Impact detection

The history of airbag development began with mechanical ball-in-tube or roll sensors that made an electrical contact when sufficient energy over a sufficient time was encountered. Because these sensors were essentially digital on-off devices, more complex detection algorithms were difficult to implement and they were not fast enough to be used for the evolving side-impact detection systems. The development of small silicon micromachined accelerometers for less than 5... 

In developing these new algorithms, the requirements for sensors added more of them outside of the central control module for two reasons. First, some types of crash were difficult to detect within the short time frame required for deployment (such as offset pole crashes) and the solution was to provide a high-g precrash sensor within the front of the vehicle. And second, the need for side-impact detection required deployment decisions even faster that the frontal crashes (within approximately 5 ms). Both of these requirements moved accelerometers to satellite positions closer to the perimeter of the vehicle where they are more likely to pick up impacts from small objects. 

The second was the impact of rocks near sensors that may be placed on the floor structure for side-impact detection. In these locations rocks or other debris hitting the bottom of the vehicle can cause high-g shocks greater than 300 g at frequencies above the 400 Hz bandwidth of the accelerometer. The last category involves various small objects such as birds, rocks, tree limbs, balls, or other road debris that can impact the side of the vehicle and thus affect sensors mounted in the B pillar or within the skin of the doors. These can also create high-g forces with high-frequency content. 

Figure 10 Visualization of low-energy impacts detected by eddy currents on a CFRP panel.

Markmiller JFC, Chang FK. Sensor network optimization for a passive sensing impact detection technique. Struct Health Monit Int J 2010 9(1) 25—39. http //dx.doi.org/... 

For each risk event, a Risk Priority Number (RPN) is computed which is, the product of the numerical scores assigned to the four risk factors, occurrence, impact, detection and recovery. 

In order to calculate the RPN for each risk event, the experts assign a rating of 1-10 (1 = Low and 10 = High) to quantify each risk factor s importance. For example, a major fire at a tier-1 critical supplier may get ratings of 4,9,2, and 8 for occurrence, impact, detection and recovery respectively. Then the RPN is calculated as follows ... 

The risk quantification models discussed in this chapter will take a broader view of supply chain risk and model it as a function of occurrence, impact, detectability, and recovery. Methods to quantify each risk component will be developed. We will begin with the development of a basic risk quantification model as a function of impact and occurrence. Separate mathematical models will then be developed for risk detectability and risk recovery time. All the models will be integrated and illustrated with a case study on risk adjusted multi-criteria supplier selection model at the end of the chapter. 

Due to of the mass-production of single-mode fibers, they are cheaper than other t3rpes and therefore often preferred for sensor purposes. For special purposes, such as pressure or current measurements, and sometimes for impact detection, high-birefringent (Hi-Bi) polarization-maintaining (PM) fibers are used because the polarization state of the output signal is definitely affected by external perturbations. 

Passive piezoelectric sensors can also be applied to detect cracking and crack growth via acoustic emission or to identify damage proceedings via impact detection. Active piezoelectric sensors in SHM structures are suited for the detection of remote damage via pulse-echo, pulse-transmission and phased-array methods or for the identification of damages nearby the sensors via high-frequency electromechanical impedance methods. 

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