Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment
- PDF / 996,264 Bytes
- 17 Pages / 600 x 792 pts Page_size
- 56 Downloads / 168 Views
Neural-Network-Based Smart Sensor Framework Operating in a Harsh Environment Jagdish C. Patra Division of Computer Communications, School of Computer Engineering, Nanyang Technological University, Singapore 639798 Email: [email protected]
Ee Luang Ang Division of Computer Communications, School of Computer Engineering, Nanyang Technological University, Singapore 639798 Email: [email protected]
Narendra S. Chaudhari Division of Information Systems, School of Computer Engineering, Nanyang Technological University, Singapore 639798 Email: [email protected]
Amitabha Das Division of Computer Communications, School of Computer Engineering, Nanyang Technological University, Singapore 639798 Email: [email protected] Received 11 February 2004; Revised 5 July 2004; Recommended for Publication by John Sorensen We present an artificial neural-network- (NN-) based smart interface framework for sensors operating in harsh environments. The NN-based sensor can automatically compensate for the nonlinear response characteristics and its nonlinear dependency on the environmental parameters, with high accuracy. To show the potential of the proposed NN-based framework, we provide results of a smart capacitive pressure sensor (CPS) operating in a wide temperature range of 0 to 250◦ C. Through simulated experiments, we have shown that the NN-based CPS model is capable of providing pressure readout with a maximum full-scale (FS) error of only ±1.0% over this temperature range. A novel scheme for estimating the ambient temperature from the sensor characteristics itself is proposed. For this purpose, a second NN is utilized to estimate the ambient temperature accurately from the knowledge of the offset capacitance of the CPS. A microcontroller-unit- (MCU-) based implementation scheme is also provided. Keywords and phrases: intelligent sensors, artificial neural networks, autocompensation.
1.
INTRODUCTION
In many practical application areas of avionics, automobiles, robotics, missile guidance, oil drilling, and industrial measurements, sensors operate in harsh environments such as extreme ambient temperature, pressure, humidity, and so forth. In such situations, the response of the sensors depends not only on the measurand but also on the environmental parameters in a nonlinear manner. Usually, an exact mathematical model of a sensor showing the relationship between the measurand and its response, and its dependency on the environmental parameters, is not available. Further, since most of the sensors exhibit some amount of nonlinear response characteristics, and the environmental parameters influence the sensor behavior nonlinearly, the problem of obtaining an accurate readout and its calibration becomes more complex.
Some of the ideal properties of a sensor include linear response characteristics, autocorrection of the adverse effects of nonlinear environmental parameters, high sensitivity and accuracy, and low power consumption. However, in practical situations, it is not easy to achieve ideal sensor characteristics, especially wh
Data Loading...
