TAILIEUCHUNG - Sensors in Manufacturing P2

Sensor Fusion With a specific focus for the monitoring in mind, researchers have developed over the years a wide variety of sensors and sensing strategies, each attempting to predict or detect a specific phenomenon during the operation of the process and in the presence of noise and other environmental contaminants. A good number of these sensing techniques applicable to manufacturing have been reviewed in the early part of this chapter. Although able to accomplish the task for a narrow set of conditions, these specific techniques have almost uniformly failed to be reliable enough to work over the range of operating conditions. | Principles of Sensors in Manufacturing I 21 threshold a decision is made that the tool is worn. The success of this strategy depends upon the degree to which the mean value of the sensor output actually represents the state and progress of tool wear. Sensor Fusion With a specific focus for the monitoring in mind researchers have developed over the years a wide variety of sensors and sensing strategies each attempting to predict or detect a specific phenomenon during the operation of the process and in the presence of noise and other environmental contaminants. A good number of these sensing techniques applicable to manufacturing have been reviewed in the early part of this chapter. Although able to accomplish the task for a narrow set of conditions these specific techniques have almost uniformly failed to be reliable enough to work over the range of operating conditions and environments commonly available in manufacturing facilities. Therefore researchers have begun to look at ways to collect the maximum amount of information about the state of a process from a number of different sensors each of which is able to provide an output related to the phenomenon of interest although at varying reliability . The strategy of integrating the information from a variety of sensors with the expectation that this will increase the accuracy and . resolve ambiguities in the knowledge about the environment Chiu et al. 14 is called sensor fusion. Sensor fusion is able to provide data for the decision-making process that has a low uncertainty owing to the inherent randomness or noise in the sensor signals includes significant features covering a broader range of operating conditions and accommodates changes in the operating characteristics of the individual sensors due to calibration drift etc. because of redundancy. In fact perhaps the most advantageous aspect of sensor fusion is the richness of information available to the signal processing feature extraction and .

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