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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.

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.

A powered seat adjuster is a complex mass-produced assembly that is heavily optimized for low cost and light weight. The consequence is an inevitable degree of uncontrolled variation in components, subassemblies, and final product. Automakers are driving an exceptional focus on quality and the showroom experience of the car buyer is paramount. Therefore, any seat adjuster with the potential to not satisfy the customer's expectation is likely to be screened on the production line. This paper describes NVH metric design in the context of automated production line detection of seat adjuster defects. A key requirement of the production environment is that the metrics offer intuitive explanations of possible defects and are based on industry-standard formulations. The metric set is a hybrid of objective and subjective parameters with a focus on ensuring a robust sorting process that maximizes detection while minimizing the possibility of failing acceptable product.

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.