The acquisition of test data is required throughout the product's life cycle - in prototype performance evaluation, reliability/durability testing, duty-cycle analysis, end of line testing, and service and aftermarket product areas. Both lab and on-road testing is needed for components, sub-systems and entire vehicles.

As in-vehicle networks become increasingly more sophisticated in terms of the number of controllers, the speed at which they communicate, and the number of parameters available, they are a virtual goldmine for the test engineer. If the data is already available on the vehicle network, the engineer may only need to add any missing sensors (or possibly none at all).

After reviewing the traditional approach of acquiring data directly from sensors, the course will focus on the newer approach of obtaining data from the in-vehicle network for both automotive and heavy duty vehicles. Attention is given to the complications of taking data from the in-vehicle network and how to overcome them, current trends and applications, wireless data acquisition (Wi-Fi and cellular), GPS, relevant technical standards, and how to simultaneously acquire network data with direct sensor measurements. Both PC-based and logger (flight recorder) data acquisition will also be covered. In addition, a practical guide for analysis and presentation techniques will be covered along with examples.

• Explain what it takes to acquire data from both in-vehicle networks and sensors
• Compare and contrast acquiring data from both in-vehicle networks and directly from sensors
• Understand OBD-II, the CAN protocol and othe common vehicle protocols
• Identify an unknown OBD-II network protocol
• Define the four database types that relate in-vehicle network messages to scaled engineering parameters
• Review vehicle messages and convert the messages to scaled engineering parameters using various automotive and heavy duty protocols and message types
• Demonstrate how sample rate has a major effect on your data beyond the obvious and show how to select the optimum sample rate
• List the four domains that are available to present your data to enhance understanding of the data
• Avoid common pitfalls of acquiring and analyzing good data
• Choose the best analysis techniques to better understand and present test data
• Compare benefits of acquiring data with a PC vs. a stand-alone data logger (without a PC in the vehicle)

• Acquiring Data Directly from Sensors
  • Sensor Inputs: Sensor overview, single-ended vs. differential inputs, proper ranging of the channels, zeroing offsets and signal conditioning.
  • Data Acquisition: Analog-to-digital converters (A/D), time and amplitude resolution, pre- and post-triggering, time synchronous averaging, sample rate, aliasing, frame length and number of frames in a data file.
• Frequency Domain
  • Analyzing data in the frequency domain with the Fast Fourier Transform (FFT) is a valuable tool to optimize sample rate, which affects many factors such as data quality (aliasing), time and frequency resolution, digital filtering, integration and differentiation. Analyzing and displaying the data in the revolution (angular) domain and order domain offer valuable insights into the data.
• In-Vehicle Data Acquisition
  • Comparison of in-vehicle data acquisition with sensor data acquisition.
  • Explanation of OBD-II and what it can and cannot do for you
  • Examination of files containing hex messages. Learn the steps required to convert to useful engineering parameters (e.g. engine RPM, wheel speed, ambient temperature). Message files will be shown from both heavy duty and automotive vehicles

DAY TWO:

• In-Vehicle Data Acquisition - continued
  • Step-by-step procedure to acquire parametric data for both a PC and stand-alone loggers.
  • Explanation of why the database relating parameters and messages is the key and how to get this database information.
  • How to calculate fuel economy and fuel consumption from the in-vehicle network for automotive and heavy duty vehicles
  • Review of applicable standards and references.
  • How to identify unknown automotive protocols and learn about the various network protocols
  • Review of wireless data acquisition options, the advantages and disadvantages of them and the practical throughput rate for real-time data acquisition.
• Data Analysis Techniques
  • How to select the best numerical techniques and how to optimize their performance for digital filtering (including IIR and FIR filters), integration, differentiation, and correlation.
  • How combining logic, statistics and Z transform provides a powerful technique to find key points along a waveform. Make decisions such as pass/fail or perform intelligent monitoring to store only the data of interest optimizing storage space and analysis time.
  • How to time correlate data taken from different sources, such as from the in-vehicle network and directly from sensors.