Any time probabilistic sensor validation

Many applications of computing, such as those in medicine and the control of manufacturing and power plants, utilize sensors to obtain information. Unfortunately, sensors are prone to failures. Even with the most sophisticated instruments and control systems, a decision based on faulty data could lead to disaster. This thesis develops a new approach to sensor validation. The thesis proposes a layered approach to the use of sensor information where the lowest layer validates sensors and provides information to the higher layers that model the process. The approach begins with a Bayesian network that defines the dependencies between the sensors in the process. Probabilistic propagation is used to estimate the value of a sensor based on its related sensors. If this estimated value differs from the actual value, then a potential fault is detected. The fault is only potential since it may be that the estimated value was based on a faulty reading. This process can be repeated for all the sensors resulting in a set of potentially faulty sensors. The real faults are isolated from the apparent ones by using a lemma whose proof is based on the properties of a Markov blanket. In order to perform in a real time environment, an any time version of the algorithm has been developed. That is, the quality of the answer returned by the algorithm improves continuously with time. The approach is compared and contrasted with other methods of sensor validation and an empirical evaluation of the sensor validation algorithm is carried out. The empirical evaluation presents the results obtained when the algorithm is applied to the validation of temperature sensors in a gas turbine of a power plant

Sensor Systems for Prognostics and Health Management

Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented