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This case study presents a new automated production noise test for power steering pumps. The test included adaptive noise cancellation, and a neural network implementation. The result mapped the pump acceleration signature into an objective repeatable noise metric. The test algorithm was a distributed DSP architecture designed for real-time measurement and decision processing. It was implemented with no increase in test cycle time. It accomplished the correlation of in-vehicle power steering pump noise to it's vibration characteristics, and retrofitting of accelerometers in place of microphones for acceptance testing.

Grunt is a structure-born noise caused by resonance of the steering gear torsion bar (T-bar) in an HPAS (Hydraulic Power Assist Steering) system. The goal of this work was to develop techniques to quantify and predict grunt in a RV (rotary valve) steering gear system. First, vehicle testing was used to identify an objective metric for grunt: y = dynamic pressure in the return line. Then, a computer simulation was developed to predict y as a function of two known control factors. The simulation results were correlated to measurements on a test vehicle. Finally, the simulation was expanded to include two additional control factors, and grunt predictions were demonstrated on a different test vehicle.