak of the fundamental or harmonic orders as an objective assessment. Both of these quantifications are problematic in that the former is purely subjective and the latter does not account for the presence of masking noise which has a profound impact on a driver's assessment of such noises. This paper presents the methodology and results of a study in which tonal noises in the presence of various level of masking noise were presented to a group of jurors in a controlled environment.

As the interior sound levels in cabin compartments of passenger vehicles continue to get quieter, noises from various sources which previously were not objectionable can become an issue. One such source is the “slosh noise” from liquid movement within fuel tanks. Vehicle manufacturers, responding to the phenomena, have turned to their suppliers and worked with them to establish robust test and analysis methods to characterize the NVH performance of their fuel storage and delivery systems. Test facilities have recently made great advancements in the capability to measure and characterize “fuel slosh noise” in tanks. However, the industry today lacks standardized procedures to apply to the issue, including defining test parameters and analysis methods (both of which are complex because of the time-domain nature of slosh events).

Binaural recordings are often used for added realism in subjective listening studies, but are commonly played back in environments that are different than those in which the recordings were taken. An important component of the added realism is the ability of the listener to locate the acoustic sources in a three dimensional space. While humans can generally do a good job of locating acoustic sources through inter-aural time differences (ITD) and inter-aural intensity differences (IID), some well documented ambiguities exist when using these acoustic cues by themselves (i.e. ITD and ILD for a source in front of or behind a listener are identical). To resolve these ambiguities, humans often rely on supplemental information from either direct visual feedback or from their knowledge of and comfort with the listening environment.

With the current focus of the automotive industry on improving fuel consumption, it is becoming increasingly more common to adapt current/existing vehicle platforms for integration with electric powertrains. This integration can have an impact on many areas of the vehicle development process, including noise and vibration performance. Alongside the understood benefits to fuel economy, electric powertrains can present many unique noise and vibration related development challenges which require specific attention, particularly for cases in which a conventional gasoline engine vehicle platform is used as a surrogate for the electric powertrain. In this paper, several of the potential noise and vibration development activities will be highlighted, including discussions on powertrain vibration, accessory noise and vibration, and acoustic package material development to deliver a refined noise and vibration experience to the customer.

The role of NVH test development has changed from addressing a system-level NV concern late in the design cycle (firefighting) to having well established NV optimized test procedures in place. One way this is achieved is by leveraging the information gained during troubleshooting of current product to improve the future product development process for noise and vibration. Today, most NV groups/laboratories use optimized test procedures for creating accurate, consistent, and efficient test results. This still requires expertise to post-process data, compute targets and interpret results to guide product development. This step is often overlooked and, in recent years, due to the lack of NV expertise of “younger” labs (typically in non-automotive industries) or of more established labs affected by the economic downturn (early retirements, lay-offs, especially in the automotive industry) there has been a growing need for automated post-processing “intelligent” procedures.

Vehicle NVH (Noise Vibration & Harshness) is of continued concern to customers in this increasingly competitive market and driveline NVH performance is a key factor in overall vehicle quality. A typical way to increase this quality is the use of axle end of line test stands that utilize NVH signal analysis methods to offer pass/fail criteria. In the manufacturing environment there are high costs associated with axle assemblies that are rejected due to the amount of time for NVH analysis to determine root cause for the failure. Of more interest to both product development and manufacturing activities is the ability to understand the root cause of the failures from the axle end of line test stand. This information can improve the manufacturing process by eliminating errors, streamlining re-build activities, aiding in product design improvements, and in turn reducing cost.

Design parameters for automotive components can be highly affected by the requirements imposed for vehicle pass-by compliance. The key systems affecting pass-by performance generally include the engine, tires, intake system, and exhaust system. The development of these systems is often reliant on the availability of prototype hardware for physical testing on a pass-by course, which can lead to long and potentially costly development cycles. These development cycles can benefit significantly from the ability to utilize analytical data to guide development of component-level design parameters related to pass-by noise. To achieve this goal, test and analysis methods were developed to estimate the vehicle-level pass-by performance from component level data, both from physical and/or analytical sources. The result allows for the estimation of the overall vehicle-level pass-by noise along with the contributions to the total and dominant frequency content from each of the key noise sources.

As an agricultural tractor OEM was moving a new tractor model from development into production, an objectionable cab “boom” was identified that was not present in the preproduction pilot -level tractors. The cab boom was identified as a low frequency tone causing an increase of 7 (dBA) over the level in the pilot tractors, which was deemed unacceptable. The process used by the tractor OEM engineering team to address this problem has been widely used and refined in the automotive industry, but it is relatively new in the agricultural/off-road vehicle industry. This paper describes the source-path-receiver approach that led to identifying the exhaust tip as the source and the vibro-acoustic coupling of a windshield structural mode with an acoustic cab cavity mode as the path of the boom event.

For the development of a self-propelled crop spraying machine, a hybrid experimental and analytical Source-Path-Contribution (SPC) approach is utilized by a leading agricultural equipment manufacturer. The objective is to predict noise and sound quality in the cab before prototypes are assembled, so that dB(A) and SQ targets can be assessed early on and better specifications sent to suppliers to achieve these vehicle-level targets. The experimental SPC task is conducted on the current crop sprayer model, which has the same cab but different engine, transmission and hydraulics than the new model. A hybrid FE-SEA model of the current cab is developed and run at load cases derived from test data. The SEA approach is needed to evaluate the effect of cab acoustic treatments, which are not accounted for in the SPC experimental model. Contributions to in-cab noise for the current sprayer are estimated from both experimental and analytical SPC.

For vehicle Original Equipment Manufactures (OEMs), road noise inside the vehicle is an important aspect that contributes to the comfort and the sound quality image of the vehicle. Road noise inside a vehicle is a function of the source (tire design interacting with road surface) and of vehicle sensitivity functions. Road noise targets and tire targets are typically developed by characterizing the tire as a noise and/or vibration source and by characterizing the vehicle as a matrix of acoustic or structural paths(1). This paper focuses on the development of a simplified procedure for measurement of Noise Reduction (or acoustic vehicle sensitivity function) from tire patch to vehicle interior. Several procedures are available from either literature, vehicle manufacturers or software providers, which exhibit important differences regarding sound production, number and position of source and receiver microphones, or measured parameters (2).