A knowledge-based system approach for sensor fault modeling, detection and mitigation

• Published in: Expert systems with applications 2012-09, Vol.39 (12), p.10977-10989
• Main Authors: Silva, Jonny Carlos da, Saxena, Abhinav, Balaban, Edward, Goebel, Kai
• Format: Article
• Language: English
• Subjects: Bias; Detection; Drift; Expert system; Fault detection; Faults; Knowledge base; Mathematical models; Neural Network; Neural networks; Sensor failure; Sensors
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.03.026

► We developed a KB system for sensor fault modeling, detection and mitigation. ► The system performance had similar results compared to a previous NN system. ► The KB system broadened the previous scope, in terms of sensor/fault combinations. ► It demonstrated robustness of fault detection in the presence of sensor dropouts. ► It was able to calculate fault parameters and correct sensor fault signals. Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reliable than the system they are observing. This is in part due to the sensors’ operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electro-mechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.