Practical NVH Signal Processing Methods

Signal processing has become a critical tool in optimizing vehicle noise. This seminar will help you to understand the foundation common to all NVH data acquisition equipment including digitizing, windows, aliasing, averaging techniques, and common analysis functions such as the power spectrum, transfer function and coherence. Fundamental concepts such as filtering, modulation, convolution, and correlation, as well as specialized techniques used in rotating machinery such as adaptive re-sampling and order tracking, will be covered. The seminar will also cover multi-input multi-output (MIMO) signal processing, array based solutions for force identification, source and path characterization and data visualization. Brief introductions to emerging concepts will also be explored and computer demonstrations, physical experiments and case studies will be used to illustrate applied, real-world problems.

What Will You Learn

By attending this seminar, you will be able to:
  • Explain the fundamental controls typical in modern spectrum analysis tools
  • Interpret NVH data and judge its relevance to physical phenomena
  • Extract new types of useful information from NVH data
  • Implement new signal processing techniques

Is This Course For You

NVH technicians, engineers and managers who want to understand how NVH data is produced and interpreted will find this seminar valuable. The material is presented at a level suitable for beginners, but offers the more experienced practitioners new insight into the concepts presented through the illustrations and demonstrations that are included.

Materials Provided

This data is not available at this time

Course Requirements

This data is not available at this time


  • Properties of the FFT
    • Sampling and digitizing
    • Aliasing and filters
    • Leakage and windows
    • Averaging techniques
    • Autopower, crosspower and coherence
    • Transmissibility and isolation
    • Measuring and interpreting the transfer function
  • Rotating Machinery Basics
    • What is an order?
    • Rotation synchronous data acquisition methods
    • AM and FM modulation effects
    • FIR, IIR and re-sampling filters
    • Up-sampling down-sampling and adaptive re-sampling
  • Time Frequency Methods
    • Short time Fourier transform
    • Gabor expansion and Gabor transform
    • Orthogonality, invertability and the dual function relationship
    • Gabor order tracking
    • Introduction to wavelets
  • Fundamentals of Multi-Input-Multi-Output (MIMO) System Analysis
    • Review of Single-Input-Single-Output (SISO) systems
    • Introduction to Single-Input-Multiple-Output (SIMO) systems
    • Partial correlation concepts
    • Coherent output power
    • Statistical errors in basic estimates
    • Conditioned spectral analysis
  • Forces and Sources in MIMO Systems
    • Least squares solution techniques
    • Force estimation technique Conditioned Source Analysis (CSA)
    • Case history: transfer path analysis
    • Case history: model correlation and updating
  • Introduction to Data Classification and Pattern Recognition
    • Techniques for building and analyzing feature vectors
    • Recognition engines: neural networks and hidden Markov models
    • Applications: machine noise recognition, vision based gear mesh quality