A New Algorithm for Fault Tolerance in Redundant Sensor Systems Based on Real-Time Variance Estimation

• Published in: IEEE sensors journal Vol. 22; no. 15; pp. 15410 - 15418
• Main Authors: Rivera Velazquez, Josue Manuel, Latorre, Laurent, Mailly, Frederick, Nouet, Pascal
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Accelerometers; Adaptive algorithms; Algorithms; data fusion; Engineering Sciences; Errors; Estimation; fault detection; Fault tolerance; homogeneous systems; Kalman filter; Micro and nanotechnologies; Microelectronics; MINQUE; Real time operation; Real-time systems; Redundancy; sensor array system; Sensor arrays; Sensor systems; Sensors; Variance estimation; weighted average
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2022.3186636

Low cost and small size integrated sensors bring a significant interest to redundant sensing systems such as sensor array systems. Redundancy may permit to increase both performance and dependability of individual sensors at the system level, however, the emergence of these redundant systems has generated the need for dynamic algorithms able to detect and act against the presence of errors in system elements. Then, a new adaptive algorithm for sensor array systems is presented in this work. This approach is generic and can be implemented for different types of sensor systems, besides it does not require any information about the inputs. The proposed real-time algorithm is based on the following: 1) estimation of sensor individual variances at each time step, 2) identification and removal of sensors affected by errors, and 3) reincorporation of sensors after recovery or replacement. Variance estimation is revisiting MInimum Norm Quadratic Unbiased Estimation (MINQUE) method to allow real-time operation and requires that the number of sensors is strictly greater than two times the number of signals to be measured. Consequently, the proposed method is able to support the presence of m-1 faulty devices, where m is the total number of sensors less two times the number of measured signals. Performance of the proposed algorithm is demonstrated through theoretical demonstrations, simulations, and experimental results.