Improving the analysis of operating data on rotating automotive components

Order tracking is a very common method to analyze the response characteristics of rotating machinery to their rotating inputs. Unfortunately, many of the order tracking algorithms that are commercially available are considered proprietary by their developers. In an effort to produce a mostly complete reference and understanding of the characteristics of these methods many of them are documented in this dissertation. Two new order tracking methods are developed and documented which have the ability to separate both close and crossing orders. The Time Variant Discrete Fourier Transform (TVDFT) is developed and shown to be a very powerful and versatile order tracking method as well as a very computationally efficient algorithm. With the ever increasing speed of computers, post-processing of time domain data is becoming popular. A post-processing application which is very computationally demanding in its current commercial implementations is adaptive resampling, commonly used to resample data from the time to the angle domain. A new adaptive resampling method is developed that is based on an upsampled interpolation filter that is very computationally efficient. It should be noted that all order tracking and adaptive resampling methods rely very heavily on an accurate tachometer signal. For this reason, the processing of tachometer signals is included in this dissertation. Finally, a new set of analysis tools formulated around the singular value decomposition (SVD) and the Complex Mode Indicator Function (CMIF) algorithms are developed to compute virtual measurements from order tracks. These tools also provide the ability to estimate linearly independent operating shapes from a set of operating shapes based on order tracks. A virtual measurement called a Mode Enhanced Order Track (MEOT) is developed which should prove useful in estimating natural frequencies and damping from order track measurements. All new methods and several of the traditional methods are evaluated using both analytical and experimental datasets. The experimental datasets include the analysis of data acquired on an automobile operating on a chassis dynamometer, including the separation of the inputs from the left and right wheels. The final result of this dissertation is a complete reference and suite of tools for analyzing many rotating equipment noise or vibration problems.