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Practical issues to consider when making measurements for Nearfield Acoustical Holography (NAH) analysis are addressed. These include microphone spacing and placement from the test surface, number of microphones and array size, reference microphone number and placement, and filtering of the data. NAH has become an accepted analysis tool so that several commercial packages are available. Its application is limited to test surfaces that are fairly planar, lending itself well to tire testing, front of dash testing, engine face testing, etc. In order to achieve accurate NAH results, the measurement and analysis process must be clearly understood on a practical level. Understanding the advantages and limitations of NAH and the measurement parameters required of it will allow the user to determine if NAH is applicable to a particular test object and environment.

Test sensors are evaluated for noise path analysis applications. Newly developed ICP™ piezo-electric strain gages are used with accelerometers and microphones in a conventional noise path analysis test on the front body/suspension attachment points of a vehicle. In a less conventional application, a steering knuckle is converted into a 6-DOF force transducer using an array of strain gages and using an array of 3-DOF load cells. The two sensor arrays are both calibrated with a 6-DOF load cell. The result is an estimate of the three translation force and three moment operating inputs entering the steering knuckle from the wheel.

Parameter estimation techniques that can be used to determine modal parameters (frequency, damping, and mode shape) from experimentally measured frequency response or unit impulse response are presented with respect to practical implementation and use. The methods are separated into two categories: 1) A curve fit of only one degree of freedom with or without residuals. 2) A curve fit of multiple degrees of freedom with or without residuals. Particular details are given with respect to multiple degree of freedom techniques utilizing the Complex Exponential algorithm.