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This paper discusses the optimization of an automotive hydraulic steering pump pulley design for improved in-vehicle pump NVH performance. Levels of steering pump whine noise heard inside a vehicle were deemed objectionable. Vehicle and component transfer path analyses indicated that the dominant noise path for the whine noise was airborne in nature. Subsequent experimental modal analysis indicated that the steering pump pulley was a major contributor to the amount of radiated noise produced by the pump/pulley system. CAE analysis was used to further analyze the dynamic behavior of the pulley and develop an optimized design with decreased noise radiation efficiency. The results predicted with the CAE analysis were verified in-vehicle, resulting in a vehicle with acceptable steering pump whine noise performance.

The present work discusses an objective test and analysis method developed to quickly quantify steering gear rattle noise heard in a vehicle. Utilizing envelope analysis on the time history data of the rattle signal, the resulting method is simple, fast, practical and yields a single-valued metric which correlates well to subjective measures of rattle noise. In contrast to many other rattle analysis methods, the approach discussed here is completed in the time domain. As applied to rattle noise produced by automotive electric steering systems, the metric produced with this analysis method correlates well with subjective appraisals of vehicle-level rattle noise performance. Lastly, this method can also be extended to rattle measurements at the component and subcomponent level.

This work discusses the development of SAE procedure J2747, “Hydraulic Pump Airborne Noise Bench Test”. This is a test procedure describing a standard method for measuring radiated sound power levels from hydraulic pumps of the type typically used in automotive power steering systems, though it can be extended for use with other types of pumps. This standard was developed by a committee of industry representatives from OEM's, suppliers and NVH testing firms familiar with NVH measurement requirements for automotive hydraulic pumps. Details of the test standard are discussed. The hardware configuration of the test bench and the configuration of the test article are described. Test conditions, data acquisition and post-processing specifics are also included. Contextual information regarding the reasoning and priorities applied by the development committee is provided to further explain the strengths, limitations and intended usage of the test procedure.

This work demonstrates a practical method for predicting vehicle-level automotive steering system NVH performance from component-level NVH measurements of hydraulic steering pumps. For this method, in-vehicle measurements were completed to quantify vehicle noise path characteristics, including steering system structure borne, fluid borne and airborne paths. At the component level, measurements of steering pump reaction forces, sound power and dynamic hydraulic pressure were also completed. The vehicle-level measurement data was used to construct NVH transfer functions for the vehicle. These transfer functions were in turn combined with the pump component data measured on a test stand to create a prediction for steering pump order vehicle interior noise. The accuracy of these predicted values was assessed through comparison with actual vehicle interior noise measurements.

The present work discusses the application of multivariate statistical methods for the analysis of NVH data. Unlike conventional statistical methods which generally consider single-value, or univariate data, multivariate methods enable the user to examine multiple response variables and their interactions simultaneously. This characteristic is particularly useful in the examination of NVH data, where multiple measurements are typically used to assess NVH performance. In this work, Principal Components Analysis (PCA) was used to examine the NVH data from a benchmarking study of hydraulic steering pumps. A total of twelve NVH measurements for each of 99 pump samples were taken. These measurements included steering pump orders and overall levels for vibration and sound pressure level at two microphone locations. Application of the PCA method made it possible to examine the entire set of data at once.

The present work demonstrates the application of Design of Experiments (DOE) statistical methods to the design and optimization of a hydraulic steering pump for NVH performance. DOE methods were applied to RPM-domain data to examine the effect of several different factors, as well as the interactions between these factors, on pump NVH. Whereas most DOE analyses typically consider only a single response variable, the present work considered multiple response variables. Specifically, pump NVH performance curves for several pump rotational orders over a range of shaft speeds were analyzed. Thus, it was possible to determine the effect of the factors in question over the entire speed range of pump operation, rather than a single speed or setting. Statistical methods were applied to determine which factors and interactions had a significant effect on pump NVH. These factors were used to construct an empirical mathematical prediction model for NVH performance.

The objective of the present work was to improve automotive hydraulic steering system Noise, Vibration and Harshness (NVH) performance through an optimization of the hydraulic flow path through the steering pump. Computational Fluid Dynamics (CFD) was used to model the hydraulic steering pump pressure cavity flow. CFD simulation revealed that several pump housing features were creating obstructions in the flow of hydraulic fluid through the pump, which in turn generated a high degree of turbulence in the flow. An optimal steering pump housing design minimizing turbulent flow within the pump was selected using CFD analysis. An improvement in steering system NVH performance with this housing design was verified through experimentation.

The objective of the present work was to establish a correlation between steering pump cam ring profile location and steering system performance for noise, vibration and harshness (NVH). Once this correlation was established, the secondary objective was to determine acceptable cam profile position tolerances from the standpoint of NVH performance. These objectives were accomplished through the use of binaural measurement and jury evaluation of vehicle interior noise. Cam rings were manufactured for this study with profiles shifted a predetermined distance away from the nominal position. These cams were built into steering pumps and these pumps were in turn installed in a vehicle. Vehicle interior noise and pump housing vibration measurements were made to quantify the steering system noise performance associated with each cam ring. The interior noise recordings were played back for a jury comprised of engineers familiar with steering system noise.

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.