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Since the beamforming concept was first introduced to acoustic engineers approximately 10 years ago, the acoustic phased array has become one of the most popular techniques used to identify noise sources. Generally speaking, the spatial resolution of the phased array is proportional to the number of microphones and the array size. With more microphones or a larger array, better spatial resolution is achieved. However, to achieve better spatial resolution for a given microphone array structure, the so-called CLEAN method was recently introduced. By applying the traditional non-parametric estimation method, such as delay-and-sum, the CLEAN algorithm first computes the largest noise and removes it from the raw data samples. Then, the program estimates a new peak noise, based on the “cleaned” data samples. The process is repeated until the remaining data samples become random noise. The CLEAN method works well if the noise sources are far apart.

Almost every automotive noise, vibration, and harshness (NVH) engineer who has ever looked at a fast Fourier transform knows the difference between instruments with 90 dB and instruments with 120 dB of dynamic range. NVH engineers understand instrumentation specs such as 24-bit resolution analog-to-digital converters and alias-free signal bandwidth. However, with modern noise and vibration instrumentation systems now being almost completely built on the PC, these specs neglect the most important X factor: the PC itself. No other aspect can affect the performance of an instrumentation system for a sensor array more than the components of the PC. Fortunately, a variety of off-the-shelf PC technologies built on industry standards are available to make it easier and less expensive than ever before to instrument and manage data from large systems. But an NVH engineer must wade through a sea of options to choose the right PC technologies for desired instrumentation system performance.

Modular instrumentation is being widely used in noise and vibration measurement systems that demand higher channel counts and the wider dynamic range that 24-bit delta-sigma ADCs make available at lower costs. This is an overview how flexible modular instrumentation employing the latest software technology can be used in making high precision noise and vibration measurements where higher sampling rates, higher channel counts, increased dynamic range, and distributed architectures were needed in smaller packages. An example where this is being used is in acoustic beam forming in aircraft pass by noise tests to measure and distinguish engine and airframe noise sources.

LIN is a low-cost, low-speed vehicle communication sub-bus becoming increasingly pervasive in automotive subsystems. It is a simple, UART-based master-slave protocol designed as a low-speed supplement to a CAN or FlexRay bus. Its primary application is cabin comfort and human interface hardware such as dashboard controls, power seat harnesses, and power door/window systems. As automotive network designers attempt to reduce wiring complexity and lower system cost, modular, inexpensive sub-buses like LIN become an attractive option. This paper presents an overview of the LIN standard and its applications, and then proposes an architecture for rapid development of LIN networks via hardware simulations of LIN nodes. Using inexpensive, off-the-shelf hardware, LIN sensor and actuator applications can be tested in-place without microcode development, speeding overall network development time.

In many measurement applications, there is a need to correlate data acquired from different systems or synchronize systems together with precise timing. Signal Based and Time Based are the two basic methods of synchronizing instrumentation. In Signal Based synchronization, clocks and triggers are physically connected between systems. Typically this provides the highest precision synchronization. In many NVH applications size and distance constrains physically connecting the systems needed for making measurements though the inter-channel phase information of simultaneously sampled signals is crucial. In Time Based synchronization, system components have a common reference of what time it is. Events, triggers and clocks can be generated based on this time.

Several advanced signal processing algorithms beyond the FFT such as time-frequency analysis, quefrency, cestrum, wavelet analysis, and AR modeling uses are outlined. These advanced algorithms can solve some sound and vibration challenges that FFT-based algorithms cannot solve. Looking at signal characteristics of a unit under test in the time-frequency plane, it is possible to get a better understanding of signal characteristics. This is an overview of these algorithms and some application examples, such as speaker testing, bearing fault detection, dashboard motor testing, and engine knock detection where they can be applied to NVH applications.

Military jet aircraft expose both ground maintenance personnel and the community to high levels of noise. The U.S. Department of Defense is funding research to develop advanced modeling tools for noise reduction techniques and community noise exposure. A large-scale microphone array for portable near-field acoustic holography (NAH) and data acquisition system was created for this purpose. The system was designed for measuring high-amplitude jet noise from current and next-generation military aircraft to provide model refinement and benchmarking, evaluate performance of noise control devices, and predict ground maintenance personnel and community noise exposure. The acoustical instrumentation system was designed to be easy to use with scalable data processing as the primary focus. The data acquisition system allowed up to 152 channels simultaneously sampled at a rate of 96 kHz.

Vehicle communication networks are challenged by increasing demands for bandwidth, safety, and security. New data is coming into the vehicle from personal devices (e.g. mobile phones), infotainment systems, camera-based driver assistance, and wireless communication with other vehicles and infrastructure. Ethernet (IEEE 802.3) provides high levels of bandwidth and security, making it a potential solution to the challenges of vehicle communication networks. However, in order to be used in control applications, Ethernet must provide known timing performance (e.g. bounded latency and jitter), and in some cases redundancy. This paper explores use of Ethernet for in-vehicle control applications.

This is an overview of the development of a portable, real-time acoustic beamformer based on FPGA (Field Programmable Gate Arrays) and digital microphones for noise source identification. Microphone arrays can be a useful tool in identifying noise sources and give designers an image of noise distribution. The beamforming algorithm is a classic and efficient algorithm for signal processing of microphone arrays and is the core of many microphone array systems. High-speed real-time beamforming has not been implemented much in a portable instrument because it requires large computational resources. Utilizing a beamforming algorithm running on a Field Programmable Gate Array (FPGA), this camera is able to detect and locate both stationary and moving noise sources. A high-resolution optical camera located in the middle of the device records images at a rate of 25 frames per second. The use of the FPGA technology and digital microphones provides increased performance, reduced cost and weight.