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The paper describes a new strategy for real-time sensor diagnostics that is based on the statistical correlation of various sensor signal pairs. During normal fault-free operation there is a certain correlation between the sensor signals which is lost in the event of a fault. The proposed algorithm quantifies the correlation between sensor signal pairs using real-time scalar metrics based on the Mahalanobis-distance concept. During normal operation all metrics follow a similar pattern, however in the event of a fault; metrics involving the faulty sensor would increase in proportion to the magnitude of the fault. Thus, by monitoring this pattern and using a suitable fault-signature table it is possible to isolate the faulty sensor in real-time. Preliminary simulation results suggest that the strategy can mitigate the false-alarms experienced by most model-based diagnostic algorithms due to an intrinsic ability to distinguish nonlinear vehicle behavior from actual sensor faults.

Computer modeling and simulation have become one of the primary methods for development and design of automobile occupant protection systems (OPS). To ensure the accuracy and reliability of a math-based OPS design, the correlation quality assessment of mathematical models is essential for program success. In a typical industrial approach, correlation quality is assessed by comparing chart characteristics and scored based on an engineer's modeling experience and judgment. However, due to the complexity of the OPS models and their responses, a systematic approach is needed for accuracy and consistency. In this paper, a correlation grading methodology for the OPS models is presented. The grading system evaluates a wide spectrum of a computer model's performances, including kinematics, dynamic responses, and dummy injury measurements. Statistical analysis is utilized to compare the time histories of the tested and simulated dynamic responses.

A seat belt usability study was conducted to investigate factors associated with seat belt comfort and convenience related to shoulder belt contact pressure, shoulder belt fit, and seat belt upper anchorage location. Two major objectives were addressed in this study: (1) Determine the shift in the contact pressure while changing the seat back angle and seat belt attachment points / B-pillar location by utilizing a body pressure measurement system; (2) Identify how seat belt contact pressure and fit affect users' subjective feeling of comfort. Results from the statistical analysis shows that the seat belt contact pressure increases when the D-ring moves away from the driver in the fore-aft direction (X-axis) whereas height adjustment of the D-ring (Z-axis) is not statistically significant in terms of pressure distribution.

Brake performance can be divided into two distinct classes: base brake performance and controlled brake performance. A base brake event can be described as a normal or typical stop in which the driver maintains the vehicle in its intended direction at a controlled deceleration level that does not closely approach wheel lock. All other braking events where additional intervention may be necessary, such as wheel brake pressure control to prevent lock-up, application of a wheel brake to transfer torque across an open differential, or application of an induced torque to one or two selected wheels to correct an under- or oversteering condition, may be classified as controlled brake performance. Statistics from the field indicate the majority of braking events stem from base brake applications and as such can be classified as the single most important function.